During the Double 11 Shopping Festival, while consumers enjoy shopping, they also face potential fraud risks. In live-streaming rooms

During the Double 11 Shopping Festival, while consumers enjoy shopping, they also face potential fraud risks. In live-streaming rooms, screens are often flooded with positive reviews and likes that can easily attract buyers. However, when consumers place orders based on high sales and five-star reviews, they often find that the actual products fall far short of expectations. Behind this phenomenon are often various "Internet Water Army" groups that create fake orders and control reviews to fabricate false sales data and positive reviews for merchants.
![2024102401.png](https://cdn.aisecurius.com/images/e09/e0996a44-4ba2-4663-bdfa-b018bc04d6ce.png)
Recently, the Neijiang Police in Sichuan Province uncovered a major case involving a "network water army." Over the past two years, this gang provided fake order and review control services to over 1,000 merchants on multiple online platforms, with a total involvement of 100 million yuan. This group not only boosted “popularity” in live-stream rooms but also participated in fake interactive comments and used “empty packages” to create fake orders.

This behavior not only disrupts normal market order but also severely affects consumers' shopping experience.


# Both an Internet Water Army and an E-Commerce Fake Order Scheme


The police investigation revealed that this “network water army” gang targeted live streaming and offered three types of services: Rank Boosting, Interaction, and Marketplace Exposure.
![2024102402.png](https://cdn.aisecurius.com/images/7e2/7e27efaf-1cf4-42ec-89cb-18e7b1ffc8f0.png)

- **Rank Boosting**: The water army increases the number of viewers in a live-streaming room by appearing as named viewers, directly affecting the room's popularity without interacting. The primary purpose is to boost superficial popularity.
  
- **Interaction**: Besides rank boosting, the water army also participates in interactions within live streams, including commenting and answering viewer questions. Merchants provide discussion topics, and sometimes the water army improvises to create a realistic interactive atmosphere. This service charges 4 yuan per person.

- **Marketplace Exposure**: The water army enters live-stream rooms via the marketplace page, further increasing the product's exposure rate. This service charges 5 yuan per person, helping merchants attract more potential customers.

Apart from acting as an “internet water army,” this gang also engages in advanced fraud schemes involving fake orders. Live-stream merchants use the water army to buy specified products to boost sales data, then ask them to report the order numbers. By mailing “empty packages,” they create fake orders. After receiving the empty package, the water army gives five-star reviews and false comments as instructed, earning commissions based on the product price.

![2024102403.png](https://cdn.aisecurius.com/images/cf3/cf30a3b3-8f79-4482-9fbe-c736c55df702.png)

- **Fake Ordering**: Merchants instruct the water army to purchase specific products to inflate sales figures. The water army then provides the order numbers back to the merchant.

- **Sending Empty Packages**: Merchants send empty packages to the water army, who does not receive actual products but instead follows instructions to leave five-star reviews and comments. This effectively creates fake orders and builds a false impression of high sales.

- **Commission Settlement**: The water army earns commissions based on product prices, incentivizing continued participation in fake order activities. Commission payments are typically tied to the purchased product price.

New members joining this gang are required to pay a membership fee ranging from 199 yuan to 2,999 yuan. Depending on the fee amount, members receive titles such as Founder, Director, or Trainee and are assigned different roles like sales, shills, or customer support. Since March 2022, the gang has manipulated reviews and fake orders for more than 1,000 merchants across multiple platforms. By recruiting new members and charging referral fees, they have illegally profited from their schemes, with a total amount involved reaching 100 million yuan. The main suspects are currently under criminal detention in accordance with the law.


# Technical Measures to Prevent Water Army and Fake Orders


This criminal group reached a scale of over 100,000 people, with each member account operated by a real person and different IP addresses, making it difficult for regulatory authorities to identify "network water armies." Analysis reveals that water army accounts may exhibit high activity shortly after registration or at specific times, while showing little to no activity at other times. Additionally, water army accounts often have lower user levels and credit scores, which differ significantly from those of real users in terms of level and permissions.

To effectively curb these illegal activities, platforms need to leverage technical means to identify and prevent suspicious behaviors, improving their ability to detect water army accounts and suspicious activities.

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**1. Identifying Abnormal IP Addresses and GPS Locations**

By monitoring IP addresses and GPS information, platforms can detect unusual login behavior. For example, if an account is accessed from multiple geographic locations in a short time, or if the IP address does not match the registered information, the system can flag the account as suspicious. Additionally, IP address whitelist and blacklist management can effectively filter known sources of water army activity, reducing potential risks.

**2. Detecting Client Device Fingerprinting**

Detecting the legitimacy of client device fingerprinting is another important method to prevent water armies. By collecting data on device operating system versions, hardware information, user behavior, and CPU instructions, the platform can identify risks like emulators, hijacking, or injection. Emulators typically cannot fully replicate carrier information and other system characteristics, making it easier to detect cheating.

Dingxiang Device Fingerprinting records and compares fingerprints to distinguish legitimate users from potential fraudsters. It assigns a unique identifier to each device, detecting maliciously manipulated devices, such as virtual machines, proxy servers, and emulators. It analyzes devices for multiple account logins, frequent IP address changes, and modifications to device properties, helping track and identify fraudulent activities.

**3. Monitoring Abnormal User Behavior and Patterns**

Once a platform has accumulated sufficient online data, it can model user registration, login, order, and purchasing behaviors through risk control and operational data. Using machine learning and data analysis, unusual behavior patterns—such as frequent registration or ordering—can be identified. The output of these models provides effective support for risk control strategies, allowing for timely detection and response to abnormal activities.

**4. Identifying Abnormal Accounts**

Quickly identifying suspicious accounts is key to preventing fake orders. Platforms can monitor several types of suspicious behaviors, such as repeated activations on the same device, associations between one device and multiple IP addresses, a high concentration of logins from the same IP in a short time, and unusual ratios of old device models or operating systems within a particular channel. Maintaining a dynamic whitelist and blacklist can help the platform continuously update and manage data, such as user IDs, phone numbers, and device information, blocking suspicious accounts in a timely manner.

Dingxiang Dinsight assists businesses with risk assessment, fraud detection, and real-time monitoring to enhance the efficiency and accuracy of risk control. Dinsight’s average processing speed for daily risk control strategies is under 100 milliseconds. It supports multi-source data configuration and integration, using well-developed indicators, strategies, and model experience, as well as deep learning techniques. This enables real-time self-monitoring and self-iteration. In conjunction with Xintell’s intelligent modeling platform, known risks can be automatically optimized. Based on risk control logs and data mining, potential risks can be identified and configured with a single click to support risk control strategies in various scenarios. With deep learning technology, Xintell standardizes complex processes, providing one-stop modeling services from data processing and feature engineering to model building and deployment.

**5. Strengthening Mechanisms to Block Abnormal Accounts**

To improve prevention effectiveness, platforms need to intercept abnormal accounts by increasing the difficulty of verification elements, such as frequently updating the verification image library to raise the operational cost for water armies and fake order participants. Additionally, comparing verification environment information with normal user behavior helps quickly detect anomalies, further securing the platform.

Dingxiang atbCAPTCHA, based on AIGC technology, can prevent threats from AI brute-force cracking, automated attacks, and phishing, effectively blocking unauthorized access, account theft, and malicious operations, thus protecting system stability. It integrates 13 verification methods and multiple risk control strategies, combining 4,380 risk policies, 112 categories of threat intelligence, and covers 24 industries with 118 types of risks. Its detection accuracy reaches 99.9%, and it quickly transforms threats into actionable intelligence. With real-time response capabilities reduced to 60 seconds, it also enhances the convenience and efficiency of digital login services for genuine users.


This Fraud Ring Has Been Caught! Mainly Boosted Online Influencers by Fake Orders and Assembled Internet Water Armies

Mr. Wang, a citizen, received a FaceTime call displaying the name “China Banking Regulatory Commission.” The caller, posing as a representative, informed him that the government had lowered loan 

Mr. Wang, a citizen, received a FaceTime call displaying the name “China Banking Regulatory Commission.” The caller, posing as a representative, informed him that the government had lowered loan interest rates and asked if he had any loans, offering to help lower his rate. Since Mr. Wang did indeed have a mortgage and had also used a consumer loan platform, he believed the caller. He agreed to the caller’s screen-sharing request. The caller also provided a website link, which appeared to be an official site. Following the “representative’s” step-by-step instructions, Mr. Wang not only transferred tens of thousands of yuan from his account but also took out two or three loans from lending platforms, suffering a total loss of over 90,000 yuan.

![2024110101.png](https://cdn.aisecurius.com/images/7ae/7ae2c334-e3c4-4d0b-bd9a-6ae57db81926.png)

Recently, reports have surged nationwide of scams involving iPhone FaceTime calls. These new fraud tactics are constantly evolving, even incorporating “AI face-swapping” technology to deceive victims. Police advise verifying the authenticity of suspicious calls.


# FaceTime Screen-Sharing Scam Process


FaceTime, an in-built feature on Apple devices, offers a screen-sharing function, and all reported victims are iPhone users. During a FaceTime call, the scammer only needs to send a screen-sharing request, and once the victim accepts, all actions on the victim’s screen become visible to the scammer. The scammer can then monitor the victim’s every move, watching them input sensitive information such as bank account numbers and passwords.
![2024110102.jpg](https://cdn.aisecurius.com/images/bc1/bc1b5da3-b4b4-415b-88fd-d4310ddfb6c3.jpg)

1. **Establishing Trust with the Victim**  
   Scammers use FaceTime’s ability to change the display name, posing as reputable institutions like the “China Banking Regulatory Commission,” “China UnionPay Service,” “Alipay Customer Support,” or even as law enforcement agencies to deceive the victim. Most users, seeing such “trustworthy” caller ID, lower their guard and become more susceptible to fraud.

2. **Exploiting Screen-Sharing to Steal Sensitive Information**  
   FaceTime’s screen-sharing function allows the scammer to see the victim’s screen in real time, guiding the victim to input sensitive information like login details and passwords. The scammer monitors the screen, capturing this valuable data without the victim’s awareness.

3. **Guiding the Victim’s Actions**  
   The scammer may coax the victim to make a transfer or direct them to a fake official website or phishing link, ultimately prompting the victim to enter banking details, passwords, or other sensitive data. If the victim hesitates, the scammer may intercept a verification code to execute the transaction independently, transferring funds without the victim’s knowledge.


# Preventive Measures for Apple iPhone Users


1. **Verify Identity Through Multiple Channels**  
   Law enforcement, banks, and government agencies typically do not use FaceTime or similar video apps to contact users. If in doubt, verify the identity through officially published contact information rather than trusting the displayed caller name.

2. **Disable the FaceTime Sharing Feature**  
   If not needed for daily use, go to “Settings” on the iPhone, find the “FaceTime” option, and turn it off to prevent scammers from initiating fraud through FaceTime.

3. **Avoid Screen Sharing with Strangers**  
   Never share your screen with strangers, regardless of the video software used. Be especially cautious if the caller claims to be an official representative and requests to view personal account activities.

4. **Be Wary of Phishing Websites and Fake Apps**  
   Do not click on unfamiliar links, especially those provided during screen sharing. Only download apps from the Apple Store to avoid malicious links or fake applications.

5. **Protect Personal Information and Privacy**  
   Do not disclose personal sensitive information such as facial recognition data, bank card numbers, dynamic verification codes, or account passwords to strangers. Maintain a high awareness of information security to prevent exploitation.

6. **End Suspicious Calls Promptly**  
   If you receive a suspicious video call or call, stay calm, make an excuse about poor signal, hang up, and immediately verify through another method. Avoid responding directly to potential scams.

In addition, Dingxiang's device fingerprinting technology can detect screen-sharing calls from apps in real time and issue immediate alerts to remind users to stay secure. When screen sharing is activated, Dingxiang’s device fingerprinting detects this and notifies users of potential risk.

FaceTime screen-sharing fraud is a new type of scam that uses video calls and screen-sharing features to deceive victims into unknowingly exposing account and personal information. By understanding the characteristics of such scams and enhancing security awareness, users can better protect themselves from potential losses. Increasing awareness of video sharing risks and maintaining vigilance against phishing sites can effectively reduce the likelihood of being scammed.

Apple iPhone Users Alert! Scammers are Using FaceTime for Fraud

Ms. Que from Chengdu encountered a typical online scam. She received a delivery package with a card reading “Claim Three Free Gifts for ¥0.”

As the Double 11 shopping festival kicks off, while consumers are busy purchasing favorite items, criminals are taking advantage by setting up various online fraud schemes. The Cybersecurity Bureau of the Ministry of Public Security advises the public to stay alert to avoid falling into these scams.

![2024102202.png](https://cdn.aisecurius.com/images/e3e/e3e1b706-fe26-4c5f-aa25-03552a9ef73f.png)

1. **Impersonated Customer Service Scams**: Scammers may pose as customer service representatives, sending fake messages or emails to request personal information or payments. Consumers should verify the customer service identity through official channels and avoid sharing personal details readily.

2. **Fake Shopping Websites**: These sites appear legitimate and attract consumers with low prices, but they’re traps set by scammers. Consumers should only shop on well-known e-commerce platforms and avoid direct payments to unknown sites.

3. **Pre-sale Scams**: Taking advantage of Double 11 pre-sale events, scammers deceive consumers into paying upfront, only to disappear afterward. Consumers should stick to pre-sale activities with platform guarantees and avoid paying strangers directly.

4. **Prize-Winning Scams**: Scammers may notify consumers by text or email about “winning” a prize, requiring a fee or taxes to claim it. Consumers should remember that legitimate prize draws do not ask for fees upfront.

5. **Delivery Scams**: Scammers posing as couriers send fake messages, tricking consumers into following a public account to steal their information. When receiving such messages, consumers should contact the delivery company directly for verification.

6. **Return Scams**: Using returns and refunds as an excuse, scammers trick consumers into providing one-time passcodes, gaining access to personal information or funds. Consumers should initiate returns through official channels and avoid trusting unknown calls or messages.


# Analysis of a Delivery Scam Process


Ms. Que from Chengdu encountered a typical online scam. She received a delivery package with a card reading “Claim Three Free Gifts for ¥0.” Tempted, she scanned the QR code on the card and added a supposed “customer service” account. She was then added to a group promoting cash-back schemes for small purchases and ultimately invested over ¥30,000. When she tried to withdraw her principal, the scammers gave various excuses and demanded additional deposits. Only then did she realize it was likely a scam.

These scams typically proceed in stages: they start by luring victims with free gifts or discounts, then lead them to join a cash-back group or download a fraudulent app. They establish trust through small rewards and ultimately refuse withdrawals, urging further investment. This scam tactic is highly concealed, technically sophisticated, and has a broad impact.

![2024102201.png](https://cdn.aisecurius.com/images/b64/b6485fdf-8975-4466-ae3e-3bc99738b9ec.png)

To prevent falling victim to such scams, the public should stay vigilant and avoid easily believing information or promises from strangers. Exercise caution with any financial transactions or investment opportunities, and be wary of sharing personal information, such as bank details or ID numbers. Safety tips from telecom operators and public security agencies should be carefully read and remembered.


# How Companies Can Prevent Fraudulent Deliveries


Dingxiang's Business Security Intelligence Center suggests that consumers should verify unknown deliveries by checking logistics and courier information on official websites and contacting the courier by phone. For unfamiliar packages, it’s essential to verify the courier's identity and inspect package contents. Additionally, consumers should avoid scanning QR codes or clicking links in unsolicited packages, especially if asked to provide sensitive information like bank or ID numbers.

To help logistics companies identify suspicious packages, Dingxiang recommends integrating its business security intelligence. This allows for screening high-risk numbers and staying informed on the latest fraud tactics to identify packages posted by fraud groups effectively.

Dingxiang’s Business Security Intelligence maintains over 30,000 risk sources, monitoring black-market communities, darknet forums, illegal sites and apps, crowdsourcing platforms, and legitimate data sharing to collect risk data. By profiling risk populations, scoring behavior, analyzing relationships, and detecting fraud networks, this service can provide insights into telecommunications fraud, IP and device risk, and transaction risk across various industries. Using a fraud risk verification module, it cross-references phone numbers in banking, transactions, and transfers to check if they are associated with fraud, scoring their risk levels.

Dingxiang also recommends courier companies use the Xintell Intelligent Modeling Platform to analyze data on senders, recipients, phone numbers, and devices. By leveraging Hadoop, custom components, and drag-and-drop operations, the platform simplifies big data analysis, making it accessible to engineers, data scientists, and business staff alike. Xintell supports anti-fraud efforts by providing model training for fraud detection, risk management, marketing, and customer segmentation, dynamically upgrading models over time.

Furthermore, courier companies should enhance training for couriers to help them recognize suspicious packages and handle unusual situations properly.

Effective prevention starts at the source. By monitoring for risks in shipments from the start, courier companies can significantly reduce losses and better safeguard recipients' financial security.


# Consumer E-Commerce Safety Warning


Online shopping festivals can be both exciting for consumers and profitable for fraudsters. Criminal groups often use sophisticated and concealed methods to target consumers on a large scale.

- **Verify Information**: Always verify the sender’s identity through official channels before providing personal information or making payments.
- **Choose Reputable Platforms**: Use known e-commerce platforms and avoid shopping on unknown sites.
- **Protect Personal Information**: Avoid sharing personal details, especially bank account numbers, passwords, and verification codes.
- **Be Wary of Suspicious Links**: Avoid clicking unknown links to prevent data theft.
- **Report Suspicious Activity**: Immediately report any suspected fraud to authorities.

The Double 11 shopping festival is a peak period for fraud. Consumers should take precautions to enjoy their shopping safely and protect their personal assets.

Be Extra Cautious of This Kind of Delivery During This Year’s Double 11 Shopping Festival

On September 1, Professor Li Ling from the National School of Development at Peking University issued a public statement saying, “

“I believe this not only damages my own interests but, more importantly, harms public interests, especially for seniors who prioritize health and are drawn to various health remedies. I urge elderly friends to be cautious and verify the source before making purchases. Ask yourself, is this person an expert in this field? For example, I'm a policy expert, not a medical specialist, so I would never recommend such treatments.”
![2024101501.png](https://cdn.aisecurius.com/images/c29/c29e4e75-91c3-49fa-b0e9-57b6d6eeb7cf.png)

On September 1, Professor Li Ling from the National School of Development at Peking University issued a public statement saying, “Recently, numerous fake videos have appeared online, falsely using my image to promote medical books and treatment methods. These videos are largely AI-generated. I have never endorsed any medical books or products on any platform. Please be aware of this scam.” She has since reported the matter to the police.
![2024101502.png](https://cdn.aisecurius.com/images/2e0/2e0191f5-04f5-4c84-b6a2-5ef578290191.png)

Celebrities hold significant social influence and public trust. Scammers use the reputations of well-known individuals, like Professor Li, to create trust in their fake endorsements, especially among seniors concerned with health. By cloning her likeness with AI, fraudsters present a believable endorsement, making it harder for elderly viewers to doubt its authenticity. Apart from Professor Li, famous figures like Scarlett Johansson, Kylie Jenner, Taylor Swift, and Tom Hanks have also been impersonated in similar scams.


# The Scam Process in Fake Celebrity Ads


Fraudsters rely on AI technology to create fake celebrity advertisements in a multi-step scam, from data collection and AI-generated videos to ad distribution and tricking victims into transferring money. By exploiting celebrity trust and technology, they create highly convincing scams.

1. **Data Collection**: Fraudsters begin by gathering public information on the targeted celebrity. This includes photos, videos, public speeches, interviews, and social media posts, all of which provide material for AI-generated content. For Professor Li’s case, scammers likely obtained footage of her public speeches or academic lectures to capture her voice, tone, and facial expressions.

2. **Fabrication of AI-Generated Audio and Video**: With sufficient material, scammers use AI to synthesize audio and video. AI technology can accurately replicate a person’s voice and facial expressions to create realistic fake advertisements. In Professor Li’s case, the fraudsters likely produced a video where she appears to endorse health products or medical books she has never promoted.

3. **Spreading Fake Ads**: The resulting fake video is then spread across various online platforms, such as short video apps, social media, and group chats. These platforms are frequently accessed by older adults, the scammers’ primary target audience. Using targeted marketing, the fake ads reach specific groups. Due to the striking similarity of these videos to genuine content, viewers are easily deceived.

4. **Creating a Sense of Urgency and Authority**: To boost their success rate, scammers often use scare tactics or emphasize the authority of the content. For example, they might exaggerate the risks of certain diseases or claim the promoted product is a “limited-time offer” or “exclusive.” For health-conscious seniors, fraudsters may even fake expert qualifications, fabricating scientific evidence to strengthen trust.

5. **Inducing Purchases**: When convinced, the victim is prompted to buy the fake product through links or customer service hotlines embedded in the video. In Professor Li’s case, scammers might include a link or QR code in the video, prompting victims to pay for bogus “medical books” or “treatment products.” Often, these products are either non-existent, of poor quality, or completely worthless, resulting in financial loss for the victim.


# How Ordinary People Can Identify Fake Celebrity Ads


Scammers leverage AI to create fake celebrity endorsements, following a multi-step process: data collection, synthetic audio/video production, ad distribution, and finally, persuading victims to transfer funds. How can regular people detect these fake celebrity endorsements?

1. **Be skeptical of shocking or “too good to be true” statements, especially involving celebrities**. Always apply common sense and logic.
2. **Look for unnatural features** in the content, like odd blinking patterns, mismatched audio, or distorted body parts, as these could signal deepfakes.
3. **Cross-check statements with reliable news media and websites**. Don’t believe content without fact-checking first.
4. **Before making a purchase or decision, consult friends, family, or colleagues for advice** and do some research. Don’t take the initiator’s words at face value, even if it’s someone like Elon Musk.
5. **Avoid commenting, sharing, or even clicking on unverified social media posts**. Interacting with accounts impersonating celebrities increases your scam risk.
6. **Never share personal details with unverified individuals, websites, or apps**.


# Platform Measures Against Fake Celebrity Ads


For fake celebrity ads and information on social media platforms, the Dingxiang Defense Cloud Business Security Intelligence Center suggests precise identification of fraudulent accounts as the first step in effective fraud prevention.

1. **Identify abnormal devices**. Dingxiang Device Fingerprinting helps distinguish legitimate users from potential fraudsters by recording and comparing device fingerprints. It uniquely identifies devices, detecting malicious use of virtual machines, proxy servers, emulators, etc., while analyzing behaviors like multi-account logins, frequent IP address changes, and device property changes that don’t match normal user patterns.

2. **Detect suspicious account operations**. Unusual activities like logging in from different locations, switching devices, changing phone numbers, or sudden activity on dormant accounts require stronger verification. Continuous identity verification throughout sessions is essential. Dingxiang’s “No Touch” verification can quickly and accurately distinguish human users from bots, helping real-time monitoring and blocking of abnormal behavior.

3. **Prevent face-swapped fake videos**. Dingxiang’s full-spectrum facial security threat detection solution utilizes multi-dimensional data, including device environment, facial information, image authenticity, user behavior, and interaction state for intelligent verification. It quickly detects and blocks over 30 types of malicious behavior, including injection attacks, live forgeries, image forgery, camera hijacking, debugging risks, memory tampering, rooting/jailbreaking, malicious ROMs, and emulator usage. This solution flexibly adjusts video verification strength to allow seamless verification for legitimate users while increasing security for suspicious accounts.

4. **Identify potential fraud threats**. Dingxiang Dinsight aids businesses in risk assessment, anti-fraud analysis, and real-time monitoring, enhancing risk control efficiency and accuracy. Dinsight processes daily risk control strategies in less than 100 milliseconds on average, supports multi-party data integration, and leverages established indicators, strategies, and deep learning technologies for self-monitoring and iterative risk control. Coupled with the Xintell intelligent modeling platform, it automatically optimizes security strategies for known risks, configuring different scenarios to support risk control strategies. Using associative networks and deep learning, it standardizes complex data processing, feature derivation, model building, and deployment for an end-to-end modeling service.

How Ordinary People Can Identify Fake Celebrity Advertisements

The Hanyang Branch of the Wuhan Municipal Public Security Bureau received a report from a bank that two elderly individuals requested early withdrawal of fixed deposits.

The Hanyang Branch of the Wuhan Municipal Public Security Bureau received a report from a bank that two elderly individuals requested early withdrawal of fixed deposits. Bank staff suspected they were victims of telecom fraud. Though the two elderly individuals did not know each other, they coincidentally went to the same bank to conduct their transactions.

![2024101401.png](https://cdn.aisecurius.com/images/091/0917d53a-1446-4928-a9dd-d5594a1a8ad0.png)

Upon investigation, Mrs. Xiao and Mr. Yang both received calls from individuals pretending to be their grandsons. Mrs. Xiao was told that her grandson had injured someone in an altercation and was being mediated at a police station, while Mr. Yang’s "grandson" claimed to have been in a traffic accident and needed immediate compensation. Shockingly, both elderly individuals were convinced the voice on the phone sounded exactly like their grandsons.

![2024101402.png](https://cdn.aisecurius.com/images/9cc/9ccb00d4-efe4-44a8-ba64-302ce27304a0.png)

However, after the police confirmed with the families, it was found that neither of their grandsons was involved in any of these emergencies. Police quickly determined this was a new type of fraud in which criminals use AI technology to clone the voices of the elderly's relatives, simulating a real conversation to craft an elaborate scam.


# Analysis of the Scammers' Process


In this type of precise telecom fraud, scammers use AI voice cloning technology to convincingly imitate the voice of a victim's relative, exploiting the elderly's trust and emotional dependence on family members, further enhancing the credibility of the scam. This not only makes the victim more likely to believe the phone call, but also puts them in a state of panic, leading them to make hasty, erroneous decisions.

1. **Obtaining Personal Information**: Scammers first acquire detailed personal information about the elderly's grandsons, including names, contact details, and schooling information, often through illegal data breaches, social media, or other illicit means. With this data in hand, scammers can use AI voice cloning technology to convincingly replicate the voice of the victims' grandsons. By accurately mimicking speech patterns and tones, the scammers make the deception even more believable.

2. **Creating Urgent Scenarios**: In the phone calls, scammers fabricated two different urgent situations — one claimed the grandson was involved in a school altercation, while the other said the grandson was in a traffic accident. Both scenarios played on the elderly's concern for their grandsons' safety, generating a sense of urgency that pressured the victims into reacting quickly. By creating this atmosphere of urgency, scammers manipulate victims into immediately transferring money to resolve the "emergency," emphasizing secrecy to prevent the victims from verifying the story with others.

3. **Exploiting Emotional Dependence**: Elderly individuals often have a strong emotional reliance on their family, especially on their children and grandchildren. When receiving a call from someone imitating a family member, the strong emotional connection can override rational thinking. Scammers exploit this deep-seated trust and care, using it as the point of entry to successfully manipulate victims into making irrational decisions. By deliberately creating a sense of urgency, scammers can cause elderly victims to lose their ability to think critically and act hastily. The elderly, due to their lack of experience in dealing with unexpected crises and their lower adaptability to new technology, are particularly vulnerable to being led by scammers under stress and anxiety.

4. **Key Role of AI Voice Cloning Technology**: AI voice cloning is the most crucial tool in these scams. For the elderly, the cloned voices sound genuine and are difficult to distinguish from the real thing. Most older individuals are not aware of or equipped to handle such sophisticated technological fraud, making them more susceptible to these types of schemes. Additionally, scammers typically isolate the victim, urging them not to inform anyone else, especially family members or bank staff. This isolation tactic minimizes the chances of the victim seeking external help or verifying the legitimacy of the situation, allowing the fraud to succeed more easily.


# How the Elderly Can Prevent AI Phone Scams


In this type of sophisticated telecom fraud, the use of AI-generated realistic audio and video makes it difficult for victims to detect anomalies through traditional methods. Therefore, even after "verifying" the caller's identity via phone or video, the elderly must remain highly vigilant when facing urgent financial requests. When it comes to financial transactions, it's essential to use multiple methods to confirm the identity of the other party and avoid making hasty decisions that could lead to falling into a trap. The **Dingxiang Business Security Intelligence Center** suggests the following effective preventive measures for the elderly:

**1. Redial the Call:**  
When receiving a suspicious phone call or video message, stay calm and avoid immediately responding to any requests. You can use a pretext, like poor signal, to hang up the call. Then, redial using a known, secure contact method to verify whether the request genuinely came from a family member. This prevents you from responding directly to fraudulent information.

**2. Use a Security Keyword:**  
Set up a "security word" or "challenge question" that only you and your close family members know, to verify the caller's identity. If you receive a suspicious call, ask the caller to provide this keyword. If they cannot answer correctly or attempt to avoid the question, this should be a warning signal of a potential scam. Immediately hang up and verify the identity through a secure method.

**3. Consult Family and Friends:**  
If faced with urgent financial requests, first check with other family members or trusted individuals to verify if they are aware of the situation or if they have also received a similar call. This prevents the pressure and confusion caused by handling false information alone. Consulting trusted relatives can help you assess the authenticity of the situation.

**4. Report to the Authorities:**  
Dial the local emergency number, **110**, to report the suspected scam in detail to the police.

These preventive measures not only help individuals defend against AI scams but also remind the elderly to stay calm and vigilant when dealing with sudden financial requests, avoiding falling into the traps set by criminals.


Two elderly people rush to the bank after receiving fraudulent AI-cloned calls

During the National Day holiday, a large number of videos featuring "Lei Jun's AI voice" appeared on short video platforms. In these videos

During the National Day holiday, a large number of videos featuring "Lei Jun's AI voice" appeared on short video platforms. In these videos, "Lei Jun" sharply commented on popular topics such as traffic jams and holidays, and some even contained inappropriate language. Besides the complaints, the "Lei Jun" in the video also claimed to be investing in a certain university.

![2024101103.png](https://cdn.aisecurius.com/images/dab/dabaf1da-1258-4bde-a325-e856947b1946.png)

AI has been widely applied in many fields, from generating images and videos to answering online queries, leading to a wide array of content. When ordinary people can easily use AI to impersonate celebrities and mass-produce audio and video, it inevitably brings problems like information distortion and cognitive confusion to society.

According to *The New York Times*, AI-generated images of Elon Musk have appeared in thousands of fake advertisements, resulting in billions of dollars in fraud cases. An 82-year-old retiree, Tiv Beauchamp, saw a “real interview” with Musk online. In the video, Musk endorsed an aggressive investment opportunity that promised quick returns. The lip movements and voice were highly synchronized, making the video seem very real. The elderly man, believing it to be authentic, contacted the company behind the ad and followed their guidance to set up an account. Over the following weeks, the elderly man spent his entire retirement savings in a series of transactions, ultimately investing over $690,000 (around 4.95 million RMB). Unfortunately, all this money was swindled by scammers, leaving the man devastated.


# These 10 Celebrities Are Frequently Impersonated by Scammers


The advent of AI has completely transformed many industries. Using AI to easily forge the likeness and voice of celebrities is not only used in phishing scams but also to promote fraudulent products, steal personal information, and trick people into downloading malware. Fans often find it difficult to tell if the content is real or fake.

![2024101101.jpg](https://cdn.aisecurius.com/images/2cd/2cdd967b-c455-4e13-b997-bdccee7f564a.jpg)

Social media is an essential platform for marketing and advertising, making it a prime channel for scams. In a report released by the security company McAfee, they listed the 10 most impersonated American celebrities. Criminals exploit their image, voice, and reputation to conduct phishing, cryptocurrency fraud, ticket resale scams, false event promotions, spread misinformation, and promote malware.

- **Scarlett Johansson**: Actor and singer, her name and likeness have been used by scammers for false advertising endorsements.
- **Kylie Jenner**: Reality TV star, her name and likeness have been used by scammers in giveaway scams, fake product promotions, and phishing websites.
- **Taylor Swift**: Singer, her name and likeness have been used by scammers for fake endorsements, ticket scams, giveaways, and spreading false information.
- **Anya Taylor-Joy**: Actor, her name, likeness, and social media accounts have been used by scammers in giveaway scams and spreading false information.
- **Tom Hanks**: Actor, his name and likeness have been used by scammers to promote fake “miracle cures” and fraudulent medical treatments.
- **Sabrina Carpenter**: Singer, her name and likeness have been used by scammers in fake ticket scams and the creation of explicit apps.
- **Sydney Sweeney**: Actor, her name and likeness have been used by scammers in cryptocurrency scams.
- **Blake Lively**: Actor, her name and likeness have been used by scammers in weight-loss gummy scams.
- **Johnny Depp**: Famous actor and director, his name and likeness have been used by scammers for fake endorsements, fraudulent gifts, and cryptocurrency scams.
- **Addison Rae**: Top social media star, her name and likeness have been used by scammers for fake endorsements, fraudulent gifts, and cryptocurrency scams.


# How Ordinary People Can Identify Fake Celebrity Ads


How can ordinary people identify fake celebrity ads? There’s no simple answer, but some essential measures can help maximize the chances of detection.

1. Social media platforms are hotbeds for misinformation and online scams. Be skeptical of shocking or too-good-to-be-true claims, especially when celebrities are involved. Don’t forget to use common sense and logic.
2. Carefully check for unnatural features in the content – strange blinking patterns, mismatched audio, or distorted body parts could be signs of deepfakes.
3. Verify the claims by cross-referencing with trustworthy news outlets and websites. Do not believe content without first performing fact checks.
4. Before making decisions or purchases, consult with friends, family, or colleagues. It’s best to do some deep research and not take the promoter’s word as gospel, even if it’s Elon Musk.
5. Don’t comment, share, or even click on unverified social media posts. Interacting with accounts impersonating celebrities increases your risk of being scammed.
6. Never submit your personal details to unverified individuals, websites, or apps.

### Social Media Platforms’ Defense Against Fake Celebrity Ads

In addressing the problem of fake celebrity ads and information on social media platforms, **Dingxiang Defense Cloud Business Security Intelligence Center** suggests that first, accurately identifying fake accounts is crucial to effectively preventing fraud.

1. **Identify abnormal devices**: Dingxiang's Device Fingerprint technology records and compares device fingerprints to distinguish between legitimate users and potential fraudsters. By uniquely identifying each device, it can detect maliciously controlled devices such as virtual machines, proxy servers, and emulators. The technology analyzes whether devices exhibit abnormal behaviors, such as multi-account logins, frequent IP address changes, or frequent modifications to device attributes, which deviate from user habits. This helps trace and identify fraudsters’ activities.

2. **Identify abnormal account operations**: Remote logins, device switches, phone number changes, and suddenly active dormant accounts require enhanced frequent verification. Additionally, continuous identity verification during sessions is critical. Keeping persistent checks ensures that the user's identity remains consistent throughout use. **Dingxiang’s atbCAPTCHA** quickly and accurately differentiates between human operators and bots, precisely identifying fraudulent activities, monitoring in real-time, and intercepting abnormal behaviors.

3. **Prevent fake videos with face-swapping**: Dingxiang's full-chain, panoramic facial security threat awareness solution intelligently verifies through multiple dimensions, including device environment, facial information, image forgery detection, user behavior, and interaction status. It quickly identifies more than 30 types of malicious attack behaviors, such as injection attacks, live body forgeries, image forgeries, camera hijacking, debugging risks, memory tampering, root/jailbreak, malicious ROMs, and emulators. Once fake videos, false face images, or abnormal interaction behaviors are detected, operations are automatically blocked. The video verification intensity and user-friendliness can be flexibly configured to dynamically adjust the verification for normal users while strengthening it for suspicious ones.

4. **Detect potential fraud threats**: Dingxiang's **Dinsight** helps enterprises conduct risk assessments, anti-fraud analysis, and real-time monitoring, improving the efficiency and accuracy of risk control. Dinsight’s daily risk control strategy has an average processing speed of under 100 milliseconds and supports the configurable access and accumulation of multi-source data. Based on mature indicators, strategies, and model experience reserves, as well as deep learning technology, it achieves a self-monitoring and self-iterating mechanism for risk control performance. Paired with **Xintell**, a smart model platform, it can automatically optimize security strategies for known risks. Using risk control logs and data to mine potential threats, Xintell allows for one-click configuration of risk control strategies across various scenarios. It standardizes the complex processes of data processing, mining, and machine learning, providing a one-stop modeling service from data processing and feature derivation to final model deployment.

Not Just Lei Jun, These 10 Celebrities Are More Frequently Impersonated by Scammers

Recently, the Chongqing police announced five typical cases of rumors fabricated using AI tools. One of these cases, "

Recently, the Chongqing police announced five typical cases of rumors fabricated using AI tools. One of these cases, "The Hechuan District Public Security Bureau's Handling of Shi for Fabricating a Fake Traffic Violation Ticket with AI Tools," showed that in July 2024, Hechuan District internet user Shi (male, 45) posted a picture of a traffic violation ticket on a short video platform, with a caption stating, "Whistling at a female traffic cop while driving, fined 200 yuan and 11 points deducted." The video attracted widespread attention and discussion among netizens.

![2024101301.png](https://cdn.aisecurius.com/images/4be/4be9a046-25ef-4f7d-acb0-d626b62130c2.png)

The public security agency found that Shi had used AI software to create the fake traffic violation ticket to gain attention, which disrupted public order and had a negative social impact. Shi confessed to the illegal act, and the Hechuan District Public Security Bureau imposed administrative penalties on him according to law.


# How to Identify Internet Rumors

Internet rumors have become an urgent issue that needs addressing in today's online environment. In daily life, individuals need to be vigilant in recognizing and resisting internet rumors.

**Curb Curiosity.**  
When faced with strange or exaggerated information, avoid the temptation to immediately explore further. AI-fabricated rumors are designed to capitalize on trending topics, leveraging the curiosity of netizens to spread false information.

**Verify Unverified Sources.**  
Avoid blindly believing or sharing unverified information. When encountering suspicious content on social media or the internet, first verify it or wait for official announcements before forming conclusions.

**Use Logical Analysis.**  
Pay attention to the logic of online information. If a piece of content has obvious logical flaws or contradictions, it is likely fabricated or manipulated.



# Platforms Strengthen the Detection of Internet Rumors

Rumormongers often use trending news and random online audio/video as material to mass-produce fake news using AI tools. They then distribute it across major online platforms to attract attention or monetize traffic. Some internet platforms, driven by a "traffic-first" mentality, allow fake news, clickbait, and controversial topics to spread unchecked. This creates a favorable environment for AI-generated rumors to thrive, accelerating the formation of a gray industry chain. Content platforms must strengthen management in terms of technology, content, and user accounts.

1. **Strengthen Content Authenticity Identification.**  
Platforms should effectively detect and address content potentially generated by AI to prevent its harmful impact on society. For example, image authenticity detection tools can analyze pixel-level anomalies and imperfections in generated images to determine whether they were created by AI. Additionally, by analyzing user behavior patterns and identity information, platforms can establish security alert mechanisms. Suspicious behavior, such as abnormal logins or frequent information dissemination, can be monitored and restricted. For example, analyzing mouse movement patterns and typing styles can detect abnormal activities, marking deviations from normal user behavior. Additional identity and device verification, along with large models, can quickly sift through massive data sets to identify subtle inconsistencies that are otherwise undetectable by humans, exposing attackers' abnormal operations.

2. **Enhance Multi-Layer Content Verification.**  
To prevent the spread of false information, platforms can introduce multi-layer verification mechanisms for sensitive content. By integrating third-party fact-checking organizations and AI tools, platforms can automatically verify the authenticity of published content. If a risk of falsehood is detected, the platform can label the content as "pending verification" or "suspected misinformation." Additionally, platforms can utilize user reporting systems and feedback from trusted users to help identify rumor content. By incorporating user rating systems, platforms can automatically mark rumored content as low credibility.

3. **Establish Account Reputation Management.**  
Platforms can analyze user behavior patterns and history to identify malicious rumormongers. By monitoring suspicious account activity, such as frequently posting sensitive or false information or widely spreading unverified content, platforms can swiftly identify and restrict rumor-spreading behavior. A user reputation scoring system can be established, where content credibility, reports, and behavioral history adjust the spread of that user's content. High-risk users can be restricted from posting content or receive warning notifications.

Chongqing Police Announce Several Cases of AI-Fabricated Rumors

To protect member data privacy and ensure account security, KrSpace has selected Dingxiang atbCAPTCHA as the tool for registration, login, and identity verification in its member management system.

KrSpace is a leading flexible office service provider. It has creatively launched the KrPass office pass, which offers nationwide access, time-sharing office options, and cost-effectiveness. KrPass is mainly aimed at freelancers, startups, and small teams outside corporate headquarters who require flexible office space. It provides a more efficient office solution. By becoming a KrPass member, individuals can enjoy access to desks, chairs, power and internet, printing equipment, package lockers, property management, cleaning services... as well as free coffee and tea. It meets the daily work needs, client meetings, business dealings, and other diversified business requirements.

![2024101202.png](https://cdn.aisecurius.com/images/557/5574c407-9dbf-44c6-99f2-9a25ca4df6c7.png)

As a crucial platform for KrPass services, KrSpace's member service management system enables nationwide member information synchronization, allowing seamless transitions across KrSpace locations in any city without the need for repeated registration. The self-service cloud printing feature allows members to upload and print documents at any location, while administrators can remotely grant permissions to members through the self-service authorization feature. The self-service invoicing feature meets financial needs nationwide, ensuring regulatory compliance in financial management. Additionally, the system integrates various intelligent access control solutions such as WeChat, app-based QR code scanning, and membership card swiping, providing members with convenient access.

To protect member data privacy and ensure account security, KrSpace has selected Dingxiang atbCAPTCHA as the tool for registration, login, and identity verification in its member management system.

![2024101201.png](https://cdn.aisecurius.com/images/a38/a380cff9-cf5d-4749-a05e-15ea03587d0b.png)

Dingxiang atbCAPTCHA, based on AIGC technology, can prevent AI-based brute-force attacks, automated attacks, phishing threats, and more. It effectively blocks unauthorized access, account hijacking, and malicious operations, ensuring the stability of the system. It integrates 13 authentication methods and multiple defense strategies, compiling 4,380 risk policies and 112 categories of risk intelligence, covering 24 industries and 118 types of risks. Its accuracy rate for risk prevention reaches 99.9%, and it quickly converts risks into actionable intelligence. Furthermore, it allows secure users to pass through without interruption, reducing real-time response time to within 60 seconds. This significantly improves the convenience and efficiency of the account login service, effectively safeguarding user accounts and website information security.

KrSpace operates over 50 communities in 13 cities, including Beijing, Shanghai, Guangzhou, Hangzhou, Nanjing, Suzhou, and Wuhan. The KrPass pass can be used across different communities within the same city and is also valid nationwide, enabling seamless work progress.

Dingxiang atbCAPTCHA Enhances KrSpace Member Account Experience and Security

Recently, the Hangzhou Public Security Bureau disclosed a major online fraud case during a press conference, 

Recently, the Hangzhou Public Security Bureau disclosed a major online fraud case during a press conference, involving the use of AI technology to forge live-action videos, bypassing biometric login authentication on certain platforms. This case highlights how AI technology is being abused by criminals, posing a significant threat to network security defenses.

![2024062706.jpg](https://cdn.aisecurius.com/images/5fd/5fd2b3ee-9bd5-49db-8a42-9813f8d2767e.jpg)

In June this year, Hangzhou cyber police discovered a criminal gang on an overseas platform claiming that they could bypass the biometric authentication of some leading platforms using AI face-swapping technology, illegally obtaining other people's network accounts and information. The gang attracted individuals by promoting services to "help retrieve others' network information," encouraging the use of AI technology for network intrusion activities.

Police investigations revealed that criminals were relying on AI large model technology from overseas sources. By simply uploading a photo, they could generate forged videos through text-based dialogues. These fake videos were used to bypass biometric login procedures on platforms, enabling the criminals to forcibly log into the target user's account and steal personal information.

After obtaining access to others’ accounts, the stolen private data and sensitive information were either sold to fraud groups or used to satisfy some individuals' desire to snoop on others' privacy. Since May this year, the gang had been advertising their "services" on overseas platforms and had successfully stolen large amounts of user data through AI-generated fake identities.

After a thorough investigation, police arrested four key suspects—Hu Mouyun, Hu Mouliang, Zhang Mouguo, and Wu Mouhao—on August 7 in Anhui, Guizhou, and Zhejiang. The gang charged between 2,000 and 5,000 RMB per login attempt, accumulating over 200,000 RMB in illegal profits. The case is still under further investigation.

During the investigation, police found that some platforms' biometric technologies had security vulnerabilities, with relatively simple identification methods, providing opportunities for criminal gangs. In contrast, some platforms had more complex identification systems with stronger defenses against AI-generated videos. This disparity in security technology across platforms highlights the need for further strengthening of login authentication mechanisms to prevent such new types of fraud.

The Hangzhou police advise users to avoid oversharing personal biometric information, such as frontal facial photos and fingerprints, online. Additionally, they recommend regularly monitoring login activities and, if any suspicious logins (such as from unfamiliar devices or IP addresses) are detected, immediately changing account passwords and contacting the platform for assistance.


# User Self-Protection Recommendations


This case not only reveals the risks of AI technology abuse but also reminds both platform operators and users to be vigilant and strengthen information security measures to counter the growing threats posed by emerging technologies.
![2024090402.png](https://cdn.aisecurius.com/images/364/364f34be-178a-41f8-8327-e5249e1cc7aa.png)

Preventing AI fraud requires both technical and behavioral measures to ensure personal information is not easily exposed. Here are specific preventive measures:

1. **Enhance Identity Verification**  
   Enable two-factor authentication (2FA) or multi-factor authentication (MFA), such as SMS verification codes or dynamic passwords, to increase account security. Even if the biometric system is compromised, the additional layers of verification can effectively prevent unauthorized logins.

2. **Limit Sharing of Sensitive Information**  
   Avoid oversharing photos or videos with frontal facial images on social media and other public platforms to prevent AI from using these materials to generate forged information. Only use fingerprint or facial recognition on secure platforms, and choose trusted technology providers.

3. **Regularly Check Account Security**  
   Periodically review the login records provided by platforms to check for any logins from suspicious devices or locations. If any suspicious activity is detected, immediately change your password and notify the platform.

4. **Be Cautious of Suspicious Requests**  
   Even if family or friends "verify" their identity through video or voice, do not easily trust them, especially when financial transactions are involved. Confirm their identity through another channel. Agree with your family or friends on a "security question" that only you know, and use it to verify their identity in case of suspicious requests.

5. **Increase Awareness of AI Technology**  
   Familiarize yourself with common characteristics of AI-generated fake videos or audio. For example, during a video call, ask the other party to perform specific actions, gestures, or expressions to help determine if the video is AI-generated.

By following these preventive measures, individuals can significantly reduce the risk of falling victim to AI fraud. Even as AI technology advances, users can still protect their information by raising security awareness and using reliable security tools.


# Platforms Should Strengthen Prevention


Enterprises can adopt multiple technologies and measures. Additionally, promoting the positive use of AI technology and strictly cracking down on criminal activities is the fundamental solution.

1. **Identify Abnormal Devices**  
   Dingxiang Device Fingerprinting can identify legitimate users and potential fraud by recording and comparing Device Fingerprinting data. It uses unique device identification and recognition technology to detect devices under malicious control, such as virtual machines, proxy servers, simulators, and so on. The system analyzes whether a device is logging into multiple accounts, frequently changing IP addresses, or modifying device attributes, identifying unusual behaviors that don't match user habits. This helps to track and identify fraudulent activities.

2. **Identify Abnormally Operated Accounts**  
   Accounts that log in from different locations, change devices, change phone numbers, or suddenly activate after being dormant need frequent verification. In addition, continuous identity verification during a session is crucial to ensure the user's identity remains consistent throughout the usage period. Dingxiang atbCAPTCHA can quickly and accurately distinguish between human operators and machines, identifying fraudulent activities with precision, and monitoring and intercepting abnormal behaviors in real-time.

3. **Prevent Fake Videos from Face-Swapping**  
   Dingxiang's Full-Chain, Panoramic Face Security Threat Detection Solution verifies information across multiple dimensions, including device environment, facial information, image forgery, user behavior, interaction status, and more. It quickly identifies over 30 types of malicious attacks, such as injection attacks, live body forgery, image forgery, camera hijacking, debugging risks, memory tampering, root/jailbreak, malicious ROM, and simulators. Once fake videos, false face images, or abnormal interaction behaviors are detected, the operation can be automatically blocked. It also flexibly configures the intensity and friendliness of video verification, implementing a dynamic mechanism where normal users are verified with atbCAPTCHA, while suspicious users undergo enhanced verification.

4. **Uncover Potential Fraud Threats**  
   Dingxiang Dinsight helps enterprises with risk assessment, anti-fraud analysis, and real-time monitoring, improving risk control efficiency and accuracy. The average processing speed for Dinsight's daily risk control strategies is within 100 milliseconds. It supports configurable access and accumulation of multiple data sources and can perform self-monitoring and iterative upgrades of risk control mechanisms using deep learning technology. Coupled with the Xintell Intelligent Modeling Platform, known risks can undergo automatic security strategy optimization. Based on risk control logs and data mining of potential risks, it enables one-click configuration to support risk control strategies for different scenarios. Utilizing relational networks and deep learning technology, it standardizes complex processes like data processing, feature derivation, model building, and ultimately deploying models into production, providing an end-to-end modeling service.

Criminal Gang Uses AI to Forge Facial Videos, Successfully Logging into Others’ Accounts

In 2024, during the Ministry of Public Security's "Net Clean 2024" special operation, a case involving the use of AI to create fake obscene images attracted widespread attention.

In 2024, during the Ministry of Public Security's "Net Clean 2024" special operation, a case involving the use of AI to create fake obscene images attracted widespread attention.

![2024100701.png](https://cdn.aisecurius.com/images/a87/a87970a7-31a5-48c7-9006-076f0505444b.png)

The suspect, Liu Moumou, obtained photos of victims by secretly taking pictures and downloading them from social media platforms like "WeChat Moments." He then used AI software to create fake obscene images, incorporating the victim's personal information and spreading the images online, which caused large numbers of netizens to engage in attacks. This seriously impacted the victims' normal work and life. Liu Moumou has now been placed under criminal compulsory measures in accordance with the law.

#   Case Technical Process Analysis

In this case, the suspect, Liu Moumou, carried out the criminal activity through the following technical process:

1. **Stealing Photos**: The suspect first obtained photos through secret photography or by downloading them from social media platforms such as "WeChat Moments." The personal photos shared on these platforms became a "resource library" for the criminal's AI image forgery. Many people frequently share personal life photos on social media, often with weak privacy protection, providing convenient conditions for such criminal acts.

2. **Image Forgery**: Liu Moumou used available AI image generation software to combine the victim's real photos with obscene images, creating fake obscene pictures. By using AI technology to replace faces, adjust colors, and enhance features, highly realistic fake images were produced.

3. **Online Dissemination and Malicious Use**: After creating the fake images, Liu Moumou added the victim's personal information and widely spread the images on social networks, leading to mass attacks and accusations from netizens. This not only caused severe damage to the victim's mental well-being, reputation, and life but also triggered a larger-scale cyberbullying event.


# Preventing the Misuse of Personal Photos


1. **Focus on Personal Privacy Protection**: As social media has become an indispensable part of people's daily lives, avoiding excessive sharing of sensitive information is essential. Social media can become a warehouse of fraud materials for criminals. Avoid sharing personal photos, voices, videos, and other sensitive information on social media. Limit the exposure of private details such as personal accounts, family, or work to reduce the risk of identity theft. If you discover AI-generated fake voices or videos, immediately report them to social media administrators and law enforcement, and take steps to remove them and trace their source.

2. **Strengthen Content Review on Social Platforms**: Social platforms should implement effective identification and timely handling of content potentially involving AI forgery to prevent negative social impacts. For instance, image authenticity detection tools can analyze pixel-level anomalies and detect “flaws” in the image generation process to determine whether the image was AI-generated.

At the same time, social media platforms should establish a security alert system by analyzing user behavior patterns and identity information. They should monitor and restrict suspicious activities, such as abnormal logins and high-frequency messaging. For example, by analyzing mouse movement patterns, typing styles, and other user behavior patterns, platforms can identify abnormal situations, flag suspicious activities that deviate from regular use, and detect attackers' abnormal operations through extra identity and device verification. Large models can quickly filter massive amounts of data and identify subtle inconsistencies that humans may miss.

3. **Dingxiang Device Fingerprinting**: This technology generates a unified and unique device fingerprint for each device. It builds a multi-dimensional identification strategy model based on device, environment, and behavior. This model identifies risky devices, such as virtual machines, proxy servers, and emulators that may be maliciously controlled. It also analyzes whether a device has multiple account logins, frequently changes IP addresses, or alters device properties in ways that show abnormal behavior or deviate from typical user patterns. This helps trace and identify fraudsters’ activities, enabling businesses to manage the same ID across different channels and enhance cross-channel risk identification and control.

4. **Dingxiang Dinsight**: This tool helps businesses conduct risk assessments, anti-fraud analysis, and real-time monitoring, improving the efficiency and accuracy of risk control. Dinsight’s daily risk control strategies are processed within an average speed of 100 milliseconds. It supports the configuration and accumulation of multi-source data, allowing risk control to self-monitor and iterate using deep learning techniques. Paired with the Xintell intelligent model platform, Dinsight can optimize security strategies for known risks, mine potential risks from risk logs and data, and enable one-click configuration for different scenarios to support risk control strategies. Based on association networks and deep learning technology, the platform standardizes complex processes such as data processing, feature derivation, machine learning, and provides an end-to-end modeling service from data processing to final model deployment.

By leveraging these technologies, social platforms can more effectively screen and filter uploaded content, preventing the spread of fake images.


The Ministry of Public Security Discloses a Case of Using AI to Create Obscene Images

the Gaoxin Branch of Hefei Public Security Bureau released a police briefing, stating that the "Three Sheep" company reported that the online spread of audio and video involving Lu Moumou was false,


On September 26, the Gaoxin Branch of Hefei Public Security Bureau released a police briefing, stating that the "Three Sheep" company reported that the online spread of audio and video involving Lu Moumou was false, severely infringing on their legitimate rights.

Investigations revealed that on the evening of September 22, the suspect Wang Moumou used audio and video materials downloaded from the internet to fabricate a script of Lu Moumou’s alleged drunken remarks. Wang then trained AI tools to generate fake audio mimicking Lu Moumou. The female voice heard in the recordings was also generated using AI tools. Subsequently, Wang used video software to synthesize the audio and video, publishing them online, leading to widespread rumors.

Wang Moumou has been subjected to criminal compulsory measures, and the case is under further investigation.
![2024092701.png](https://cdn.aisecurius.com/images/9f5/9f5c71a9-b418-42a2-a5f3-5f4ec7624567.png)


# Where was Lu Moumou’s Voice Stolen From?


Cloning anyone’s voice using AI technology has become incredibly easy. By extracting voice samples from public sources, even just a few seconds of a sample can be enough to produce highly convincing voice clones. These samples can mimic not only a person’s tone and inflection but also their speech habits and mannerisms, making it extremely difficult for recipients to detect any discrepancies in authenticity.

Dingxiang’s Cloud Business Security Intelligence Center suggests multiple avenues for such information leakage:

1. **Social Media**: Social media has become an integral part of people’s daily lives. Over-sharing sensitive information on these platforms has turned them into a repository for criminals seeking fraudulent materials.

2. **Data Breaches**: Large-scale data breaches may expose personal information, including photos, videos, and audio. Criminals can acquire this leaked data from dark web platforms to carry out fraudulent activities.

3. **Phishing and Malware**: Criminals often use phishing emails or malware to extract personal information, including photos and videos. Once they gain access to a victim's device, they can further obtain additional personal data.

4. **Public Events and Live Gatherings**: At public places like conferences, exhibitions, or social gatherings, criminals may collect photos, voices, and videos of their targets.


# How Individuals Can Prevent Voice Cloning


Facing the challenge of AI voice cloning requires a multi-layered defense. On one hand, it’s crucial to detect and identify forged voices, with platforms needing to strengthen their capabilities to recognize voice cloning. On the other hand, preventing the misuse and spread of cloned voices through user behavior and identity verification is essential to reduce fraud by attackers effectively.

1. **Reduce Sharing of Sensitive Information**: Individuals should avoid sharing personal photos, voice, and video on social media to minimize the risk of identity theft. Limiting the public disclosure of personal accounts, family, and work-related private information reduces the chance of being targeted. If you come across “deepfake” content, immediately report it to social media administrators and law enforcement to have it removed and trace the source.

2. **Platforms Strengthen Voice Cloning Detection**: Social media and communication platforms must enhance their ability to detect voice cloning using advanced AI technology for authenticating voices, quickly identifying, and blocking suspicious content. Platforms should develop and apply AI tools for voice authentication, such as voiceprint recognition, to ensure the security of voice communication.

3. **Identify Abnormal Operations Through Behavior Analysis**: Platforms should analyze user behavior patterns and identity information to establish security warning mechanisms. This involves monitoring and restricting suspicious activities like abnormal logins or high-frequency messaging. By analyzing mouse movement patterns, typing styles, and other behavioral patterns, platforms can flag suspicious activities that deviate from normal usage. Additionally, extra identity and device verification, as well as large models capable of scanning vast amounts of data, can identify subtle inconsistencies that are usually undetectable by humans, helping uncover abnormal operations by attackers.

Dingxiang Device Fingerprinting can generate a unified and unique fingerprint for each device. It builds a multi-dimensional recognition strategy model based on device, environment, and behavior to detect risk devices like virtual machines, proxy servers, and simulators that may be maliciously manipulated. The system tracks and identifies fraudulent activities by analyzing abnormal behaviors, such as frequent IP address changes, multiple account logins, and alterations in device attributes, helping businesses manage risk across channels with a single ID.

Dingxiang atbCAPTCHA is based on AIGC technology and prevents AI brute force cracking, automated attacks, and phishing threats. It effectively blocks unauthorized access, account theft, and malicious operations, ensuring system stability. It integrates 13 verification methods and multiple defense strategies, with 4,380 risk strategies, 112 risk intelligence categories, covering 24 industries, and 118 risk types. Its accuracy rate is as high as 99.9%, quickly converting risk intelligence and reducing response time to under 60 seconds, further enhancing the convenience and efficiency of digital login services.

Dingxiang Dinsight’s real-time risk control engine assists businesses in risk assessment, anti-fraud analysis, and real-time monitoring, improving the efficiency and accuracy of risk management. Dinsight’s daily risk control strategy processing speed is under 100 milliseconds, supporting the integration and storage of multi-source data. It leverages mature indicators, strategies, model experience, and deep learning to achieve self-monitoring and iteration of risk control performance. Paired with the Xintell intelligent model platform, known risks are automatically optimized, and potential risks are identified through risk control logs and data mining. It offers one-click configuration for various scenarios, supporting risk control strategies. By standardizing the complex processes of data processing, feature derivation, model building, and deployment, Xintell provides an end-to-end modeling service.

4. **AI Voice Tools Should Enforce Anti-Counterfeiting Features**: For instance, incorporating small disturbances, erratic noises, or fixed background rhythms allows listeners to distinguish between real and AI-generated voices. Additionally, hardware that records audio in AI voice synthesis tools should integrate built-in sensors to detect and measure human biological signals when speaking, such as heartbeat, lung movement, vocal cord vibration, as well as the movement of lips, jaws, and tongue. This data can be attached to the audio, providing listeners with verifiable information to distinguish between naturally recorded voices and AI-generated forgeries.

Analysis of the "Three Sheep Recording" Incident: How Criminals Use AI to Synthesize Fake Audio and Video

Recently, Mr. Ma from Guyuan City, Ningxia, reported to local police that he had been a victim of telecom fraud, losing 15,000 yuan to a scammer pretending to be his cousin. 

Recently, Mr. Ma from Guyuan City, Ningxia, reported to local police that he had been a victim of telecom fraud, losing 15,000 yuan to a scammer pretending to be his cousin. This scam not only involved AI face-swapping technology but also exploited the victim's trust in his family members.

The incident began when Mr. Ma was browsing videos on a platform during his free time and suddenly received a message from his "cousin." The person claimed to be working in Africa and, due to the unstable local network, was unable to frequently contact family. The "cousin" then requested to be added as a friend, stating that he needed help with an urgent matter. Trusting his relative, Mr. Ma immediately accepted the friend request without hesitation.

![2024091801.png](https://cdn.aisecurius.com/images/e4e/e4eac092-d3c0-4d03-9e1d-3e621dfd3711.png)

After adding each other, the "cousin" made a video call. In the video, the "cousin" appeared normal, speaking familiarly, with a face that looked similar. He even mentioned some family matters, further dispelling Mr. Ma's doubts. During the chat, the "cousin" claimed that he urgently needed to sign an important contract with his company, but due to the unstable network in Africa, the transfer function could not be used properly. He asked Mr. Ma to temporarily help by covering the contract funds, promising to return the money once the network stabilized.

Based on the "verification" from the video call, Mr. Ma did not doubt the person and believed it was indeed his cousin. Trusting his family member, Mr. Ma quickly agreed to help. Following the "cousin's" instructions, he made three transfers to a designated bank account, totaling 15,000 yuan.

![2024091802.png](https://cdn.aisecurius.com/images/f20/f2024b0e-92bc-49cb-91f3-ae3bb1e6f90d.png)

However, a few days later, when Mr. Ma attempted to contact his cousin again through WeChat to ask for repayment, he sensed something was wrong. His real cousin said that he had never borrowed money from Mr. Ma, nor was he working in Africa. It was at this point that Mr. Ma realized he had been scammed and immediately reported the case to the police.

The police's initial investigation revealed that this was a typical case of modern telecom fraud. The scammer had used advanced AI face-swapping technology to forge the image of the "cousin" in the video to deceive Mr. Ma. Additionally, the scammer utilized voice-cloning technology to imitate the "cousin's" voice and accent, making the scam even more convincing.


# How to protect oneself


In such scams, fraudsters collect their target's voice patterns, speech, or facial information, and then use AI technology to create fake audio, video, or images, producing almost indistinguishable fake content. This increases the victim's trust and makes the scam highly deceptive. They then use excuses such as borrowing money, investments, or emergency rescues to lure the victim's friends or relatives into transferring money or providing sensitive information like bank account passwords.

This case serves as a reminder to the public that when faced with sudden financial requests from family or friends, even if there is a video "verification," one must remain cautious. When money is involved, it's important to confirm the identity through multiple channels. If necessary, direct phone calls or verification through other family members can help avoid falling into a trap.


### 1. Distinguishing during calls: 
During video chats, ask the other person to press their nose or face to observe changes. If it's a real person's nose, pressing it will cause deformation. You can also ask the person to eat or drink to observe facial movements. Alternatively, request odd gestures or facial expressions, such as waving or making a difficult hand gesture, to verify authenticity. During waving, facial data may be disrupted, causing some shaking, flickering, or anomalies.


### 2. Replaying the video:
 When receiving a suspicious video or call, stay calm, hang up under the pretext of a bad signal, and immediately call back through other means to verify the person’s identity, avoiding an immediate response to potential scam content.


### 3. Asking keyword questions: 
Set a "safety word" or "challenge question" that only friends, family, or colleagues would know. Use it to verify the identity of the caller when receiving suspicious communications. If the person cannot provide the correct safety word or avoids the question, hang up immediately and use a known safe contact method to confirm their identity.。


# How Platforms Can Prevent Fraud


It is recommended that enterprises adopt multiple technologies and methods. Moreover, promoting the positive application of AI technology and severely cracking down on criminal activities is the fundamental solution.


### 1. **Identifying suspicious devices**:
 Dingxiang Device Fingerprinting uses the recording and comparison of device fingerprints to distinguish legitimate users from potential fraudulent behavior. It uniquely identifies each device and detects maliciously controlled devices like virtual machines, proxy servers, and emulators. It also analyzes abnormal behaviors such as multi-account logins, frequent IP address changes, and device attribute modifications that deviate from normal user habits, helping to track and identify fraudsters.

### 
2. **Detecting suspicious account activity**: 
For scenarios such as remote logins, device changes, phone number replacements, and sudden activity from dormant accounts, frequent verification needs to be enforced. Additionally, persistent identity verification during sessions is critical to ensure the user's identity remains consistent. Dingxiang's atbCAPTCHA quickly and accurately distinguishes whether the operator is human or machine, precisely identifying fraudulent activities and monitoring and intercepting abnormal behavior in real-time.


### 3. **Preventing fake face-swapped videos**: 
Dingxiang's full-chain panoramic facial security threat detection solution uses multiple dimensions such as device environment, facial information, image forgery detection, user behavior, and interaction state for intelligent verification. It rapidly identifies over 30 types of malicious activities, including injection attacks, live body forgery, image forgery, camera hijacking, debugging risks, memory tampering, rooting/jailbreaking, malicious ROM, and emulators. Upon detecting fake videos, forged facial images, or abnormal interactions, it can automatically block operations. The system also allows flexible configuration of video verification intensity and user-friendliness, implementing dynamic mechanisms to strengthen verification for abnormal users while maintaining atbCAPTCHA for regular users.


### 4. **Uncovering potential fraud threats**: 
Dingxiang’s Dinsight real-time risk control engine helps enterprises conduct risk assessments, anti-fraud analysis, and real-time monitoring, improving the efficiency and accuracy of risk control. Dinsight’s average daily processing speed is within 100 milliseconds and supports the configuration and accumulation of multi-source data. It leverages mature indicators, strategies, model expertise, and deep learning technology to achieve self-performance monitoring and self-iteration of risk control mechanisms. Paired with Dingxiang's Xintell intelligent model platform, the solution automatically optimizes security strategies for known risks, extracts potential risks from risk control logs and data, and allows one-click configuration to support risk control strategies in different scenarios. The solution standardizes complex processes such as data processing, feature derivation, and model building through deep learning, providing a one-stop modeling service from data processing to final model deployment.


A man from Ningxia falls into the "African working cousin" scam, losing 15,000 yuan

Dingxiang App Security can also conduct security checks on apps, identifying potential risk vulnerabilities and making targeted fixes

In the wave of digital transformation, Jiahua Technology, as a full-industry chain IoT big data company, is leading industry innovation with its unique model of "developing products with data, providing services with algorithms." By building the "data factory" system, Jiahua Technology is productizing core technologies such as intelligent manufacturing, IoT, blockchain, identity resolution, and artificial intelligence. The company continuously deepens the research of large model technologies, providing strong support for the digital transformation of government and enterprise users.

![2024092002.png](https://cdn.aisecurius.com/images/59a/59aa379b-68e9-4892-9522-3fca70f73321.png)

Jiahua Technology is one of Beijing's first enterprises in the data asset registration pilot program and has received the country's first listed company data asset registration certificate and the first bank data asset pledge loan. This marks a significant breakthrough for Jiahua Technology in the assetization of data, setting a new milestone for the development of the digital economy.

Faced with the increasing complexity of mobile application attacks, Jiahua Technology has shown foresight by deploying Dingxiang App Security across multiple apps. Dingxiang App Security is an adaptive hardening solution based on graph neural network technology. By conducting in-depth analysis and feature extraction of app code, it automatically selects appropriate methods for encryption, obfuscation, and compression, increasing the difficulty of reverse analysis, effectively preventing apps from being cracked, copied, or repackaged by attackers. The use of encryption and obfuscation engines further enhances the security of the apps.

Compliance has become a key factor in app development. For app developers, compliance means strengthening the protection of user data, implementing appropriate security measures and technical means to ensure the security of user privacy. Additionally, developers must comply with relevant laws and regulations, clearly define user permissions and the purpose of information collection, and promptly update privacy policies and user agreements, providing users with transparency and choice.

Dingxiang App Security can also conduct security checks on apps, identifying potential risk vulnerabilities and making targeted fixes. It protects sensitive data, code obfuscation, code integrity, and memory data, preventing security risks from system vulnerabilities at the source, guarding against data leaks, and ensuring app security. It also provides real-time security monitoring of app operations, monitoring terminal devices, operating environments, and user behaviors, helping apps establish a real-time risk monitoring, warning, blocking, and traceability system.

The deployment of Dingxiang App Security not only enhances the security of Jiahua Technology's apps but also provides a solid foundation for its digital construction.

Dingxiang Assists Jiahua Technology in Digital Construction

Recently, employees at a courier company on Youth Road in Kunming City reported a suspicious situation involving a woman attempting to mail gold bars worth over 300,000 yuan

Recently, employees at a courier company on Youth Road in Kunming City reported a suspicious situation involving a woman attempting to mail gold bars worth over 300,000 yuan. Police quickly responded to the scene and successfully prevented a telecommunications fraud case.

Ms. Wang explained that the "best friend" in question was her former college roommate, who had helped her financially during tough times, and they had stayed in touch ever since. The friend later moved abroad, and despite the distance, they continued to communicate regularly through QQ. On the day of the incident, Ms. Wang received a video call request from her friend's QQ account. In the video, her friend asked Ms. Wang to buy 300,000 yuan worth of gold bars and send them to Guangzhou as a wedding gift. Trusting her long-time friend, Ms. Wang agreed to the request and even paid for the gold bars out of her own pocket.
![2024091401.png](https://cdn.aisecurius.com/images/0ea/0ea29d2b-538b-42a1-8b64-d1099dbfb409.png)

Following the police's advice, Ms. Wang immediately asked the "friend" to verify her identity through an international phone call or other means. Shortly after sending this message, Ms. Wang found that her QQ account had been deleted by the other party. It was then that Ms. Wang realized she had narrowly avoided falling into a telecommunications fraud trap that involved AI technology.


#  The Fraudster's Tactics

This is a classic case where fraudsters used AI technology to swap faces and clone voices to impersonate a friend and commit fraud.
![2024080801.png](https://cdn.aisecurius.com/images/d13/d13be02a-8054-4a98-8d45-78065bedb962.png)

In such scams, criminals collect target individuals' speech, facial data, or video, using AI technology to create fake audio, video, or images. They simulate others' voices or appearances to gain trust and then use excuses such as borrowing money, asking for investments, or emergency assistance to trick their victims into transferring funds or providing sensitive information like bank account passwords. Additionally, criminals might use AI to artificially create audio or video of celebrities, experts, or law enforcement personnel, impersonating them to spread false messages and achieve fraudulent goals.

# The Four Phases of the Scam

**Phase 1: Information Gathering.** The first step in the scam involves collecting the victim's personal information. Fraudsters obtain sensitive data such as facial photos, phone numbers, and voice recordings through black market transactions, illegal database leaks, or organized cyberattacks. This information serves as the foundation for their next steps.

**Phase 2: Forging Face Videos and Cloning Voices.** Once they have gathered enough personal information, criminals use software to capture the target's facial features and voice patterns. With AI synthesis technology, they create fake videos and audio clips.

**Phase 3: Identity Impersonation.** The fraudsters begin contacting the victim, mimicking the tone, speech style, and behavior of the person they are impersonating. They communicate through text, voice, or video, gradually lowering the victim's guard and gaining their trust.

**Phase 4: Inducing Victim to Transfer Money or Mail Goods.** Once the victim fully trusts the impersonated identity, the criminals make their move by asking for money transfers, payments for goods, or mailing packages. They often fabricate urgent situations or business needs, pushing the victim to transfer funds to a designated bank account. Under the combined pressure of urgency and trust, the victim often complies without further verification, following the fraudster's instructions.

# Technical Measures to Prevent DEEPFAKE Fraud

To prevent new types of telecommunications fraud involving "DEEPFAKE" and "AI voice cloning," it is essential to verify any suspicious online activity through offline methods. Increasing the duration of communication, using probing techniques, such as asking the other party to perform specific actions to reveal potential flaws, can be helpful. Additionally, companies should adopt multiple technical measures and tools, while promoting the positive application of AI technology and strictly cracking down on criminal behavior, as the fundamental solution.

1. **Identifying face-swapped videos and voices.** During video chats, one can ask the other party to press their nose or face to observe changes in facial features. If the person is real, their nose will deform when pressed. Alternatively, ask the person to eat food or drink water and observe facial movements. Another strategy is to ask them to perform unusual actions or expressions, such as waving or making difficult hand gestures, to detect any anomalies. Waving, for example, may interfere with facial data and cause some shaking, flickering, or other irregularities.

2. **Identifying abnormal devices.** Dingxiang Device Fingerprinting helps distinguish legitimate users from potential fraudsters by tracking and comparing Device Fingerprints. This technology assigns a unique identifier to each device, identifying malicious devices such as virtual machines, proxy servers, and emulators. It also analyzes whether the device has multiple account logins, frequently changes its IP address, or exhibits abnormal behavior inconsistent with typical user habits, aiding in the detection and tracking of fraudulent activities.

3. **Identifying accounts with abnormal operations.** Abnormal behaviors such as logging in from a different location, changing devices, changing phone numbers, or suddenly reactivating dormant accounts require frequent validation. Continuous identity verification during a session is crucial to ensure user identity consistency. Dingxiang's atbCAPTCHA can quickly and accurately distinguish between human and machine operators, precisely identifying fraudulent behavior and monitoring for abnormal activities in real-time.
![2024062705.png](https://cdn.aisecurius.com/images/e06/e06b5696-4cde-4099-bdfe-b33389a1def8.png)
4. **Preventing face-swapped fake videos.** Dingxiang's Full-Chain, Full-Perspective Facial Security Threat Detection Solution conducts intelligent verification through multi-dimensional data, including device environment, facial information, image forgery detection, user behavior, and interaction state. This enables rapid detection of injection attacks, fake live bodies, image forgery, camera hijacking, debugging risks, memory tampering, rooting/jailbreaking, malicious ROMs, emulators, and over 30 other malicious attack behaviors. Upon detecting fake videos, fake face images, or abnormal interaction behaviors, it can automatically block operations. Additionally, it allows for flexible video verification intensity and friendliness, dynamically adjusting to provide atbCAPTCHA for regular users and enhanced verification for suspicious users.

5. **Identifying potential fraud threats.** Dingxiang's Dinsight Real-Time Risk Control Engine helps enterprises conduct risk assessments, anti-fraud analysis, and real-time monitoring, improving the efficiency and accuracy of risk control. Dinsight's daily risk control strategy processing speed averages under 100 milliseconds, supporting configurable access to and storage of multiple data sources. Based on established indicators, strategies, and model expertise, along with deep learning technology, it offers self-monitoring and iterative risk control mechanisms. Complemented by Xintell, an intelligent modeling platform, Dinsight automatically optimizes security strategies for known risks. It leverages risk control logs and data to uncover potential risks, offering one-click configuration for different scenarios and supporting tailored risk control strategies. The platform standardizes the complex processes of data processing, mining, and machine learning, providing end-to-end modeling services from data preparation to model deployment.

Criminals Use AI Technology to Impersonate "College Best Friend," Woman Scammed Out of 5 Gold Bars

enterprises need to combine technical measures, human resources, management mechanisms, and legal regulations to establish a comprehensive defense system that ensures business security and stability.

To tackle the increasingly complex threats posed by AI, enterprises need to combine technical measures, human resources, management mechanisms, and legal regulations to establish a comprehensive defense system that ensures business security and stability.

# Cutting-edge Anti-fraud Technology

Utilizing advanced technological measures to build a solid defense system is the first step in addressing AI risks.


### Advanced Anti-fraud Tools
. Using technologies such as deep learning, natural language processing, and machine learning, enterprises can monitor and analyze transaction data and behavior patterns in real-time to identify anomalies and potential fraudulent activities. For example, behavioral biometrics can differentiate between legitimate users and cybercriminals by monitoring user behavior traits.

![2024090401.jpeg](https://cdn.aisecurius.com/images/b3d/b3d8baf3-0f9f-4c53-9667-17d4ad081f59.jpeg)

Dingxiang Dinsight's real-time risk control engine helps enterprises conduct risk assessments, anti-fraud analysis, and real-time monitoring, improving the efficiency and accuracy of risk control. Dinsight’s daily risk control strategy processing speed averages under 100 milliseconds, supporting the configurability and accumulation of multi-source data. Based on mature metrics, strategies, and models, along with deep learning technology, it enables self-monitoring and iterative mechanisms for risk control performance. Paired with the Xintell intelligent model platform, it can automatically optimize security strategies for known risks, mining potential risks from risk logs and data, and configuring risk control strategies for different scenarios with a single click. Using associative networks and deep learning, it standardizes complex data processing, mining, and machine learning processes, offering a one-stop modeling service from data processing to model deployment.


### Multi-layered Identity Verification
. Enterprises should adopt multi-factor authentication, biometrics, and Device Fingerprinting technology to enhance the security of user verification. Biometrics combined with device information and behavioral data can provide more precise identity verification, reducing the risk of account hacking or identity theft.

Dingxiang Device Fingerprinting identifies legitimate users and potential fraud by recording and comparing device fingerprints. This technology uniquely identifies each device, detecting virtual machines, proxy servers, emulators, and other maliciously manipulated devices. It analyzes if a device has abnormal behavior, such as multi-account login, frequent IP address changes, or frequent changes in device attributes, helping track and identify fraudulent activities.

Dingxiang atbCAPTCHA quickly and accurately differentiates between humans and machines, precisely identifying fraud behaviors, monitoring, and intercepting anomalies in real-time. atbCAPTCHA, based on AIGC technology, can prevent brute-force attacks, automated attacks, and phishing threats posed by AI. It effectively prevents unauthorized access, account theft, and malicious operations, thereby safeguarding system stability. It integrates 13 verification methods and multiple defense strategies, covering 4,380 risk strategies, 112 types of risk intelligence, across 24 industries and 118 types of risks. Its control accuracy reaches 99.9%, swiftly converting from risk to intelligence. It also supports seamless user authentication without security compromise, with real-time threat handling reduced to under 60 seconds, further improving account login convenience and efficiency, and ensuring the security of user accounts and information.


### Real-time Threat Intelligence Sharing
. Through threat intelligence-sharing platforms across various industries, institutions and enterprises can collaboratively analyze cyber threats and preemptively prevent potential attacks. Meanwhile, continuous AI model optimization and real-time updates enable rapid responses to new risks.


# Professional Anti-fraud Teams


Although technology is at the core of addressing AI risks, it also requires proper human resource allocation and related training. Enterprises need to cultivate and recruit more professionals with skills in AI, cybersecurity, and other areas. Establishing dedicated anti-fraud teams that include security experts, AI engineers, and business risk managers helps build a multidisciplinary collaboration mechanism, ensuring full capability to respond to complex attacks.
![2024090404.jpg](https://cdn.aisecurius.com/images/b8a/b8a8a4aa-cd68-46c9-b4e3-b72caa29021c.jpg)
Additionally, regular internal training is essential to raise overall employee risk awareness, particularly in recognizing AI technologies and cybercrime. Employees, as the first line of defense, play a critical role in identifying suspicious activities, which is key to preventing data breaches and mitigating internal risks.

A note on applying for phone permissions in compliance guidelines for special business scenarios should be added.


# Scientific Security Management Mechanism

The optimization of management mechanisms should be based on the organization’s overall security architecture, ensuring coordination and efficiency between departments. Enterprises should establish comprehensive AI risk assessment systems, regularly evaluating and adjusting potential AI threats. Standardized emergency response processes should be created for possible AI attack scenarios to ensure swift reactions when issues arise.

Business risk management requires close collaboration with departments like legal, compliance, risk management, and operations. A cross-departmental collaboration mechanism ensures that every department can respond quickly to AI risks, forming a cohesive defense.

Enterprises must also strengthen customer data protection measures by establishing strict data access control mechanisms, ensuring that data is encrypted throughout its collection, storage, and use. Furthermore, internal audits and regulatory oversight should be implemented to ensure that data usage complies with relevant legal requirements and privacy standards.


# Legal Safeguards

Effectively addressing AI-related risks requires legal protection. Governments and regulatory bodies play a crucial role in combating AI crimes, and enterprises must ensure their operations comply with regulatory requirements while actively participating in the development of industry standards.

Businesses must adhere to relevant laws and regulations in their respective countries and regions, especially regarding data privacy, AI usage, and crime prevention. Strengthening interaction with regulatory authorities is also essential to ensure that the implementation of new AI technologies does not violate legal regulations.

Given the complexity and rapid evolution of AI applications, companies should actively engage in industry organizations or standardization efforts to promote the standardization of AI technologies in the financial sector. This ensures clear guidelines and regulations during the implementation and application of these technologies.

The cross-border nature of cybercrime requires legal coordination and cooperation between nations. In combating AI-related crimes, enterprises must collaborate closely with law enforcement agencies across different countries to effectively tackle cross-border crimes and money laundering activities.

Building a Comprehensive Defense System to Address AI Risks

. In the past, developing a powerful malware might take months. But with the proliferation of AI tools, attackers can now automatically generate malware in just a few minutes. 

I[n February 2024, the security company Group-IB discovered a malware named "GoldPickaxe," which is the first AI virus software of 2024. ](https://www.aisecurius.com/blog/article/51)This malware tricks users into undergoing facial recognition and submitting identification information. It then uses this information to generate fake videos. These fake facial videos can deceive the biometric systems of banks, allowing attackers to successfully log into the victim's bank accounts. What makes this even more dangerous is that the malware intercepts the bank's SMS verification codes, allowing attackers to perform transfers, make purchases, and even change account passwords.
![2024090404.jpg](https://cdn.aisecurius.com/images/b8a/b8a8a4aa-cd68-46c9-b4e3-b72caa29021c.jpg)
Creating malware is a specialty of AI. In the past, developing a powerful malware might take months. But with the proliferation of AI tools, attackers can now automatically generate malware in just a few minutes. This automated attack tool greatly enhances attack efficiency, and AI's strong understanding of programming languages allows it to quickly discover potential vulnerabilities in software. These automatically generated malware not only increase the speed of attacks but also shorten the reaction time window for defenders. Cybersecurity teams are forced to face more frequent and complex attacks, posing a significant challenge to existing defense mechanisms.

In addition to creating malware, attackers can use AI to conduct in-depth analysis of entire software or system codebases and suggest modifications or replacements. In such cases, attackers can easily implant malicious code, create counterfeit apps, or inject malware into legitimate software. Moreover, AI-generated code often manages to bypass conventional security checks.

AI can also send automated DDoS attacks. Traditional DDoS attacks rely on vast networks of "botnet" devices to initiate attacks, while AI-driven autonomous systems can automatically locate and exploit vulnerable devices, creating even larger botnets. These intelligent systems are not only capable of launching massive DDoS attacks but can also adjust their attack modes in real time based on the target system's response. This means that the attack systems can "learn" the weaknesses in the target's defenses, continuously optimizing their attack strategies to maximize damage.

Moreover, AI can identify potential vulnerabilities in networks and systems. Attackers use AI-based tools to identify vulnerabilities within the IT networks, software, or systems of financial institutions. These tools not only help attackers discover zero-day vulnerabilities but also automatically generate attack scripts targeting specific systems. This technology allows attackers to conduct more precise and targeted attacks, particularly when targeting financial systems, data centers, and other high-value assets.

AI Enables More Rampant Cyber Virus Attacks

As AI becomes more widespread, phishing strategies continue to evolve, and fraud techniques become more complex and covert

Phishing is a classic form of cyber fraud in which attackers often impersonate trusted institutions or individuals to trick victims into revealing sensitive information. Although this type of fraud has been around for decades, it remains one of the most common and effective methods for penetrating companies, stealing data, and conducting financial fraud. Its success lies in the use of social engineering, manipulating human trust, emotions, and impulses to lead the target to make poor decisions.

![2024090202.jpg](https://cdn.aisecurius.com/images/846/846c8a0b-5d43-42f6-82a9-8abce24660b8.jpg)

Phishing attacks are typically carried out via email, fake websites, applications, phone calls, text messages, and social media. These attacks exploit human weaknesses (such as impulsiveness, curiosity, and trust) to lure victims into providing personal information, clicking on malicious links, downloading malware, or transferring funds.

As AI becomes more widespread, phishing strategies continue to evolve, and fraud techniques become more complex and covert.** Fraudsters can use AI to generate realistic and customized phishing content, significantly increasing the deception and success rate of their attacks. According to 2023 data, AI-driven phishing scams have surged by 3,000%, demonstrating the immense threat of this new technology.


# Changes AI Brings to Phishing Attacks


1. **Making Phishing Attacks Easier**: AI dramatically lowers the threshold for phishing attacks, especially in cross-language attacks. Traditional phishing requires attackers to have a certain level of language proficiency, while AIGC can generate high-quality text, voice, and images based on simple prompts and automatically correct spelling and grammatical errors. This means that attackers can generate seamless phishing content in multiple languages, making it easy to target victims across different regions of the world.

2. **Making Phishing Emails More Convincing**: AI can quickly collect, analyze, and integrate publicly available information about an organization or individual, such as social media profiles, news reports, or other public data. Based on this data, AIGC can generate customized “bait files,” making phishing emails more credible and realistic. As AI-generated content is highly intelligent, the typical red flags in phishing emails, such as grammatical errors, spelling issues, or improper formatting, are entirely eliminated, making it difficult for victims to detect.

3. **Diversifying Phishing Methods**: AI can generate not only precise text but also personalized voice and video content. This allows phishing attacks to expand from traditional emails to voice phishing (vishing) and video phishing (deepfakes). Attackers can create simulated phone voices, fake video conferences, or social media interactions, mimicking real voices and images to deceive targets. This diversity in attack methods makes phishing no longer confined to a single text-based channel but more three-dimensional and multi-layered.

4. **Making Phishing Attacks Harder to Detect**: Egress’s 2023 phishing threat trend report shows that in three-quarters of cases, traditional security measures struggle to distinguish AI-generated phishing emails from those written by humans. This is because AI-generated content is highly natural and intelligent, avoiding the typical crude traces found in phishing attacks. Security technologies and systems need further development to meet this challenge, as existing anti-phishing tools are increasingly struggling to detect and defend against such advanced attacks effectively.

5. **Making Phishing Attacks More Targeted**: AI can help attackers create highly personalized attacks by tailoring content based on the target's interests, habits, profession, and social network. By analyzing social networks, public profiles, and other digital footprints, AI can simulate the target's life scenarios, professional environment, and even daily conversation styles to carry out highly precise, targeted attacks. Compared to traditional broad-scope attacks, AI-driven phishing attacks have a significantly higher success rate and destructive power, as their content better aligns with the target's expectations and trust patterns.


#  AI Diversifies Phishing Methods


- **Phishing Email**: Email phishing is the most common form of phishing, often impersonating official emails from well-known financial institutions, government departments, or other legitimate organizations. Attackers typically ask recipients to provide login credentials, bank account information, or click on malicious links to download malware or trojans. The design of the emails often mimics real email templates, even including legitimate companies' logos, contact information, and other visual elements to increase credibility.
![2024090201.png](https://cdn.aisecurius.com/images/1d5/1d59f85c-ab8b-49be-a6ed-6fced40939d4.png)
- **Website Phishing**: Website phishing typically involves creating fake websites almost identical to legitimate ones, with URLs differing by only a single character, making them extremely difficult to distinguish. Users are often tricked into entering personal information, credit card data, or login credentials on these counterfeit websites. Attackers may even use SSL certificates (https) to increase the credibility of their fake websites, deceiving victims into believing they are interacting with a legitimate site.

- **Smishing**: Attackers send fake text messages pretending to be from banks, government agencies, or other trusted organizations, which contain links to malicious websites or induce victims to call scam numbers. Smishing usually employs urgent or tempting content, such as frozen bank accounts or unexpected winnings, to provoke a quick response from users, causing them to overlook careful scrutiny of the link or message.

- **Vishing**: Voice phishing involves attackers directly calling targets or using automated voice messages to scam them. Attackers impersonate bank representatives, law enforcement, or tech support, attempting to obtain sensitive information such as bank account details, passwords, or social security numbers through voice communication. Vishing often emphasizes urgency and authority, trying to pressure victims into disclosing information.

- **Social Media Phishing**: On social media platforms, phishing typically appears as enticing offers, free gifts, or breaking news. Attackers use fake accounts and malicious links to lure users into clicking, leading to the installation of malware or the disclosure of information. Attackers may also use social engineering techniques to impersonate the user's friends or colleagues, further increasing the success rate of the attack.

- **QR Code Phishing**: With the rise of QR codes, malicious QR phishing has become a new attack method. Attackers may distribute malicious QR codes via emails, flyers, or public spaces, luring users to scan and access malicious websites or download malicious applications. Since QR codes do not directly display URLs, users may unknowingly fall into a trap after scanning them.


# How to Prevent AI Phishing Fraud


T[he intelligence bulletin **"DEEPFAKE Threat Research and Security Strategies"** suggests that to prevent and combat AI fraud, ](https://www.aisecurius.com/blog/article/55)it is essential to both effectively detect and identify AI-generated content and to prevent the exploitation and spread of AI fraud. This requires not only technical solutions but also sophisticated psychological strategies and the enhancement of public safety awareness.

1. **Comparison and Identification of Device Information, Geographical Location, and Behavioral Operations**. **Dingxiang Device Fingerprinting** can help identify legitimate users and potential fraudulent behaviors through the recording and comparison of Device Fingerprinting. This technology uniquely identifies and recognizes each device, detecting maliciously controlled devices such as virtual machines, proxy servers, and emulators. It analyzes whether the device shows abnormal behaviors or actions that do not align with the user’s habits, such as logging into multiple accounts, frequently changing IP addresses, or frequently modifying device attributes, thus helping track and identify fraudsters' activities.

2. **Detection of Account Anomalies**. Strengthen frequent authentication in cases of abnormal activities such as account login from different locations, device changes, phone number changes, or sudden activity in dormant accounts. Continuous identity verification during sessions is crucial; persistent checks should be conducted to ensure that the user's identity remains consistent throughout usage. **Dingxiang atbCAPTCHA** can quickly and accurately differentiate between human and machine operators, accurately identifying fraudulent actions and providing real-time monitoring and interception of abnormal behavior.
3. 
![2024062705.png](https://cdn.aisecurius.com/images/e06/e06b5696-4cde-4099-bdfe-b33389a1def8.png)

3. **Prevention of Fake Videos and Images**. **Dingxiang's Full-Chain Panorama Facial Security Threat Perception Solution** uses intelligent verification through multidimensional information such as device environment, facial information, image forgery detection, user behavior, and interaction status. It rapidly identifies more than 30 types of malicious attacks, including injection attacks, liveness forgeries, image forgeries, camera hijacking, debugging risks, memory tampering, rooting/jailbreaking, malicious ROMs, emulators, and other system or operational threats. Upon detecting forged videos, fake facial images, or abnormal interaction behavior, it can automatically block operations. The solution also allows flexible configuration of video verification intensity and user-friendliness, implementing a dynamic mechanism to use **atbCAPTCHA** for regular users and strengthen verification for suspicious users.

4. **Uncovering Potential Fraud Threats**. **Dingxiang Dinsight's Real-Time Risk Control Engine** helps companies assess risks, analyze fraud, and conduct real-time monitoring, improving the efficiency and accuracy of risk management. The average processing speed of Dinsight’s daily risk control strategy is within 100 milliseconds, supporting the integration and storage of multi-party data. Based on mature indicators, strategies, and experience in modeling, coupled with deep learning technology, Dinsight implements a self-monitoring and self-iterating mechanism for risk control performance. Together with **Xintell Intelligent Model Platform**, it enables automatic optimization of security strategies for known risks and supports risk control strategies across different scenarios with one-click configuration. Xintell standardizes complex data processing, mining, and machine learning processes, providing an end-to-end modeling service from data processing, feature derivation, model building, to final model deployment.

When Scammers Start Using AI for Phishing

As the digitalization of the financial services industry accelerates, cybercriminals are increasingly leveraging advanced technologies

As the digitalization of the financial services industry accelerates, cybercriminals are increasingly leveraging advanced technologies, particularly AI, to carry out financial fraud. According to BioCatch's *2024 AI, Fraud, and Financial Crime Survey*, the widespread adoption of digital banking during the pandemic has exposed financial systems to greater risks and provided more opportunities for threat actors. AI technology has not only significantly lowered the technical barriers for committing crimes but has also increased the precision and success rate of fraud.
![2024083001.png](https://cdn.aisecurius.com/images/ece/ece8588f-8d57-47c0-8c96-a84df28dcda3.png)


#  AI’s Contribution to Financial Fraud


The report reveals that over 56% of respondents observed an increase in financial crime activity over the past year. Payment fraud, credit card fraud, and identity theft have emerged as the most common types of financial fraud. Among them, synthetic identity fraud has shown especially rapid growth, being recognized as one of the fastest-growing types of financial crime. This type of fraud involves creating fictitious or composite identity information to apply for credit cards, open new accounts, and engage in borrowing or money laundering activities. Alarmingly, traditional fraud detection models have been largely ineffective at identifying the majority of synthetic identities.

![2024083002.png](https://cdn.aisecurius.com/images/10b/10b0bb2a-9ce0-4222-a27e-fb12ec2cbed1.png)

The report notes that nearly 70% of respondents believe that criminals are becoming more skilled at using AI to perpetrate financial fraud. Through AI-powered voice cloning, fraudsters can mimic the voices of legitimate users to bypass identity verification, circumventing traditional security measures. Consequently, 91% of respondents indicated that their organizations are re-evaluating the use of voice verification technologies.

Furthermore, the use of AI in synthetic identity fraud is becoming increasingly widespread. More than 70% of companies detected synthetic identities during the onboarding of new customers last year, with most of these synthetic identities evading detection by traditional fraud tools. This enables criminals to hide within the financial system under false identities and use sophisticated techniques to avoid detection.


# The Importance of Behavioral Biometrics in Fraud Prevention


In response to the increasingly complex methods of fraud, financial institutions are turning to more advanced defense technologies. Behavioral biometric intelligence has emerged as an effective tool for fraud prevention. This technology analyzes users' online behavior, device information, geolocation, and other factors, combined with AI models, to identify and categorize potential fraudulent activity. Behavioral biometrics can not only capture normal user operation patterns but also detect anomalies, such as unusual application flow smoothness or data handling familiarity—often indicative of fraud.

Financial institutions are leveraging behavioral biometric technology to gain insights that traditional fraud prevention tools cannot provide, allowing them to effectively differentiate between legitimate users and cybercriminals. By applying AI technologies, they can proactively identify threats and protect customers while fostering long-lasting trust.

The report also highlights that financial institutions need to share behavioral biometric intelligence more broadly and work closely with regulatory bodies to effectively combat the wave of financial crime driven by AI. In the future, banks and financial institutions will need to continually enhance their technological capabilities to address the challenges posed by AI and safeguard their customers' assets.

BioCatch Report: AI Becomes a Crucial Technology in Financial Fraud

On Android devices, "screen sharing" is implemented through the MediaProjection service. MediaProjection is a technology available for developers on Android 5.0 and above

At the end of July, the National Financial Regulatory Administration issued a risk alert about new types of telecom network fraud, reminding the public to be wary of scams involving "screen sharing" and "AI face-swapping with voice imitation." The notice urged people to enhance their awareness of risk prevention and identification to protect their finances.

![2024073101.PNG](https://cdn.aisecurius.com/images/ed3/ed375e63-abb3-4789-bb18-ecb6cd58c53d.PNG)

The notice pointed out that in "screen sharing" scams, fraudsters use excuses such as "increasing credit card limits," "canceling bad loans under your name," "terminating insurance services," "flight delay refunds," or "offering gifts" to send texts or make phone calls to individuals. They trick consumers into downloading specific chat or online video conference software and instruct them to enable the "screen sharing" feature. This allows the fraudsters to "monitor" the consumer's phone or computer screen in real-time while "guiding" them through actions such as linking bank cards or changing passwords. In doing so, they gain access to personal banking information, passwords, and verification codes, eventually stealing funds from bank accounts.

In June, the Ministry of Public Security released the top ten telecom network fraud cases, one of which also involved "screen sharing" fraud. In May 2024, a woman named Du in Wuxi, Jiangsu, received a video call at home from someone claiming to be a police officer from the criminal investigation team of the Wuxi Public Security Bureau. The person on the video, dressed in uniform, claimed that Du's bank card was involved in a money laundering crime and that she needed to cooperate with an investigation. Du followed the instructions, downloaded meeting software, and enabled screen sharing to allow the "police officer" to verify the funds in her bank account. The fraudster then told Du that she needed to transfer the funds in her account to a "safe account" to prove her innocence. During the process, the "police officer" also instructed Du to take out a 150,000-yuan loan from the bank and transfer it to the "safe account" to show that her financial transactions were normal. It wasn't until her family intervened that Du realized she had been scammed.

In such cases, fraudsters deceive victims into enabling screen sharing on their phones, leading to the exposure of account passwords. Even without directly asking for information, fraudsters can view everything on the victim's phone, including the characters typed when entering passwords and the verification codes received, allowing them to transfer funds from the victim's bank accounts.


# How Attackers Use "Screen Sharing" to Commit Fraud

One of the scenarios where the "screen sharing" feature is applied involves using the internet to project the screen information of one device in real-time to another device. This includes all content displayed on the screen, such as app screens, SMS pop-up notifications, WeChat messages, other app notifications, account and password entry, unlocking procedures, and other real-time operations. Originally designed for user convenience, this feature has become a tool for fraudsters to profit.

According to an analysis by Dingxiang's Cloud Security Intelligence Center, fraudsters use the following four steps to carry out "screen sharing" scams:
![2024071901.png](https://cdn.aisecurius.com/images/94e/94ec54a1-6b3e-4d4a-8d34-81d4fd333514.png)

### Step 1: Obtaining information and gaining trust.

Fraudsters use illegal channels to acquire victim information and impersonate law enforcement, shopping platform customer service, or staff from student loan cancellation services. They accurately match the victim's situation to gain their trust.


### Step 2: Deceiving victims into enabling "screen sharing."

Using certain apps (e.g., QQ, Tencent Meeting, DingTalk) with built-in screen sharing features, fraudsters can monitor the victim's phone operations in real time.


### Step 3: Logging into online banking and monitoring SMS verification codes in real-time.

Fraudsters first instruct the victim to download an app and enable screen sharing. They then use previously obtained bank card details to log into the victim's online banking and initiate a transfer while monitoring incoming SMS verification codes in real-time.


### Step 4: Stealing funds.

Fraudsters initially trick the victim into making a transfer voluntarily. If the victim realizes the scam or hesitates, the fraudster uses the intercepted verification codes to complete the transaction, transferring the funds without the victim's awareness.


#  Dingxiang Device Fingerprinting Can Prevent "Screen Sharing" Risks


On Android devices, "screen sharing" is implemented through the MediaProjection service. MediaProjection is a technology available for developers on Android 5.0 and above, enabling them to capture or record screens. When a user applies to use the MediaProjection service, Android will pop up a permission request. Only after the user clicks "Confirm" can the screen sharing or recording begin.

![2024071903.png](https://cdn.aisecurius.com/images/7f7/7f74b560-d6c2-4c53-b7b5-21ea50d4fabd.png)

Dingxiang Device Fingerprinting can detect the activation of screen sharing through apps like DingTalk and Tencent Meeting in real-time and immediately issue an alert to warn users about potential risks. When MediaProjection is activated, the system automatically creates a virtual display, and Dingxiang Device Fingerprinting detects this additional screen interface, confirming that the device is in a "screen sharing" state. It then promptly reports this status to the risk control center. The risk control center will issue a warning, reminding the user of potential danger and urging them to stay cautious.

Dingxiang Device Fingerprinting collects and analyzes hardware, software, and behavioral data to uniquely identify and recognize each device. This technology can identify maliciously controlled devices such as virtual machines, proxy servers, and emulators. It analyzes whether a device is showing suspicious behavior, such as multiple account logins, frequent IP address changes, or frequent modifications to device attributes that do not align with typical user habits. By tracking and identifying fraudulent activities, it helps prevent fraud. Dingxiang Device Fingerprinting can also be used as an additional factor for identity verification, enhancing the security of user login and transaction processes. By recording and comparing device fingerprints, it can distinguish between legitimate users and potential fraudsters. It supports Android, iOS, H5, WeChat public accounts, and mini-programs, effectively detecting risks such as emulators, rooted/jailbroken devices, system modifications, and injection attacks. It boasts fast detection, efficient risk identification, over 99% stability, and 100% uniqueness.


# How Users Can Prevent "Screen Sharing" Risks


"Screen sharing" is increasingly being used in various forms of fraud. The final step in these scams typically involves asking the victim to use or download video conferencing software that offers "screen sharing" functionality. The goal is to spy on the victim's device to obtain critical information like bank account passwords.

1. **Do not activate the "screen sharing" feature on Android phones without verification.**
2. **Decline any "screen sharing" invitation from strangers.** Do not use the "screen sharing" function with strangers, as they can see all your actions, including when you enter passwords.
3. **Be extra cautious with private information, especially bank account passwords and verification codes.** Never disclose this information to any stranger.

In short, if you receive a suspicious video or phone call, remain calm. You can use the excuse of a bad signal to hang up, then immediately verify the situation through other means to avoid responding directly to possible scam content.

Dingxiang Device Fingerprinting Protects Against Scams Warned by Financial Regulatory Authority

To effectively address these risks, the "3D Vision Developer Community" has adopted Dingxiang atbCAPTCHA as its identity verification tool for user registration and login.


China's 3D vision technology is currently developing rapidly, especially in applications such as artificial intelligence, autonomous driving, industrial robotics, virtual reality (VR), augmented reality (AR), medical imaging, and consumer electronics. With advancements in deep learning, computer vision, and sensing technologies, 3D vision is gradually becoming a core technology in these industries. The number of professionals in the 3D vision field in China has increased significantly, with the number of technical personnel in related fields such as AI, computer vision, and 3D modeling exceeding hundreds of thousands. The specific number is expected to continue to grow as the industry expands. In particular, interdisciplinary talents in deep learning and computer vision have become scarce resources in the market.

![2024082301.png](https://cdn.aisecurius.com/images/b07/b073dd98-ab92-435a-8a18-5987d8e62c5f.png)

The "3D Vision Developer Community" is an open platform launched by Shenzhen Orbbec Technology Co., Ltd., targeting developers from enterprises, universities, and individual tech groups. It aims to accelerate the commercialization of 3D vision technology in smart security, smart home, intelligent retail, commercial display advertising, smart logistics, smart healthcare, and other fields. It also provides systematic learning opportunities for students in related fields, offering a platform for technical exchange with industry experts. This initiative contributes to cultivating a talent pool for China's 3D vision industry.

The "3D Vision Developer Community" includes features from mainstream developer communities such as Odroid, Khadas, Raspberry Pi, NVIDIA, and RealSense. It provides technical documentation, standard SDK downloads, technical forums, industry insights, product and technical training, and other services, offering professional technical support to developers. However, the traditional user registration and login systems in developer communities face security challenges such as weak passwords, phishing, and brute-force attacks. Additionally, fake registrations and account theft not only negatively impact daily operations, user experience, and resource management but also severely damage the community's credibility and reputation, leading to increased legal and security risks. Therefore, community managers need to adopt robust protective measures to prevent and address these issues.

To effectively address these risks, the "3D Vision Developer Community" has adopted Dingxiang atbCAPTCHA as its identity verification tool for user registration and login.

![2024082201.png](https://cdn.aisecurius.com/images/fa3/fa3d5afe-9b4b-41bf-95a7-b974e051d13d.png)

Dingxiang atbCAPTCHA, based on AIGC technology, can prevent threats such as AI-driven brute force attacks, automated attacks, and phishing. It effectively prevents unauthorized access, account theft, and malicious operations, thereby ensuring system stability. It integrates 13 types of verification methods and various protection strategies, gathering 4,380 risk strategies, 112 types of risk intelligence, covering 24 industries and 118 types of risk categories. Its defense accuracy reaches 99.9%, and it can quickly convert risks into actionable intelligence. Additionally, it supports seamless user authentication without user awareness and reduces the real-time response time to within 60 seconds, further enhancing the convenience and efficiency of account login services while effectively safeguarding user accounts and website information security.

Dingxiang atbCAPTCHA Empowers 3D Vision Development Community Security

AISecureus technical experts shared insights on the current prevalent forms of cyber fraud, cutting-edge anti-fraud products, and illustrated the practical effects of anti-fraud measures through

Recently, AWS "Security Day" was held in Hong Kong. Industry experts from AWS, financial institutions, and security companies discussed the role of cybersecurity in the era of AI and its multifaceted impact on the security landscape. AISecureus technical experts shared insights on the current prevalent forms of cyber fraud, cutting-edge anti-fraud products, and illustrated the practical effects of anti-fraud measures through two case studies.

![2024081904.jpg](https://cdn.aisecurius.com/images/b85/b8539dfa-8bd8-426a-8aa5-ee828c438cd8.jpg)

# Four Common Forms of Cyber Fraud


Device fraud, application fraud, account fraud, and marketing fraud are various types of cybercrime in the digital age where fraudsters exploit technology. These activities not only infringe upon users' property and privacy but also distort the fair competition environment in the market. They present significant challenges to enterprises' security defenses and resource allocation, necessitating an increase in security awareness among users and enhanced technical defenses by enterprises to build a safer network environment.

1. **Device Fraud:** Fraudsters use emulators to forge device properties to register a large number of fake accounts. These fake accounts are used for "bonus hunting" and "fake transactions." Through an automated tool known as a "device farm," fraudsters can easily manipulate these accounts in bulk, increasing the concealment and scale of the fraud. Device fraud not only undermines the fairness of corporate promotional activities but also leads to resource wastage. More seriously, this behavior may result in the depletion of resources for real users and even the leakage of sensitive user information.

2. **Application Fraud:** Application fraud involves fraudsters hacking, tampering with, or forging apps or embedding malicious code, and then relaunching these compromised applications. These malicious apps can steal users' accounts, funds, and other sensitive information. Application fraud poses a severe threat to users' property security and may lead to the leakage of users' privacy. As more users rely on mobile applications for daily operations, the harm of this fraud method becomes increasingly significant.

3. **Account Fraud:** Account fraud typically involves cracking account passwords, spoofing logins, and gaining access to victims' accounts to defraud their acquaintances, colleagues, or friends. This type of fraud often utilizes social engineering techniques to deceive the victim into trusting the fraudster, thereby achieving the fraudulent goal. Account fraud can not only lead to direct financial losses for the victim but also result in severe privacy breaches, potentially affecting the victim's personal and professional life.

4. **Marketing Fraud:** Marketing fraud refers to fraudsters using professional tools to register fake accounts, thereby snatching marketing benefits, occupying user resources, and fabricating promotion effects. This not only prevents companies' marketing budgets from generating actual results but also leads to decision-making biases within the company. Such fraudulent behavior not only wastes corporate resources but may also impact the fairness of market competition, leading to the formation of an unhealthy competitive environment.
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# Three Major Anti-Fraud Products


Effectively combating these fraudulent activities requires joint efforts from users and enterprises to raise security awareness and adopt advanced technical measures to prevent and respond to potential risks. Only by doing so can we ensure the safety of users' funds and information in an increasingly complex online environment. AISecureus technical experts provided a detailed introduction to anti-fraud products such as App hardening, CAPTCHA, and Device Fingerprinting.

1. **Identifying Device Risks:** AISecureus Device Fingerprinting works by integrating information from multiple devices, generating a unified and unique Device Fingerprint for each device. A multidimensional identification strategy model based on device, environment, and behavior is established to identify risk devices such as those under the control of virtual machines, proxy servers, or emulators. It analyzes whether devices exhibit abnormal or non-user-like behaviors, such as multiple account logins, frequent IP address changes, or frequent changes in device attributes. This helps trace and identify fraudulent activities, enabling companies to operate under the same ID across all channels and assist in cross-channel risk identification and management.

2. **Ensuring Application Security:** AISecureus's adaptive App hardening, based on Graph Neural Network technology, deeply analyzes and extracts code features, automatically selecting appropriate methods for obfuscation according to the characteristics of different code blocks. This significantly increases the difficulty of reverse engineering while reducing computational performance consumption by 50%. Through its encryption obfuscation engine, the app code is encrypted, obfuscated, and compressed, greatly enhancing the security of the app code and effectively preventing products from being cracked, copied, or repackaged by attackers.

3. **Protecting Account Security and Preventing Malicious Registrations and Logins:** AISecureus atbCAPTCHA, based on AIGC technology, can prevent threats such as AI brute force cracking, automated attacks, and phishing attacks, effectively preventing unauthorized access, account theft, and malicious operations, thereby protecting system stability. It integrates 13 verification methods and various defense strategies, with a collection of 4,380 risk strategies, 112 categories of risk intelligence, covering 24 industries and 118 types of risks. Its defense accuracy reaches 99.9%, and it can quickly transform from risk to intelligence. It also supports seamless user pass-through security, reducing the real-time response and processing time to within 60 seconds, further enhancing the convenience and efficiency of digital login services.


![2024081903.jpg](https://cdn.aisecurius.com/images/f8b/f8b80fb6-4009-4c72-a8db-d860a7885aec.jpg)

# Two Client Case Studies


AISecureus technical experts delved into the practical applications of their technology in the financial sector and the aviation industry, showcasing their outstanding capabilities in addressing security threats.

In one case, a well-known mobile banking application experienced a severe vulnerability attack, where attackers attempted to exploit multiple vulnerabilities across the client, H5 pages, and web end. Notably, through port vulnerabilities, attackers aimed to conduct mass registration, mass login, and mass order grabbing, posing significant security risks to the banking system. To address this challenge, the bank deployed the AISecureus App hardening solution. This solution provided comprehensive encryption protection for the client, stored data, keys, and data transmission through a powerful security SDK. The results showed that after deployment, all attempted intrusions and cracking activities were successfully blocked, ensuring the app's security while maintaining efficient operation.

Another case involved a large airline, whose ticketing system once faced massive malicious bot attacks, leading to the malicious occupation of system resources. Data showed that as much as 90% of search requests were from malicious bots, severely impacting the search experience for regular users. After deploying the AISecureus solution, high-risk search requests quickly dropped from the initial 5% to just 0.008%, medium-risk search requests decreased significantly from 90% to 24.5%, and normal search requests increased from 5% to 75%. These figures not only demonstrated AISecureus's efficiency in countering malicious attacks but also significantly improved user experience and system performance.

This "Security Day" event was the first of its kind held by AWS in Hong Kong in 2024, with the core concept of sharing intelligence. It aimed to provide participants with fundamental knowledge of cloud computing security and share various new technologies to help enterprises enhance security awareness.

AWS Security Day: AISecureus Experts Detail Three Major Anti-Fraud Products

The development of AI voice cloning technology has revolutionized fields such as voice synthesis and personalized services. However


The development of AI voice cloning technology has revolutionized fields such as voice synthesis and personalized services. However, the dual-edged nature of this technology has also led to a rise in fraud activities based on AI voice cloning, with increasingly varied and rampant methods.


# How AI Voice Cloning Fraud Works


1. **Voice Collection:** Fraudsters collect voice samples of their target from social media videos, public speeches, or intercepted phone calls. These samples include voice fragments, tone, and speaking style, among other detailed information.

2. **AI Learning:** Fraudsters use these voice samples to train AI algorithms, enabling the AI to mimic the target’s voice patterns, tone, and characteristics, and even generate new phrases or sentences that the target has never spoken.

3. **Voice Cloning:** Once trained, the AI can generate highly realistic audio that sounds exactly like the target. These cloned voices can then be used to impersonate and deceive victims.

![2024080801.png](https://cdn.aisecurius.com/images/d13/d13be02a-8054-4a98-8d45-78065bedb962.png)


# How Fraudsters Use Cloned Voices


1. **Impersonating Companies or Individuals:** Fraudsters might call or leave voice messages pretending to be trusted entities, such as banks, companies, friends, or family members, to trick victims into sharing personal information or transferring money.

2. **Manipulating Others:** Fraudsters might impersonate the target to manipulate others into taking specific actions, such as transferring funds or disclosing sensitive information.

3. **Conducting Fake Transactions:** Fraudsters may impersonate the target to place orders or conduct transactions over the phone, thereby carrying out fraudulent activities.


#  How to Prevent AI Voice Cloning Scams


As a novel form of fraud, voice cloning poses a serious threat to individual information security. By understanding how these scams operate and implementing effective prevention strategies, we can significantly reduce the risk of falling victim to such scams. The public should enhance their security awareness, remain vigilant, and actively learn prevention techniques to build a safer, healthier online environment. The Dingxiang Defense Cloud Business Security Intelligence Center suggests that individuals can defend themselves against AI voice cloning fraud in the following ways:

1. **Recognize Warning Signs:** When receiving a suspicious video or phone call, maintain a skeptical attitude. Make an excuse about poor signal to hang up and immediately confirm the call by other means to avoid responding directly to potential scam content.

2. **Be Wary of Urgent Requests:** Fraudsters often create a sense of urgency, pressuring victims to act quickly and inducing panic, forcing them to make hasty decisions without thinking. If the caller makes unexpected requests, especially for personal information or financial assistance, remain skeptical. Be cautious of high-pressure tactics and avoid disclosing sensitive information or making immediate payments. Legitimate organizations typically do not demand urgent actions over the phone.

3. **Protect Personal Information:** Never disclose sensitive information over the phone, such as bank account details, ID numbers, or passwords, especially in unsolicited or suspicious calls.

4. **Set Up a "Safe Word":** Establish a "safe word" or "challenge question" known only to close friends, family, and colleagues, to verify the caller’s identity when receiving suspicious calls or messages. If the caller cannot provide the correct "safe word" or evades the question, hang up immediately and confirm through a known, secure contact method.

5. **Reduce Sharing of Sensitive Information:** Avoid sharing personal photos, voice recordings, and videos on social media to reduce the risk of identity forgery. Be cautious about publicly revealing personal, family, and work-related information. If you encounter "deepfake" content, report it to social media administrators and law enforcement immediately, take steps to remove it, and trace its source.

6. **Enhance Cybersecurity Awareness:** Consider using reliable software to protect your devices from malicious activities. Stay informed about the latest voice fraud and cybersecurity threats. Regularly check credible sources for updates on common scam calls and emerging technologies to boost your security awareness.

7. **Report to Authorities Promptly:** If you realize you’ve become a victim of fraud, preserve all related evidence immediately and report it to the police by dialing 110, so they can intervene in a timely manner.

Additionally, platforms should analyze user behavior patterns and identity information to establish security warning mechanisms, monitoring and restricting suspicious activities such as abnormal logins and high-frequency message sending. By analyzing user behavior, such as mouse movement patterns and typing styles, platforms can identify anomalies and flag activities that deviate from normal usage as suspicious. Extra identity and device verification, along with using large models to quickly sift through massive data and detect subtle inconsistencies usually missed by humans, can help identify fraudulent actions by attackers.


 Seven Crucial Measures to Prevent AI Voice Cloning Fraud

During the peak summer travel season, free tickets to popular museums such as the National Museum of China and the Palace Museum have become a resource out of reach for many tourists, 

During the peak summer travel season, free tickets to popular museums such as the National Museum of China and the Palace Museum have become a resource out of reach for many tourists, as scalpers use illegal means to hoard these tickets and resell them at high prices.
![2024081302.png](https://cdn.aisecurius.com/images/a86/a86d502d-cc6d-4b34-b8f7-f63adbd491f1.png)
According to China Central Television, in early August, reporters repeatedly tried to book tickets through the National Museum's mini-program but found that all reservations within the week were fully booked, making it impossible to visit the museum. However, large numbers of tourists were still able to enter these popular attractions each day. Where did their tickets come from? In a random interview with 30 tourists outside the National Museum, it was found that only 2 had successfully booked tickets through official channels, while the remaining 28 obtained their tickets through scalpers at inflated prices.

Reporters then searched for the "National Museum" on several online platforms and found that most discussions were related to ticketing. Scalping was prevalent on many platforms, with some even offering "guided tour" services as a cover for ticket reselling. These services were simply a front; the real purpose was to use technical means to hoard tickets and resell them at high prices.

![2024081303.png](https://cdn.aisecurius.com/images/0a3/0a3c0b96-66cd-414e-8089-7f58aaabff44.png)

Statistics show that not only the National Museum, but also the Palace Museum, Military Museum, and other popular museums, as well as prestigious universities like Peking University and Tsinghua University, have seen this phenomenon.

The Haidian District People's Procuratorate in Beijing handled a scalping case that revealed the underlying tactics. In the case, three individuals—Li, Wang, and Zhang—used ticket-snatching software to secure reservation tickets for well-known tourist attractions nationwide, then resold them at a markup of 80 to 150 yuan. In less than a month during the 2023 summer vacation, they made over 300,000 yuan in illegal profits.

![2024081304.png](https://cdn.aisecurius.com/images/8d2/8d29a6b1-25a7-432f-afda-4943b5bf7624.png)

It was found that the suspects preloaded tourists' names, ID numbers, and other information into the software and used the built-in SMS code platform to receive verification codes in advance. While normal reservations required tourists to enter this information, taking up valuable time, the suspects could receive the codes two minutes earlier, with a validity of up to ten minutes, significantly increasing their chances of securing tickets. Once tickets were released by officials, these scalpers could lock in a large number of tickets within seconds with just one click.


# An In-Depth Investigation into Museum Ticket Scalping


In July 2023, the 19th issue of "Dingxiang Defense Cloud Business Security Intelligence" investigated why tourists were unable to secure museum tickets during the summer vacation. The investigation found that dozens of popular museums nationwide, including the National Museum of China, Nanjing Museum, Hunan Provincial Museum, and Shaanxi History Museum, particularly the trendy ones, had been almost entirely infiltrated by ticket scalpers.

Dingxiang Defense Cloud Business Security Intelligence Center tracked and analyzed that scalpers resell free tickets mainly through three methods: real-name reservation with a surcharge, resale of tickets at higher prices, and selling guided tour packages. The first and third methods were the most common.

**First Method: Reservation with a Surcharge**  
This requires the buyer to provide their real name, ID number, and other personal information. Ticket prices vary depending on the date, with weekdays being relatively cheaper and weekends or holidays more expensive.

**Second Method: Resale of Tickets at Higher Prices**  
Scalpers holding multiple ID cards book tickets under one person’s name and have another person (the tourist) use that ID to enter, repeating the process.

**Third Method: Selling Unauthorized Tour Packages**  
Some travel agency employees and private guides collaborate to register multiple museum accounts using different phone numbers, reserving tickets for various tourists and charging a service fee ranging from 10 to several tens of yuan per ticket. There are also "electronic guide + ticket reservation" or "ticket package" services, with packages including tickets, guide fees, and travel expenses.

![2024081305.png](https://cdn.aisecurius.com/images/970/9705e910-8ad3-4eac-89ee-4fcaa8bd7260.png)

The scalping phenomenon not only affects tourists' visiting experiences but also undermines the fairness of public resources. In these cases, relying solely on the management measures of museums or attractions is far from sufficient. A comprehensive approach involving legal, technical, and market measures is needed to improve regulatory effectiveness and curb the spread of scalping.


# Why Scalpers Can Secure Tickets


Dingxiang Defense Cloud Business Security Intelligence Center found that scalpers often use cheating tools like plug-in software and script programs to snatch tickets.

Ticket grabbing is a race against time. If person A is 1 second faster than person B, person A will be able to purchase the ticket, while person B will miss out. When it comes to placing orders, humans rely on nerve responses, while software operates based on preset processes. Therefore, the running speed of the software far exceeds that of humans, and the success rate of scalping is much higher than that of ordinary tourists.

Although many museums try to regulate scalpers through ticketing rules and technical means—such as blocking frequent reservations based on phone numbers and IP addresses—scalpers continuously update their cheating tools. These scalper-developed ticket-snatching tools can register, log in, and grab tickets in bulk, allowing them to quickly and instantly hoard a large number of tickets. They only need to fill in the identity information, set the quantity, and configure the running time to complete the automatic purchase.


# Preventing Scalpers from Using Software to Snatch Tickets


To prevent scalpers from snatching tickets, simply restricting IPs is of little significance and may accidentally block legitimate tourist reservations. Dingxiang Defense Cloud Business Security Intelligence Center suggests that in addition to restricting IPs and accounts, technical measures should be enhanced to prevent scalpers' cheating software, thereby effectively curbing scalping.


### Ticketing Rules Restrictions


1. **Increase Document Usage Limits**: Each ID number can only be used to make one reservation; each account can reserve tickets for up to 5 people.

2. **Increase Account Usage Limits**: Some museums require that if an account frequently makes reservations within a week and the no-show rate exceeds 50%, the account (phone number) will be restricted from making reservations for 30 days. Some museums also require that accounts that cancel reservations 3 times within 7 days or 5 times within 30 days, or do not enter the venue after booking, will be placed in a "blacklist" for 30 days, prohibiting further reservations, to prevent scalpers from repeatedly using identity information.

3. **Increase Ticket Release Times and Channels**: Releasing tickets irregularly and increasing manual random ticket releases can also reduce the chance of technical ticket snatching to some extent.


### Technical Measures to Prevent Scalping


1. **Detect and Identify Abnormal Ticket Snatching Devices**: Identify whether the client's Device Fingerprinting is legitimate and whether there are risks such as injection, hooks, or emulators. Identify whether the client's Device Fingerprinting is legitimate, quickly identify risks like flashing or modifying devices, rooting, jailbreaking, hijacking injections, etc. Quickly detect multiple activations from the same device, abnormal behavior from the same device associated with an IP, a large number of gatherings from the same IP in a short time, an abnormal proportion of old device models in the same channel, and an abnormal proportion of old operating systems in the same channel.

Dingxiang Device Fingerprinting can identify devices under malicious control such as virtual machines, proxy servers, and emulators, analyze whether the device exhibits abnormal or non-user-friendly behavior like multi-account logins, frequent IP address changes, and frequent changes in device attributes, helping to track and identify fraudulent activities. This allows for early detection of fraudulent behavior, preventing ticket snatching incidents. It can also serve as an additional factor in identity verification, enhancing security during user logins and transactions. By recording and comparing Device Fingerprinting, legitimate users can be distinguished from potential fraudulent activities.

2. **Detect and Block Abnormal Ticket Snatching Accounts**: Use behavior-based strategies to control accounts that switch between multiple accounts on the same device to place orders.

Dingxiang atbCAPTCHA can verify, judge, and block malicious accounts and malicious scraping behaviors in real time at key stages such as registration, login, and query. It is based on AIGC technology and can prevent threats like AI brute force cracking, automated attacks, and phishing attacks, effectively preventing unauthorized access and intercepting web crawlers from stealing data. It integrates 13 verification methods and various defense strategies, supporting seamless security for legitimate users, with real-time response capabilities reduced to within 60 seconds, further improving the convenience and efficiency of the login service experience.

3. **Enhance Risk Identification and Prevention Capabilities**: Establish a dynamic local list management mechanism based on registration data, login data, and activation data, and maintain corresponding black and white list data, including user IDs, phone numbers, devices, etc. After accumulating a certain amount of online data, use risk control data and business sediment data to model registration, login, order, and snatching behaviors, with the model output directly used in risk control strategies.

Dingxiang Dinsight Real-Time Risk Control Engine helps companies with risk assessment, anti-fraud analysis, and real-time monitoring, improving the efficiency and accuracy of risk control. The Xintell Intelligent Model Platform paired with Dinsight can automatically optimize security strategies for known risks, identify potential risks based on risk control logs and data mining, and configure risk control strategies for different scenarios with one click.


China Central Television: Scalpers Profiteering from Popular Museum Free Tickets

Recently, the National Financial Regulatory Administration issued a risk warning against new types of telecom network fraud, urging the public to remain vigilant against evolving scams

Recently, the National Financial Regulatory Administration issued a risk warning against new types of telecom network fraud, urging the public to remain vigilant against evolving scams, enhance risk awareness, and develop the ability to identify threats, in order to safeguard their finances. The National Financial Regulatory Administration identified several new types of telecom network fraud, including “screen sharing” scams, “DEEPFAKE” scams, fraudulent online investment schemes, and fake transactions involving online gaming products.

![2024073101.PNG](https://cdn.aisecurius.com/images/ed3/ed375e63-abb3-4789-bb18-ecb6cd58c53d.PNG)

In “DEEPFAKE” fraud, criminals use pretexts such as “online shop customer service,” “marketing promotion,” “recruitment for part-time jobs,” or “dating and relationships” to contact consumers through WeChat, phone calls, and other means to collect voice, speech, or facial information. They then use “face swapping” and “voice imitation” technologies to create fake audio, video, or images that simulate the voice or appearance of others, thereby gaining the victim's trust. Under the guise of borrowing money, investment opportunities, or emergency assistance, they persuade the victim’s relatives or friends to transfer money or provide sensitive information such as bank account passwords, which the criminals then quickly exploit to transfer funds. Additionally, criminals might use deepfake technology to artificially create audio or video content of celebrities, experts, or law enforcement personnel, and spread false information to facilitate fraud.

The police have previously disclosed several cases of telecom network fraud involving DEEPFAKE technology. Recently, in Ordos, Inner Mongolia, a resident named Ms. Li fell victim to a new type of telecom network scam using DEEPFAKE technology. The scammer impersonated Ms. Li’s old classmate “Jia” and established contact with her through WeChat and QQ platforms, using AI technology to fake a video call, thereby gaining her trust. The scammer then requested Ms. Li to transfer money, citing a need for financial assistance, and even sent a fake bank transfer record. Without verifying the recipient, Ms. Li transferred 400,000 yuan to the scammer’s account. Additionally, Mr. Li from Hebei also became a victim of DEEPFAKE telecom fraud, where scammers used a dating app to lure him into a nude chat and download an illegal chat software. They then used AI technology to create a fake pornographic video and extorted 120,000 yuan from him.


# Fraud Using DEEPFAKE Technology


Dingxiang Defense Cloud Business Intelligence Center published a special report titled “DEEPFAKE Threat Research and Security Strategies,” which systematically introduced the industrial chain behind new types of telecom network fraud based on DEEPFAKE technology. New telecom network frauds operate through a complete industrial chain, where the upstream illegally obtains citizens' private information, the midstream customizes scripts to carry out fraud, and the downstream converts fraud into money.

**Stage One: Information Gathering.** The first step of the scam is collecting personal information about the target. Scammers obtain sensitive information such as photos of the victim’s face and phone number through black market transactions, illegal database breaches, or organized cyber-attacks. This information forms the foundation for the scammers’ next move.

**Stage Two: Fake Video Synthesis.** After obtaining personal information, DEEPFAKE technology captures the victim's facial features through software and combines them with AI synthesis techniques to create fake video content.

**Stage Three: Identity Disguise and Communication.** The criminals begin adding the victim’s contact information. They mimic the target’s tone and style, communicating with the victim through text, voice, and video, gradually lowering the victim's guard and gaining their trust.

**Stage Four: Inducing Money Transfers, Loans, and Extortion.** Once the victim fully believes in the scammer's identity, the criminal issues orders for money transfers, loan payments, or goods purchases. They might fabricate urgent situations or business needs to induce the victim to transfer funds to a designated bank account. Under the combined pressure of urgency and trust, the victim often follows the scammer's instructions without further verification.


# Technical Measures to Prevent New Types of Telecom Network Fraud


To prevent “DEEPFAKE” telecom network fraud, it is essential to verify any suspicious activity online by confirming it offline, increasing the duration of communication, and employing probing tactics such as asking the other party to perform specific actions that may reveal inconsistencies. Additionally, companies are advised to adopt multiple technical measures and methods. Furthermore, promoting the positive application of AI technology and cracking down on criminal activities are fundamental solutions.

1. **Identifying DEEPFAKE Fraud Videos.** During video chats, you can ask the other party to press their nose or face to observe changes in facial features. If it's a real person, pressing the nose will cause it to deform. You can also ask them to eat or drink and watch for facial changes. Alternatively, ask them to perform strange actions or facial expressions, such as waving or making a difficult gesture, to distinguish between real and fake. Waving can disrupt facial data, causing some shaking, flickering, or other anomalies.

2. **Comparing and Identifying Device Information, Geographic Location, and Behavioral Operations.** Dingxiang Device Fingerprinting can identify legitimate users and potential fraudulent activities by recording and comparing device fingerprints. This technology assigns a unique identifier to each device, helping detect maliciously controlled devices like virtual machines, proxy servers, and emulators. It also analyzes whether the device exhibits unusual behaviors, such as logging into multiple accounts, frequently changing IP addresses, or altering device properties, which may indicate fraud.

3. **Strengthening Account Identification.** Activities such as remote logins, device changes, phone number changes, and sudden activity from dormant accounts require more frequent verification. Additionally, continuous identity verification during sessions is crucial to maintain consistent user identity throughout usage. Dingxiang atbCAPTCHA can quickly and accurately distinguish between human and machine operators, precisely identify fraudulent activities, and monitor and intercept abnormal behaviors in real-time.

4. **Preventing DEEPFAKE Fake Videos and Images.** Dingxiang's full-chain panoramic facial security threat perception solution performs intelligent verification using multidimensional information, including device environment, facial information, image forgery detection, user behavior, and interaction status. This solution can quickly identify and block more than 30 types of malicious activities such as injection attacks, live body forgeries, image forgery, camera hijacking, debugging risks, memory tampering, Root/jailbreak, malicious ROM, and emulator operations. Upon detecting forged videos, fake facial images, or abnormal interactions, it can automatically block the operations. Additionally, the solution allows for flexible configuration of video verification strength and user-friendliness, implementing a dynamic mechanism that applies atbCAPTCHA to normal users while strengthening verification for abnormal users.

5. **Uncovering Potential Fraud Threats.** Dingxiang Dinsight real-time risk control engine helps companies with risk assessment, anti-fraud analysis, and real-time monitoring, improving the efficiency and accuracy of risk control. Dinsight’s average processing speed for daily risk control strategies is within 100 milliseconds and supports the configurable access and accumulation of multi-source data. It leverages mature indicators, strategies, model experience, and deep learning technology to achieve self-performance monitoring and iterative risk control. Paired with the Xintell intelligent model platform, Dinsight can automatically optimize security strategies for known risks, mine potential risks from risk control logs and data, and configure risk control strategies for different scenarios with one click. The platform standardizes complex processes such as data processing, mining, and machine learning, providing an end-to-end modeling service from data processing and feature derivation to model construction and deployment.


Financial Regulatory Authority: Alert on “DEEPFAKE” Fraud

As a trillion-dollar industry, used car trading platforms offer consumers diversity and convenience, with a wide selection of used cars and streamlined transaction services,


In recent years, the rapid growth of China's automotive market has fueled the expansion of the used car sector, driving new consumer momentum at an unprecedented pace. According to data from the China Automobile Dealers Association, in the first half of 2024, cumulative used car transactions reached 9.3829 million units, a year-on-year increase of 7.01%, with a total transaction value of 625.212 billion yuan.

![2024080901.jpg](https://cdn.aisecurius.com/images/ac0/ac04dc16-b1ee-4270-a5df-6cf875067a0b.jpg)

As a trillion-dollar industry, used car trading platforms offer consumers diversity and convenience, with a wide selection of used cars and streamlined transaction services, significantly simplifying the car-buying process and improving efficiency.

Che300 is an independent third-party platform for automotive transaction and financial big data services, providing one-stop services for key data in the automotive transaction and financial industry chain. It offers online information query services such as vehicle price evaluation, vehicle maintenance records, model identification, and vehicle value retention analysis for car buyers and sellers.

Che300 enhances the standardization of vehicle model information, vehicle condition information, and vehicle pricing, providing foundational database services for auto dealers and trading platforms. The platform's internet-based comprehensive data search and analysis capabilities reduce potential profit risks in used car transactions. Additionally, it offers vehicle valuation through extensive historical transaction data and provides market analysis for target models. Che300 also supports banks, insurance companies, leasing firms, auto manufacturers, and automotive finance companies with fraud detection, thorough screening, and continuous monitoring functions.
![2024080501.png](https://cdn.aisecurius.com/images/648/648cc32e-f5d5-471f-a89d-5405a78f4b20.png)
Data is a crucial digital asset. To ensure user account security and prevent malicious bot attacks, Che300 has chosen Dingxiang atbCAPTCHA. This tool effectively blocks malicious bots from stealing data and can perform real-time verification, assessment, and interception of malicious accounts and data scraping behaviors during critical stages such as registration, login, and queries.

Dingxiang atbCAPTCHA, based on AIGC technology, prevents AI-driven brute force attacks, automated attacks, and phishing threats, effectively protecting against unauthorized access and intercepting web crawlers. It integrates 13 verification methods and multiple defense strategies, supporting seamless user verification while reducing real-time response capability to within 60 seconds, thereby enhancing the convenience and efficiency of the login experience.

Moreover, Dingxiang atbCAPTCHA can effectively prevent these automated tools from malicious program or personnel mass registrations and logins. Malicious programs or individuals often use brute force attempts to log into website accounts, but Dingxiang atbCAPTCHA significantly increases the difficulty of such attacks, preventing unauthorized logins and safeguarding users' privacy and financial security.

Dingxiang atbCAPTCHA Enhances Data Security in the Used Car Market

accounting for 25.90% and 18.50%, respectively. Additionally, "impersonating leaders," "guess who I am," and cryptocurrency fraud also represented a significant proportion.

Li's QQ account was added to a work group by a scammer. Seeing that the names in the group were those of his company's staff, he did not verify further. Days later, Li received a group message: the scammer, pretending to be the "general manager," claimed that a payment to a contractor was needed and asked Li to check the company's account balance. After verifying the funds, the "general manager" asked Li to transfer all the money to a specified account, urging him to hurry due to the urgency. Fearing to offend the "leader," Li transferred 400,000 yuan from the company account. It was only after the real general manager received a bank notification that Li realized he had been scammed.

![2024072401.png](https://cdn.aisecurius.com/images/040/040acdda-1de2-4c3c-a7aa-10f3413c0a4d.png)

Recently, the Ministry of Public Security announced the top ten types of telecom network fraud, with "impersonating leaders and acquaintances" standing out. Scammers use photos and names of victims' leaders or acquaintances to create fake social media accounts, add victims as friends, or include them in chat groups. They then impersonate leaders or acquaintances to gain trust by showing concern and giving commands in a familiar tone. Claiming to have transferred funds to the victim’s account and asking for a re-transfer to another account, they also send fake screenshots of successful transfers. Trusting the "leader" or "acquaintance," victims often do not verify the identity and believe the funds were transferred to their account. The scammers then push for a quick re-transfer, thus defrauding the victims. This type of fraud exploits the victim’s trust in familiar individuals, neglecting identity verification.


#  Criminals Using AI to Impersonate Leaders


Analysis by Dingxiang Defense Cloud Business Security Intelligence Center shows that from 2015 to 2023, impersonating online shopping customer service and rebate fraud were the most common types of telecom network fraud, accounting for 25.90% and 18.50%, respectively. Additionally, "impersonating leaders," "guess who I am," and cryptocurrency fraud also represented a significant proportion.

![2024072301.png](https://cdn.aisecurius.com/images/212/2122dc68-d940-4be8-b5a0-7f64bbb97dc3.png)

With the advancement of AI technology, high-tech fraud methods like voice cloning have become rampant. "Impersonating leaders and acquaintances" frauds have become more deceptive. Criminals use voice cloning technology and AI algorithms to mimic specific individuals' voices, creating realistic audio that sounds like genuine conversations. This technology typically employs text-to-speech conversion, turning written text into voice output. Scammers combine synthesized voices and fake video calls to further enhance the credibility of their scams. These new telecom fraud methods are more concealed and challenging to prevent.

 
# Four Steps of AI Voice Cloning Fraud


Dingxiang Defense Cloud Business Security Intelligence Center's analysis reveals the four stages scammers use to conduct voice cloning fraud. Each stage is a meticulously designed trap, and if victims cannot effectively discern, they may fall step by step into the scammers' trap.

#### Stage One: Information Gathering

The first step in fraud is gathering personal information about the target. Scammers obtain sensitive information like facial photos, phone numbers, and bank card numbers through black market transactions, illegal database leaks, or organized cyber attacks. This information forms the basis for the scammers' next actions.

#### Stage Two: Voice Synthesis

With personal information in hand, scammers use advanced voice synthesis technology and deep learning algorithms to create realistic voice samples. These samples not only mimic the target's tone and inflection but also replicate their language habits and expressions, making the voice sound indistinguishable from the real person and greatly enhancing the deception.

#### Stage Three: Identity Masquerade and Communication

Once the voice samples are ready, criminals start contacting the victim. They impersonate the target’s tone and style, communicating through text, voice, or video. During this stage, scammers strive to emulate the target’s everyday communication style to lower the victim’s guard and build trust.

#### Stage Four: Inducing Money Transfer

When the victim is convinced of the scammer's identity, the criminals begin issuing commands to transfer money or make payments. They may fabricate urgent situations or business needs, prompting the victim to transfer funds to designated bank accounts. Driven by urgency and trust, victims often comply without further verification, following the scammers’ instructions to transfer the funds.


# How to Prevent "Impersonating Leaders" Scams?


#### Personal Precautions

When you receive a friend request on social media from someone claiming to be your leader, always verify their identity first. Avoid trusting blindly and handle friend requests cautiously. For any requests involving loans or money transfers, especially those that seem urgent or unusual, be vigilant and confirm with the leader in person through reliable channels. Only proceed with financial transactions after thoroughly verifying the situation.

Additionally, do not disclose your personal information to unfamiliar individuals online to enhance your personal prevention awareness. If you find yourself a victim of fraud, immediately preserve all related evidence and call 110 to report to the police so they can intervene promptly.

#### Technical Precautions

Platforms should analyze user behavior patterns and identity information to establish a security alert mechanism, monitoring and restricting suspicious activities such as abnormal logins and high-frequency messaging. By analyzing mouse movement patterns, typing styles, and other user behavior patterns, anomalies can be identified, and activities deviating from normal usage can be flagged as suspicious. Additional identity and device verification measures should be implemented, and large models can quickly screen vast amounts of data to detect subtle inconsistencies usually undetectable by humans, identifying attackers' abnormal operations.

**Dingxiang Device Fingerprinting** can generate a unified and unique Device Fingerprint for each device, identifying risk devices such as virtual machines, proxy servers, and emulators being maliciously controlled. It analyzes whether the device has multiple account logins, frequently changes IP addresses, or frequently changes device properties, identifying behaviors that are abnormal or inconsistent with user habits, tracking and identifying fraudsters' activities.

**Dingxiang atbCAPTCHA** based on AIGC technology can prevent brute force attacks, automated attacks, and phishing attacks by AI, effectively preventing unauthorized access, account hijacking, and malicious operations, thereby protecting system stability.

**Dingxiang Dinsight Real-Time Risk Control Engine** helps enterprises conduct risk assessments, anti-fraud analysis, and real-time monitoring, improving the efficiency and accuracy of risk control. Paired with Dinsight, the **Xintell Smart Model Platform** can automatically optimize security strategies for known risks, mine potential risks based on risk control logs and data, and configure different scene-supported risk control strategies with one click.


 When Criminals Use AI to Impersonate Leaders for Fraud

Through advanced speech synthesis and deep learning algorithms, scammers generate extremely realistic voice samples that are almost indistinguishable from the real thing

An office worker received an urgent call during a busy workday. The caller claimed to be his boss, informing him that he forgot to transfer funds to a new partner before leaving and needed to handle it immediately. The voice sounded very realistic, and the urgent content made him accept the transfer task without hesitation. After the call ended, he completed the transfer according to the provided wire transfer information. However, it was a scam call based on voice cloning.

![2024071003.png](https://cdn.aisecurius.com/images/249/249445af-4757-47af-b07a-957a6739bc28.png)

The key behind this phone fraud is AI technology. Through advanced speech synthesis and deep learning algorithms, scammers generate extremely realistic voice samples that are almost indistinguishable from the real thing. These voice samples not only mimic a person's tone and intonation but also replicate their language habits and expression styles, making it difficult for the recipient to doubt their authenticity.

The application of voice cloning in phone scams shows that traditional identity verification methods are no longer secure and reliable. Faced with this challenge, technical experts and enterprises need to take multi-layered defense measures: on the one hand, they need to effectively detect and intercept forged voices; platforms need to strengthen the identification of voice cloning; on the other hand, they must prevent the use and spread of voice cloning and verify user behavior and identity; thereby effectively reducing fraud actions by attackers.



# Several Technologies for Identifying Voice Cloning


Using advanced AI technology to authenticate the authenticity of voices, timely detecting, and intercepting suspicious content. Developing and applying AI tools for voice anti-counterfeiting, such as using voiceprint recognition technology to confirm the authenticity of the voice, providing security for voice communication.

### 1. Resemble's Detect-2B

Traditionally, most AI-generated audio clips sound "too clean," lacking the natural noise of real recordings. AI-based models can make judgments by focusing on these subtle differences.

Resemble recently launched the Detect-2B model, an AI solution specifically designed to detect cloned audio. The model architecture is based on Mamba-SSM or state space models, opting not to rely on static data or repetitive patterns but instead using random or probabilistic models. It responds better to different variables and dynamics, maintaining efficiency and accuracy even in poor recording conditions. It effectively handles audio signal diversity, capturing dynamic changes in audio clips, and thus effectively detecting voice cloning.

### 2. Meta's AudioSeal

Meta's Fundamental AI Research (FAIR) team introduced AudioSeal, an audio watermarking technology designed specifically for detecting AI-generated speech. AudioSeal's core technologies include advanced audio feature extraction and comparison algorithms, as well as efficient audio processing and watermark embedding techniques. Through these technical means, AI models can accurately identify and mark AI-generated audio segments, making the detection process faster and more efficient. Reportedly, the new localized detection method increased detection speed by 485 times, greatly enhancing the capability to handle large-scale audio data.

Compared to traditional methods, AudioSeal can accurately locate AI-generated speech segments in longer audio clips, effectively preventing cloning or tampering.

### 3. McAfee's Project Mockingbird

McAfee developed the AI model Project Mockingbird, specifically for detecting and identifying AI-generated audio content. Project Mockingbird uses advanced neural network architecture and large-scale data training. The model can accurately analyze audio features, distinguishing AI-generated audio from real recordings. It has rapid response capabilities, allowing real-time detection and identification during audio streaming, effectively addressing immediate audio fraud risks.

### 4. Multi-Factor Authentication Technology

Based on user behavior and identity to identify attackers. Platforms should analyze user behavior patterns and identity information to establish security warning mechanisms, monitor and restrict suspicious behaviors such as abnormal logins and high-frequency message sending. They should also analyze user behavior patterns like mouse movement and typing style to identify abnormalities, flagging suspicious activities deviating from normal usage. Additionally, extra identity and device verification can be employed. Large models can quickly sift through massive data and identify subtle inconsistencies usually undetectable by humans, discovering abnormal operations by attackers.

**Dingxiang Device Fingerprinting** can generate a unified and unique device fingerprint for each device, identifying risk devices like virtual machines, proxy servers, and emulators manipulated maliciously. It analyzes whether the device has multiple account logins, frequently changes IP addresses, or frequently changes device attributes to detect abnormal or user-unusual behaviors, tracking and identifying fraudsters' activities.

**Dingxiang atbCAPTCHA**, based on AIGC technology, can prevent brute force cracking, automated attacks, and phishing attacks by AI, effectively preventing unauthorized access, account hijacking, and malicious operations, thus protecting system stability.

**Dingxiang Dinsight Real-Time Risk Control Engine** helps enterprises with risk assessment, anti-fraud analysis, and real-time monitoring, improving risk control efficiency and accuracy. Paired with the **Xintell Smart Model Platform**, it can automatically optimize security strategies for known risks, mine potential risks based on risk control logs and data, and configure risk control strategies for different scenarios with one click.


Voice cloning technology presents both opportunities and threats. The development of effective detection and analysis technologies, as well as comprehensive multi-factor authentication mechanisms, are crucial in mitigating the risks associated with AI-generated voice fraud.

Recommended: Mainstream Voice Cloning Detection and Analysis Technologies

On April 23, 2024, the Beijing Internet Court ruled that the defendant tech company and software company should apologize to the plaintiff, 

With the widespread adoption of AI technology, extracting sufficient voice samples of a person can enable the "cloning" of their voice to create AI voice products. This technological advancement also poses challenges to the protection of voice rights.

The first nationwide AI voice infringement case, judged by the Beijing Internet Court, has sparked widespread attention to the boundaries of AI technology application. The case involves Ms. Yin, whose voice was cloned and commercialized without authorization.


# A Legal Case of Voice Cloning


Ms. Yin is a professional voice actress who discovered that her voice was being used for AI voiceover on a short video platform without her authorization. Through investigation, Ms. Yin found that her voice was recorded during cooperation with a cultural media company and then provided to a software company without her consent, ultimately purchased and commercialized by a tech company.

![2024071503.jpg](https://cdn.aisecurius.com/images/2eb/2ebab65b-476f-436b-8bba-766ec3a72308.jpg)

The tech company used AI technology to clone Ms. Yin's voice for a text-to-speech product called "Mo Xiaoxuan," which they sold on their software platform. Ms. Yin believed this action infringed on her voice rights and sued the relevant companies, demanding they cease the infringement, apologize, and compensate her for economic and emotional damages totaling 600,000 yuan. This case not only highlights the conflict between technology and law but also raises a deep reflection on the ethics of AI technology application. The development of voice cloning technology has brought convenience to areas such as speech synthesis and personalized services, but it must be cautiously applied within a legal framework to respect and protect individuals' personality rights.

A key focus of the case is the issue of authorization. The defendants argued that the AI-generated voice lacked identifiability and should not be protected under voice rights. The plaintiff contended that even if the defendants had copyright authorization, it did not equate to authorization of personality rights. The court ruled that if the public can associate the voice with a specific individual based on its timbre, tone, and speaking style, the voice is identifiable and should be protected. The court ultimately determined that the cultural media company and software company used the plaintiff's voice without permission, constituting infringement.

According to Article 1023 of the Civil Code of the People's Republic of China, the protection of a natural person's voice rights follows the relevant provisions of portrait rights. Voice rights are part of personality rights, with the prerequisite for protection being "identifiability."

![2024071504.jpg](https://cdn.aisecurius.com/images/9bf/9bf194cd-2c8e-4f35-bdb2-c270ac6f2bd3.jpg)

Beijing Internet Court's Vice President Zhao Ruigang noted that the iconic nature of a voice is very clear and can be associated with a person, thus voice rights are a new type of iconic personality right added to the Civil Code. Zhu Wei, Deputy Director of the Communication Law Research Center at China University of Political Science and Law, pointed out that voice rights are protected by referencing portrait rights, with the right to recognize a specific person through their voice belonging to specific personality rights. Experts believe that obtaining the legitimate authorization of the rights holder is key to determining voice rights infringement.

On April 23, 2024, the Beijing Internet Court ruled that the defendant tech company and software company should apologize to the plaintiff, and the cultural media company and software company should compensate the plaintiff 250,000 yuan in economic damages. The court's judgment reflects the importance of protecting voice rights and sets legal requirements for the commercial application of voice cloning technology.


#  Where Is an Individual's Voice Stolen?


The development of voice cloning technology has brought revolutionary changes to fields such as speech synthesis and personalized services. However, the dual-edged nature of technology has also led to an increase in scams based on voice cloning. Criminals use voice cloning for fraud in various ways, becoming increasingly rampant.

![2024071003.png](https://cdn.aisecurius.com/images/249/249445af-4757-47af-b07a-957a6739bc28.png)

Cloning anyone's voice based on AI technology is very easy. It only requires extracting voice samples from public channels, sometimes needing just a few seconds or even seconds of samples, to create highly realistic voice clones. These voice samples can not only mimic a person's tone and intonation but also replicate their language habits and expression styles, making it difficult for recipients to doubt their authenticity.

Before implementing new types of telecom network fraud using voice cloning, fraudsters first collect target information, including images, contact details, home addresses, work information, and lifestyle details. The Dingxiang Defense Cloud Business Security Intelligence Center believes that there are various channels through which this information can be leaked:

1. **Social Media**: As social media has become an indispensable part of people's daily lives, avoiding the oversharing of sensitive information on social media has become a repository for fraudsters to gather scam material.

2. **Network Data Breaches**: Large-scale network data breaches can lead to the leakage of personal information, including photos, videos, and voices. Fraudsters can acquire these leaked data on dark web platforms to carry out fraudulent activities.

3. **Phishing and Malware**: Fraudsters send phishing emails or use malware to obtain victims' personal information, including photos and videos. Once they successfully gain access to the victim's device, they can further acquire more personal data. For instance, on February 15, 2024, the foreign security company Group-IB announced the discovery of malicious software named "GoldPickaxe." This malware's iOS version deceived users into face recognition and submitting ID documents, then conducted "AI face-swapping" fraud based on the user's facial information.

4. **Public Events and On-Site Activities**: At public places or specific events such as conferences, exhibitions, and social activities, fraudsters might collect victims' photos, voices, and videos.

In the face of voice cloning challenges, technical experts and companies need to adopt multi-layered defense measures: on one hand, they need to effectively detect and identify fake voices; platforms need to strengthen the identification of voice cloning; on the other hand, prevent the use and spread of voice cloning, verifying the behavior and identity of users; thereby effectively reducing fraudulent actions by attackers. Particularly, it is important to avoid sharing personal photos, voices, and videos on social media, and reduce the exposure of personal accounts, family, work, and other private information to lower the risk of identity forgery. Once discovering fake content of voice cloning, it should be reported immediately to social media administrators and law enforcement to take measures to delete and trace the source.

What Are the Culprits Behind Personal Voice Cloning?

To address the new security challenges brought by AI, Hanxiaocircle App decided to adopt Dingxiang iOS reinforcement.



With the widespread application of AI technology, the security threats it brings have increasingly attracted people's attention. Especially in the mobile internet environment, users' personal information and data security are facing unprecedented challenges.[ In February 2024, a malicious software named "GoldPickaxe" appeared. The iOS version of this software deceived users into face recognition and submitting ID documents, using deepfake technology to create fake face videos, thus defrauding users' bank account information, posing a significant security risk to users.](https://www.aisecurius.com/blog/article/51)

This AI-based new threat not only poses a threat to users' personal information and data security but also could seriously impact users' safety and privacy rights. The development of AI makes false information and fraud easier. Attackers may use this technology to create fake videos or images, damaging the reputation of individuals or organizations, and even causing social chaos. Additionally, social engineering attacks are becoming more rampant, with attackers using AI to generate fake accounts or information, conducting phishing and online scams, leading to privacy leaks and financial losses. To address the new security challenges brought by AI, Hanxiaocircle App decided to adopt Dingxiang iOS reinforcement.

![2024072603.png](https://cdn.aisecurius.com/images/e56/e5666740-9b91-47a5-9e8d-5759ace5d2b4.png)

Hanxiaocircle App is an application that provides a comprehensive collection of content related to Korean dramas and entertainment. It has won the favor of many users with its rich content and smooth user experience. The app offers a vast amount of Korean drama and entertainment content, allowing users to get all the information they are interested in at once. With a large collection of drama video resources, users can enjoy new and popular dramas, with highly-rated shows all available. The smooth interactive experience allows users to quickly play high-definition videos with easy swiping, enjoying the ultimate viewing experience. However, with the increase in user numbers and the diversification of content, the app also faces many security challenges. Particularly with increasingly complex attack methods targeting mobile applications, security protection becomes extremely important.

Dingxiang iOS reinforcement is an adaptive reinforcement solution based on graph neural network technology. By deeply analyzing the App code and extracting features, it automatically selects suitable methods for encryption, obfuscation, and compression, increasing the difficulty of reverse engineering the code, effectively preventing the App from being cracked, copied, and repackaged by attackers. Additionally, the use of encryption and obfuscation engines further enhances the security of the App.

Dingxiang iOS reinforcement has the following technical advantages:

- **Deep Analysis and Feature Extraction**: By thoroughly scanning and analyzing the application code, extracting key features, and automatically selecting the optimal reinforcement scheme.
- **Multi-layer Encryption and Obfuscation**: Combining multi-layer code encryption and obfuscation techniques to increase code complexity and prevent reverse engineering and cracking.
- **Compression Technology**: Optimizing code size while ensuring code security, improving application runtime efficiency.
- **Real-time Protection**: Using dynamic monitoring and real-time protection mechanisms to timely detect and prevent potential security threats.

These technical advantages ensure that Hanxiaocircle App can maintain a high level of security and stability in the face of complex and ever-changing security threats. 

Hanxiaocircle App is committed to becoming a trusted comprehensive platform for Korean dramas and entertainment for users, providing a safe, convenient, and rich entertainment experience for Korean drama enthusiasts.

Security-Focused Hanxiaocircle App Adopts Dingxiang iOS Reinforcement

Among the top ten high-frequency telecom network fraud cases disclosed by the Ministry of Public Security, a case involving the use of “screen sharing” technology to commit fraud attracted significant attention.

In June, the Ministry of Public Security released the top ten high-frequency telecom network fraud types. According to the report, in 2023, the average age of telecom network fraud victims was 37 years old, with 62.1% between 18 and 40 years old and 33.1% between 41 and 65 years old.

Among the top ten high-frequency telecom network fraud cases disclosed by the Ministry of Public Security, a case involving the use of “screen sharing” technology to commit fraud attracted significant attention. In May 2024, Ms. Du from Wuxi, Jiangsu, received a video call at home from a person claiming to be a criminal investigation team officer of the Wuxi Public Security Bureau. In the video, a fake “police officer” dressed in uniform told Ms. Du that her bank card was suspected of money laundering and needed her cooperation for investigation. Following the instructions, Ms. Du downloaded a conferencing software for screen sharing to cooperate with the “officer” in verifying the funds in her bank account. The “officer” said that Ms. Du needed to transfer the funds in her bank account to a designated “safe account” to prove her innocence. During this process, to demonstrate the normal flow of funds, the “officer” also instructed Ms. Du to take out a bank loan of 150,000 yuan and transfer it to the “safe account.” Ms. Du only realized she was being scammed when her family discovered the situation.

![2027072302.png](https://cdn.aisecurius.com/images/998/998e04d0-9ea7-421f-8f6d-3ed25ef430da.png)

In this case, the scammer induced the victim to enable screen sharing on her phone, leading to the leakage of her account passwords. Even if the scammer did not actively ask for information, they could see everything on the victim’s phone, including characters typed during password entry, received verification codes, and more, thereby transferring funds from the victim’s card.


#  How Scammers Use "Screen Sharing" for Fraud


"Screen sharing" is one of the application scenarios of Internet technology, where the screen information of one device is projected in real-time onto another device over the network. This includes screen apps, pop-up SMS, WeChat messages, other app notifications, the process of entering account passwords, unlocking, and all other real-time operations displayed on the screen. Originally designed to facilitate user sharing, this function has become a means for fraudsters to profit.

According to the analysis by Dingxiang Security Cloud Business Intelligence Center, scammers use “screen sharing” for fraud in four main steps:

1. **Information Gathering and Trust Building:** Scammers use illegal channels to obtain victim information and impersonate public security officers, shopping platform customer service, or campus loan cancellation staff to accurately match the victim’s situation and gain trust.

2. **Tricking Victims into Enabling "Screen Sharing":** Using apps with built-in screen sharing functions (QQ, Tencent Meeting, DingTalk, etc.), scammers can monitor all operations on the victim’s phone in real-time.

![2024071901.png](https://cdn.aisecurius.com/images/94e/94ec54a1-6b3e-4d4a-8d34-81d4fd333514.png)

3. **Monitoring Online Banking Accounts and Verification Codes:** Scammers instruct victims to download apps and enable screen sharing. Using the previously obtained bank card numbers, they log into online banking to perform transfer operations while monitoring the received SMS verification codes in real-time.
![2024071902.png](https://cdn.aisecurius.com/images/2d3/2d3c59f0-a9c9-44cd-89d8-29599ff15fd6.png)
4. **Stealing Funds:** Initially, scammers trick victims into transferring funds themselves. If the victim realizes the scam or hesitates, scammers use intercepted verification codes to directly perform transfers, moving the funds without the victim’s knowledge.


# Dingxiang Device Fingerprinting Can Prevent This Risk


On Android devices, “screen sharing” is implemented through the MediaProjection service. MediaProjection is a technology available in Android 5.0 and above, allowing developers to capture or record the screen. When the MediaProjection service is requested, the Android system pops up a permission request box. Only after the user clicks "Confirm" can screen sharing or recording begin.

![2024071903.png](https://cdn.aisecurius.com/images/7f7/7f74b560-d6c2-4c53-b7b5-21ea50d4fabd.png)

Dingxiang Device Fingerprinting can detect screen sharing calls from apps like DingTalk and Tencent Meeting in real-time and immediately issue alerts to warn users of potential risks.

When MediaProjection is initiated, the system automatically creates a virtual display. Dingxiang Device Fingerprinting can detect this additional display, confirm that the device is in “screen sharing” mode, and immediately report it to the risk control center. The risk control center will then issue an alert, warning the operator of the potential risk and advising caution.

Dingxiang Device Fingerprinting involves collecting and analyzing a device's hardware, software, and behavioral data to uniquely identify and recognize each device. By using Device Fingerprinting, virtual machines, proxy servers, simulators, and other maliciously controlled devices can be identified. This technology can analyze whether a device has multiple account logins, frequently changes IP addresses, or frequently changes device attributes, identifying abnormal or unusual user behaviors. This helps track and identify fraudulent activities, preventing insurance fraud incidents from occurring. Additionally, Device Fingerprinting can serve as an extra layer of identity verification, enhancing security during user login and transaction processes. By recording and comparing Device Fingerprinting, legitimate users and potential fraudsters can be distinguished. This technology supports Android, iOS, H5, public accounts, and mini-programs, effectively detecting risks such as simulators, rooted devices, jailbreaks, and injection hijacking. It features rapid countermeasures, efficient risk identification, over 99% stability, and 100% uniqueness.


# How Users Can Prevent "Screen Sharing" Risks


"Screen sharing" is increasingly being used in various forms of scams. The final step in these scams is to trick the victim into using or downloading network video conferencing software that provides "screen sharing" functionality. The goal is to spy on the user’s phone operations to obtain critical information such as bank account passwords.

1. **Do not casually enable the "screen sharing" function on your Android phone unless confirmed.**

2. **Immediately close any "screen sharing" invitations from strangers. Do not use the "screen sharing" function with strangers, as they can see all your operations, including entering passwords.**

3. **Be extremely cautious when dealing with private information, especially bank card passwords and verification codes. Do not disclose any such information to strangers.**

When receiving suspicious video calls or phone calls, stay calm. Use an excuse like a bad signal to hang up and then immediately call back through another method to confirm the legitimacy of the call, avoiding direct responses to potential scam content.

The Ministry of Public Security Disclosed This Telecom Fraud Case, Dingxiang Device Fingerprinting Can Prevent It

However, employment service websites face issues such as data leakage, identity theft, phishing attacks, and account security problems

Employment service websites play a crucial role in promoting local enterprise recruitment and talent employment. For enterprises, these websites provide a convenient and efficient recruitment channel, reducing recruitment costs, lowering advertising expenses, and simplifying recruitment processes. For job seekers, these websites serve as a two-way platform for self-presentation, with transparent job information and feedback 
mechanisms that enhance information transparency and optimize matching mechanisms. This increases supply-demand matching efficiency and simplifies recruitment and job search processes. Additionally, employment service websites offer services such as contract termination, dispatch, and redeployment, facilitating various employment relationships. In summary, employment service websites enhance local enterprise recruitment efficiency and talent supply-demand matching, becoming a vital force in driving local economic development and social stability.

![2024071708.png](https://cdn.aisecurius.com/images/b66/b6676486-5941-403b-8075-aae3ac311939.png)

However, employment service websites face issues such as data leakage, identity theft, phishing attacks, and account security problems. To effectively address these risks, Turpan Employment Service Network has strengthened data encryption and security protection, improved identity verification mechanisms to prevent identity theft, and chosen Dingxiang atbCAPTCHA as a tool for user registration and login identity verification.

Dingxiang atbCAPTCHA, based on AIGC technology, can prevent AI brute force attacks, automated attacks, and phishing threats, effectively preventing unauthorized access, account theft, and malicious operations, thus protecting system stability. It integrates 13 types of verification methods and various defense strategies, encompassing 4380 risk strategies, 112 types of risk intelligence, covering 24 industries and 118 types of risks. Its defense accuracy rate is as high as 99.9%, and it can quickly transform from risk to intelligence. Additionally, it supports seamless passing for secure users, reducing real-time countermeasure handling time to within 60 seconds, further enhancing the convenience and efficiency of digital login services, effectively safeguarding user accounts and website information security.

Turpan Employment Service Network is hosted by the Turpan City Human Resources and Social Security Bureau and organized by the City Public Employment Service Bureau. It is an employment information portal that integrates job seekers, employers, and employment services. As the most authoritative public employment service website in Turpan, the information published online is characterized by "authenticity, authority, and convenience." The website offers sections on talent recruitment, employment consultation, policies and regulations, employment promotion, job search and recruitment, career intermediaries, and self-employment. In addition to publishing job seekers' personal information and employer needs, it also undertakes multiple services such as online job fairs, signing, contract termination, dispatch, redeployment, reporting, file inquiry, and statistics between graduates and employers.

 Dingxiang Captcha Enhances Security for Turpan Talent Service Platform

Using forged voices, attackers utilize various media such as social media, email, remote meetings, online recruitment, and news information to target and attack their audience.

Voice cloning technology uses AI and algorithms to replicate a person's voice, making it sound almost identical to the original. This technology can be applied in movie dubbing and speech synthesis, and it has broad application prospects in fields such as healthcare, education, and virtual assistants. For example, it helps people who have lost their voice regain their speech ability, create audiobooks, create personalized digital assistants, and provide natural text-to-speech and speech translation services.

![2024071002.png](https://cdn.aisecurius.com/images/8fd/8fd4b464-761e-4c5d-8c3b-5b6d9d986873.png)

There are three main ways of voice cloning: playback-based cloning, synthesis-based cloning, and mimicry-based cloning.

1. **Playback-based Cloning**
This method involves copying the recording of the speaker's voice and then using playback and deep learning algorithms to simulate and replicate the speaker's voice. There are two main types: far-field detection and cut-and-paste detection. Far-field detection uses a hands-free phone to play the victim's recording, while cut-and-paste forges sentences requested from text-dependent systems.

2. **Synthesis-based Cloning**
Voice synthesis-based voice cloning uses software or hardware systems to convert text into natural speech in real-time. Such systems typically consist of three modules: a text analysis model, an acoustic model, and a vocoder. First, clear and well-structured original audio and corresponding text transcriptions need to be collected. Then, using this data, a text-to-speech model is trained to build a synthetic audio generation model. The text analysis module converts the input text into linguistic features, the acoustic module extracts the target speaker's parameters based on these features, and finally, the vocoder creates the human voice waveform based on these parameters, generating the final audio file.

3. **Mimicry-based Cloning**
Mimicry-based voice cloning, also known as voice conversion, is a method of converting the original speech of one speaker to sound like another speaker. This technology tries to mimic the target voice without changing the linguistic information by altering the style, tone, or prosody of the voice signal. This technique usually uses generative adversarial networks (GANs) for mimicry generation, offering high flexibility and quality, and can change the style, tone, or prosody of the voice while keeping the input text unchanged.


# Voice Cloning as a New Means for Scammers


The development of voice cloning technology has brought revolutionary changes to speech synthesis, personalized services, and other fields. However, the double-edged nature of technology has also led to an increase in fraud activities based on voice cloning. Voice cloning is being used by criminals to carry out various and increasingly rampant fraud schemes.

![2024071003.png](https://cdn.aisecurius.com/images/249/249445af-4757-47af-b07a-957a6739bc28.png)

**Social Media Fraud.** Scammers use cloned voices to impersonate public figures or the victim's relatives and friends on social media to carry out fraudulent activities.

**Social Tools or Phone Fraud.** By sending forged voice messages through social tools or directly making phone calls, scammers induce victims to disclose sensitive information or directly transfer money.

**Remote Meeting Attacks.** In remote meetings, scammers use cloned voices of participants to interfere or mislead, stealing business secrets or personal data.

**Fake News Information.** Scammers use cloned voices in fake news information to increase the credibility of the information, tricking victims into falling for it.


#  How to Prevent Voice Cloning Fraud


Using forged voices, attackers utilize various media such as social media, email, remote meetings, online recruitment, and news information to target and attack their audience. To prevent and combat voice cloning fraud, it is crucial to effectively identify and detect forged voices while also preventing the utilization and spread of voice cloning. Therefore, individuals should reduce sharing sensitive information and implement multiple preventive measures. Platforms utilized by attackers need to strengthen the identification of voice cloning and reduce fraudulent activities by analyzing and verifying user behavior and identity. Additionally, AI tools for voice anti-counterfeiting can provide security for voice communication.


###  Individual Preventive Strategies


1. **Reduce Sharing of Sensitive Information.** Avoid sharing sensitive information such as personal photos, voices, and videos on social media, and reduce public disclosure of private information such as personal accounts, family, and work to lower the risk of identity forgery. If "deepfake" content is discovered, it should be reported to social media administrators and law enforcement authorities immediately, and measures should be taken to delete and trace the source.

2. **Proactive Prevention by Voice Cloning Victims.** Set a "safety word" or "challenge question" known only to close friends, family, and colleagues, to verify the identity of the caller when receiving suspicious calls or messages. If the other party cannot provide the correct "safety word" or avoids the question, hang up immediately and confirm through known safe contact methods.

3. **Recipient Vigilance.** When receiving suspicious videos or calls, remain calm, make an excuse about bad signal to hang up, and call back immediately through other means to avoid directly responding to potential fraudulent content.



### Platform and Technology Preventive Measures



 **1.Enhance Voice Cloning Detection Technology.** Social media and communication platforms should strengthen their ability to identify voice cloning using advanced AI technology for voice authenticity verification, promptly detecting and intercepting suspicious content. Develop and apply AI tools for voice anti-counterfeiting, such as using voiceprint recognition technology to confirm the authenticity of the voice, providing security for voice communication.

2. **Identify Attackers Based on User Behavior and Identity.** Platforms should establish a security alert mechanism by analyzing user behavior patterns and identity information, monitoring and restricting suspicious activities such as abnormal logins and high-frequency message sending. Additionally, analyzing user behavior patterns such as mouse movement and typing style to identify anomalies, marking suspicious activities that deviate from normal use. Implement additional identity and device verification, and use large models to quickly screen vast amounts of data and detect subtle inconsistencies usually unnoticed by humans, identifying attackers' abnormal operations.

Dingxiang Device Fingerprinting can generate a unified and unique device fingerprint for each device. It builds a multi-dimensional recognition strategy model based on devices, environments, and behaviors to identify risk devices such as virtual machines, proxy servers, and emulators being maliciously controlled. It analyzes whether the device has multiple account logins, frequent IP address changes, or frequent device attribute changes, tracking and identifying fraudster activities. This helps enterprises achieve unified ID operations across all scenarios and channels, supporting cross-channel risk identification and control.

Dingxiang atbCAPTCHA, based on AIGC technology, can prevent threats such as AI brute force attacks, automated attacks, and phishing attacks, effectively preventing unauthorized access, account hijacking, and malicious operations, thereby protecting system stability. It integrates 13 verification methods and multiple prevention and control strategies, with 4,380 risk strategies, 112 types of risk intelligence, covering 24 industries and 118 types of risks. Its prevention and control accuracy rate is as high as 99.9%, and it can quickly convert risks into intelligence. It also supports seamless user experience for secure users, reducing real-time response handling to within 60 seconds, further enhancing the convenience and efficiency of digital login services.

Dingxiang Dinsight helps enterprises with risk assessment, anti-fraud analysis, and real-time monitoring, improving the efficiency and accuracy of risk control. Dinsight's average processing speed for daily risk control strategies is within 100 milliseconds, supporting the configuration and accumulation of multi-source data. It leverages experience in mature indicators, strategies, and models, as well as deep learning technology, to achieve self-monitoring and self-iterating mechanisms for risk control performance. Paired with the Xintell intelligent model platform, it can automatically optimize security strategies for known risks, detect potential risks based on risk control logs and data mining, and support risk control strategies for different scenarios with one-click configuration. Its technology, based on association networks and deep learning, standardizes the complex processes of data processing, mining, and machine learning, offering end-to-end modeling services from data processing, feature derivation, model building to final model deployment.


### Application of AI Tools for Voice Anti-counterfeiting


AI voice integration tools can also embed indelible watermarks, such as slight disturbances, random noise, or fixed background rhythms, which can be identified by the listener. Additionally, hardware for recording audio with AI voice integration tools can include built-in sensors to detect and measure biological signals produced by the human body while speaking, such as heartbeat, lung movements, vocal cord vibrations, and movements of the lips, jaw, and tongue. These recorded signals can be attached to the audio, providing verifiable information to the listener to distinguish whether the voice is naturally recorded or AI-forged.

How to Identify: Is the Voice on the Phone Cloned by Scammers?

On April 10, 2024, password management tool company LastPass disclosed through its official blog a scam involving the impersonation of its CEO

On April 10, 2024, password management tool company LastPass disclosed through its official blog a scam involving the impersonation of its CEO, Karim Toubba, using Deepfake technology, warning the public to beware of AI-generated fraudulent calls.

![2024070401.png](https://cdn.aisecurius.com/images/339/339dccff-e0fc-4cb2-a61c-26c595472459.png)

According to the blog, a fraudster impersonated LastPass CEO Karim Toubba by creating a WhatsApp account. The scammer then sent a series of messages to LastPass employees, including AI-generated voice messages imitating Karim Toubba's voice, multiple unanswered audio calls, and other communications. This created a sense of urgency to force employees into believing it was a genuine call from CEO Karim Toubba. Social engineering tactics in cybercrime often rely on creating urgency or panic to compel victims into hasty decisions.

However, the employee quickly identified this as a fraudulent call. WhatsApp is not a commonly used communication tool within LastPass, and the content of the voice messages exceeded the employee's expectations of normalcy. The employee promptly reported the incident to LastPass' security department. Subsequently, LastPass issued a notice confirming the attack did not compromise its overall security posture.

This attempted fraud highlights how advancements in AI technology, specifically voice cloning, are making cybercrime scams increasingly sophisticated. Furthermore, it underscores the importance of employee awareness training in mitigating such attacks.

These incidents continue to demonstrate the evolving complexity of cyberattacks. On the other hand, LastPass emphasizes the importance of employee awareness training in mitigating such attacks. Social engineering strategies often rely on creating urgency or panic to compel victims into hasty decisions.

AI voice cloning can generate synthetic voices for individuals with speech impairments, but it also serves as a tool for malicious actors seeking to profit from deception. To defend against AI voice cloning fraud, the Dingxiang Cloud Business Security Intelligence Center recommends several defensive measures:

1. **Multi-dimensional Voice Collection**: Enhance voice capture with additional dimensions. Hardware used for audio recording can include built-in sensors to detect and measure biological signals such as heartbeat, lung movements, vocal cord vibrations, and movements of the lips, jaw, and tongue. Recorded voices can be augmented with this information, providing verifiable details to distinguish natural recordings from AI fabrications.

2. **Embedded Indelible Watermarks in AI Voice Fusion Tools**: AI voice synthesis tools should incorporate indelible voice watermarks, such as subtle disturbances, random noise, or fixed background rhythms. These enable listeners to identify discrepancies.

3. **Establishment of "Safety Words"**: Pre-establish a "safety word" known to family, friends, colleagues, and partners. If receiving a suspicious call, request the caller to provide this "safety word." Failure to do so can promptly identify fraudulent calls and prevent panic.

4. **Hang Up and Redial**: If receiving a suspicious call, simulate poor signal and promptly hang up. Then redial the purported caller's number to confirm authenticity.

In conclusion, when confronted with AI voice cloning fraud, maintaining composure and vigilance is crucial alongside implementing effective prevention and response measures. Timely reporting and cooperation with law enforcement are essential in combating such criminal activities.


Renowned US Password Tool Company Falls Victim to AI Voice Cloning Scam

Liaoning Internet Security Department received reports from the public that their bank accounts were missing three thousand yuan, with several records showing transfers to unfamiliar accounts.


In May 2024, Liaoning Internet Security Department received reports from the public that their bank accounts were missing three thousand yuan, with several records showing transfers to unfamiliar accounts. Upon receiving the clues, Liaoning cyber police promptly launched an investigation. During the investigation, the Internet security department received similar reports multiple times, where money was sporadically transferred to unfamiliar accounts, mostly in amounts below 50,000 yuan. These cases shared the common traits of small transfer amounts and covert methods, raising high alert levels among law enforcement.

![2024070203.png](https://cdn.aisecurius.com/images/d05/d0580d7f-06c7-4d88-b4e9-4786fdd83665.png)

After several months of investigation, Liaoning cyber police uncovered an organized criminal group supported by AI technology. The criminal group, led by Ren Moumou, consisted of four "information officers" and "liaison officers" as key members, with an additional 27 "support personnel" providing assistance. They had clear divisions of responsibility: "information officers" Zhao Moumou and Liu Moumou were responsible for purchasing citizens' personal information elements—facial photos, mobile numbers, and bank card numbers. "Liaison officer" Liu Moumou was responsible for contacting overseas criminals, with "Qiu Mou" selling these elements. "Qiu Mou" then used AI software to create facial recognition verification videos using face images, subsequently stealing depositors' bank balances.


# The theft process of the criminal group


Dingxiang Defense Cloud Business Security Intelligence Center released a special issue titled "AI Face-swapping Threat Research and Security Strategy," revealing that the criminal group used AI face-swapping technology. Through deep learning algorithms, they synthesized the facial features of the target individuals into videos to bypass financial institutions' facial recognition verification systems. This application of technology enabled criminals to remotely control bank accounts without the depositors' awareness.

For financial institutions, the new type of telecom fraud based on "AI face-swapping" is particularly concerning. Fraudsters fabricate information, voice, video, and images, combining real and false identity information to create entirely new false identities for opening bank accounts or engaging in fraudulent purchases. Moreover, fraudsters can use "AI face-swapping" technology to quickly learn different banking processes and swiftly perpetrate fraud across multiple banks simultaneously.

![2024062703.jpg](https://cdn.aisecurius.com/images/727/727f6a32-2069-4abc-a621-3c159014cd8c.jpg)

Using a specific case intercepted by Dingxiang Defense Cloud Business Security Intelligence Center as an example of a new type of telecom fraud based on "AI face-swapping," the fraud process of the fraudster primarily involves four stages. "AI face-swapping" technology is just one critical factor in the fraud process; in other stages, if victims cannot discern and judge, they can easily step into traps following the instructions of fraudsters.

### 
1. Initially, the criminal group acquires large 
amounts of citizens' personal information, including facial photos, mobile numbers, and bank card numbers, through the black market or illegal means. This information may stem from various illegal database leaks or organized cyber attacks.


### 2. Utilizing AI technology
, the criminals use acquired facial photos as base material to generate high-quality fake faces through face synthesis algorithms. These faces may not exist in reality but closely resemble the original photos visually, sufficient to deceive most facial recognition systems.


### 3. Subsequently
, the criminal group uses AI software to embed the generated fake faces into videos, creating seemingly authentic facial recognition verification videos. These videos typically include fake faces, corresponding mobile numbers, and bank card information. They simulate real face-to-face verification processes, deceiving most facial recognition systems. Once the fake verification videos are created, the criminal group combines them with stolen bank card information.


### 4. The criminal group uses smartphones 
or computers through banking applications or online banking to transfer funds from victims' accounts to specified unfamiliar accounts, often during late nights or when users infrequently use their bank accounts, to avoid detection by victims.


# Defense: Multiple Technological Measures


[To combat "AI face-swapping" new telecom fraud, it is essential to verify offline when suspicious online incidents arise, increase communication ](https://www.aisecurius.com/blog/article/55)time, and employ exploratory measures such as requesting specific actions to expose potential flaws. Furthermore, enterprises are advised to adopt multiple technologies and methods. Additionally, guiding the positive application of AI technology and aggressively combating criminal activities are fundamental solutions.


### 1. Recognition of fraudulent "AI face-swapping" videos:
 During video chats, requesting actions like touching the nose or face to observe facial changes can help detect authenticity. A real person's nose will deform when pressed. Alternatively, requesting actions such as eating or drinking to observe facial changes, or asking to perform unusual gestures or expressions like waving or difficult hand gestures can aid in distinguishing between genuine and fake. Waving can cause facial data disturbance, resulting in slight shaking, flickering, or other abnormal conditions.


### 2. Comparison and identification of device information,
 geographical location, and behavioral operations: Dingxiang Device Fingerprinting identifies legitimate users and potential fraudulent behavior by recording and comparing device fingerprints. This technology uniquely identifies and recognizes each device, detecting manipulated devices such as virtual machines, proxy servers, or emulators. It analyzes behaviors like multiple account logins, frequent IP address changes, or frequent device attribute changes that deviate from typical user habits, assisting in tracking and identifying fraudster activities.


### 3. Identification of abnormal account behaviors: 
Activities such as remote logins, device changes, phone number changes, or sudden activity in dormant accounts require enhanced verification. Continuous identity verification during sessions is crucial to ensure consistent user identity throughout usage. Dingxiang atbCAPTCHA accurately distinguishes between human operators and machines, precisely identifying fraudulent behaviors, and real-time monitoring and intercepting abnormal activities.


### 4. Prevention of fake videos and images from "AI face-swapping"
: Dingxiang's comprehensive facial security threat perception solution intelligently verifies multiple dimensions of information such as device environment, facial information, image authenticity, user behavior, and interaction status. It quickly identifies and blocks injected attacks, live forgery, image forgery, camera hijacking, debugging risks, memory tampering, root/jailbreak attempts, malicious ROMs, emulators, and over 30 types of malicious behaviors. It can dynamically configure video verification strength and user-friendliness, implementing a dynamic mechanism of stronger verification for abnormal users while facilitating smoother access for legitimate users.


### 5. Uncovering potential fraud threats:
 Dingxiang Dinsight assists enterprises in risk assessment, anti-fraud analysis, and real-time monitoring to enhance the efficiency and accuracy of risk control. Dinsight's average daily processing speed for risk control strategies is within 100 milliseconds, supporting configurable access and deposition of multi-source data. Leveraging mature indicators, strategies, models, and deep learning technologies enables self-monitoring and iterative mechanisms for risk control. Paired with the Xintell intelligent model platform, it automatically optimizes security strategies based on risk logs and data mining of potential risks, supporting risk control strategies for various scenarios. Standardizing complex data processing, mining, and machine learning processes using correlation networks and deep learning technology provides end-to-end modeling services from data processing, feature derivation, model construction to final model deployment.



Stolen Bank Account: Tens of thousands quietly transferred away late at night

'Jihe Network,' digitizes linear textual content of ancient books. It collaborates with over 20 publishing institutions and research teams,

Ancient books, as precious heritage of human civilization, play an indispensable role in cultural and social development. They not only record and preserve ancient knowledge and wisdom but also serve as cornerstones for academic research, providing rich resources for understanding history and culture. The ideas and viewpoints within ancient books transcend time and space, stimulating modern thinking and innovation, and fostering exchange between different cultures. Moreover, ancient books constitute a significant part of cultural heritage, and through translation and digitization efforts, they can be better protected and disseminated.

The national comprehensive platform for the collation and digitization of ancient books, 'Jihe Network,' digitizes linear textual content of ancient books. It collaborates with over 20 publishing institutions and research teams, establishing fair trade rules and pricing systems to facilitate the circulation and dissemination of digital resources of ancient books.
![2024070202.png](https://cdn.aisecurius.com/images/306/3061f3d8-ed2f-406d-82a7-a7bdbd8d7234.png)
Jihe Network places great emphasis on data security and privacy protection, implementing a robust copyright protection mechanism to ensure the legality and compliance of every digital resource transaction. To prevent risks such as account verification through SMS being abused by malicious entities, Jihe Network has implemented a series of security measures and introduced Dingxiang seamless verification.


# How to Prevent Website SMS Abuse


Whether individuals face SMS bombardment or enterprises suffer SMS misuse, ultimately, it is due to attacks on the enterprise-side SMS interface. Effective security measures need to be taken to prevent the misuse of mobile SMS.


### 1. Set SMS sending time intervals:
 Set time intervals for repeated sending to the same number, generally set between 60-120 seconds, to prevent malicious attacks on the SMS interface.


### 2. Set SMS acquisition limits
: Restrict the number of times a particular phone number can receive SMS verification codes within a certain time period, such as different limits for 24 hours, 12 hours, 1 hour, etc.


### 3. Restrict SMS call IP addresses:
 Set the maximum sending volume for a single IP address within a certain time period, although this may cause false positives.
![2024070201.png](https://cdn.aisecurius.com/images/16f/16f02e3c-1b36-4398-94b2-6fe7d3af3b9a.png)

### 4. Deploy intelligent CAPTCHA:
 Before sending SMS verification codes, validate through CAPTCHA to prevent automated software sending. Dingxiang atbCAPTCHA provides additional security verification for SMS sending, preventing software-based sending, and using risk data and dedicated risk control models based on sender environment, devices, and more, it estimates possible abnormal operations for secure operations provide risk decision data.

Dingxiang atbCAPTCHA based on AIGC technology can prevent AI brute-force attacks, automated attacks, phishing attacks and other threats, effectively prevent unauthorized access, account theft and malicious operations, thus protecting system stability. It integrates 13 verification methods and multiple prevention and control strategies, compiling 4380 risk policies, 112 risk intelligence categories, covering 24 industries and 118 risk types. Its precision in prevention and control reaches 99.9%, and it can quickly convert from risk to intelligence. It also supports seamless secure user pass-through, with real-time handling capabilities reduced to within 60 seconds, further enhancing the convenience and efficiency of user login services.

Jihe Network is the first comprehensive platform for the collation and digitization of ancient books in China, developed and operated by Guolian (Beijing) Digital Media Technology Co., Ltd., a subsidiary of Zhonghua Book Company. Jihe Network integrates a variety of digital products of ancient books, providing unified user management and online purchasing services. It includes various digital products such as 'Chinese Classic Ancient Books Library,' 'Chinese Literature and History Academic Works Library,' 'Chinese Ancient Books Bibliography Database,' 'Chinese Stone Inscription Database,' 'Overseas Traditional Chinese Medicine Ancient Books Database,' 'Chinese Literature and History Reference Works Database,' 'Imperial Examination Database,' 'Chinese Woodblock New Year Prints Database,' 'Chinese Calligraphy Database,' 'Jihe Library,' 'Late Qing and Republic of China Literature Platform,' 'Southwest Associated University Special Database,' and more. The database types and themes are diverse, encompassing ancient literature, academic research, cultural arts, and thematic achievements. As of 2021, the 'Chinese Classic Ancient Books Library' has online resources exceeding 5,000 kinds, totaling approximately 2 billion characters, and has become one of the authoritative databases in its professional field."

Dingxiang atbCAPTCHA Helps Promote Research on Ancient Books and Culture

Mr. Li from Hebei was browsing a dating app when he was tempted and agreed to chat with the so-called "beauty" and downloaded an illegal chat software sent by the other party

As AI technology continues to develop, AI has become a new tool for online fraud. Criminals use deepfake technology to perform "AI voice impersonation" and "Deepfake", combining other people's faces and voices to create fake images and videos, creating fake identities, and impersonating others to induce victims to transfer money and other crimes. This type of fraud is varied and difficult to distinguish, and consumers are easily caught in scams. Without a doubt, in today's digital environment, AI has changed the nature of identity fraud.


# Four Types of People that Fraudsters Like to Impersonate


**1. Impersonating acquaintances**

[Ms. Li, a resident of Ordos City, Inner Mongolia, recently encountered a new type of telecommunications and network fraud case that used AI face-swapping technology.](https://www.aisecurius.com/blog/article/84) The fraudster impersonated Ms. Li's old classmate "Jia Mou" and contacted her through WeChat and QQ platforms. They used AI technology to create fake video calls and successfully gained Ms. Li's trust. Ms. Li received a WeChat friend request, and the other party's nickname and avatar were her old classmate "Jia Mou". After a brief identity confirmation via QQ video call, the fraudster asked Ms. Li for help with a cash flow problem and sent her a screenshot of a fake bank transfer record. Without verifying the recipient's information, Ms. Li transferred 400,000 yuan to the other party's account. When the other party continued to ask for money, Ms. Li realized that she might be being scammed, so she immediately contacted her real classmate and called the police. The Ordos police quickly activated the emergency stop payment mechanism and successfully intercepted and returned Ms. Li's 400,000 yuan.

![2024062501.png](https://cdn.aisecurius.com/images/22b/22b8287a-1b65-44fb-97dc-74fd64cea3a7.png)

**2. Impersonating beauties**

[Mr. Li from Hebei was browsing a dating app when he was tempted and agreed to chat with the so-called "beauty" and downloaded an illegal chat software sent by the other party. ](https://www.aisecurius.com/blog/article/84)However, the so-called "beauty" was actually a fake person created by the scammer. During the chat, the scammer took screenshots of Mr. Li's appearance and used "Deepfake" to create a pornographic video with Mr. Li's face. At the same time, the scammer also used the illegal chat software to obtain the mobile phone numbers of all the contacts in Mr. Li's mobile phone, and used this information to blackmail and extort Mr. Li. In order to get the scammer to delete the pornographic video, Mr. Li had no choice but to agree to transfer 120,000 yuan to the other party.

**3. Impersonating colleagues**

[In January of this year, there was a 200 million Hong Kong dollar "Deepfake" video conference fraud case in Hong Kong. The characteristic of this fraud case is that it uses AI deepfake technology. ](https://www.aisecurius.com/blog/article/50)The fraudster first collected facial and voice data of the target company's senior management personnel, and then used AI technology to "face swap" this data onto the scammer's body to create a seemingly real video conference, which led to the company employees participating in the video conference transferring 200 million Hong Kong dollars to the scammer's account.

![2024063704.png](https://cdn.aisecurius.com/images/0c7/0c71c6c2-5294-40da-8e7d-766f4b571f96.png)

**4. Impersonating family members**

[In December 2023, an overseas student was "kidnapped" abroad, and his parents were asked by the "kidnappers" for a ransom of 5 million yuan. ](https://www.aisecurius.com/blog/article/47)They also received a video of the "hostage" being controlled and harmed. Through on-site investigation and international police cooperation, 5 hours later, the student involved, Xiaoxia, was successfully rescued - not from the kidnappers, but at the immigration port of entry in the country where his school is located. The truth was also revealed: the scammer used deepfake technology to create Xiaoxia's voice and kidnapping video, and then sent it to Xiaoxia's family to defraud money.

![2024062707.png](https://cdn.aisecurius.com/images/31f/31f92432-e84e-4662-9394-ee7176914f06.png)

How victims' information is leaked

Before carrying out new telecommunications network frauds such as "AI voice imitation" and "Deepfake," fraudsters first collect targets' information, including portraits, contact details, home addresses, work information, lifestyle details, as well as photos, videos, and voice recordings of victims. The avenues through which this information is obtained are diverse and include:


### 1. Social media
: Fraudsters can gather victims' photos and personal information through social media platforms and other publicly available online resources. Many individuals share a substantial amount of personal photos and videos on social media, which can be exploited by malicious actors. Additionally, some forums and online platforms may publicly display users' photos and videos, allowing fraudsters to gather relevant information.


### 2. Network data breaches
: Large-scale network data breaches can lead to the exposure of personal information, including photos, videos, and voice recordings. Fraudsters can access this leaked data through dark web platforms to execute fraudulent activities.


### 3. Phishing and malware
: Fraudsters use phishing emails or malicious software to obtain victims' personal information, including photos and videos. Once they gain access to the victim's device, they can further acquire more personal data. For example, on February 15, [2024, the foreign security company Group-IB announced the discovery of malicious software named "GoldPickaxe." The iOS version of this malware deceives users into performing facial recognition, submitting identification documents, and then uses the user's facial information for "Deepfake" fraud.](https://www.aisecurius.com/blog/article/51)


### 4. Public events and on-site activities
: In public places or specific events such as conferences, exhibitions, and social gatherings, fraudsters may collect victims' photos and videos.

Among these methods, collecting a large number of photos, information, and videos from publicly accessible social media is prevalent. Since social media has become an indispensable part of daily life, it is crucial to avoid oversharing sensitive information on social media platforms, as it serves as a warehouse for fraudsters to obtain materials for fraud.

Especially, every dynamic update and every photo shared by victims on social media contain a wealth of sensitive information, which has become a repository for fraudsters to obtain materials for fraud. Based on the collected sensitive information, fraudsters perform feature extraction, enabling them to create fake facial videos. By extracting voice samples from video clips on social media, with samples as short as 30 seconds to 1 minute, fraudsters can produce highly realistic voice clones.


# Technology Prevention: New Telecommunications Network Fraud


[Dingxiang Defense Cloud Business Security Intelligence Center has released a special report titled "Deepfake Threat Research and Security Strategies,"](https://www.aisecurius.com/blog/article/55) systematically detailing the industrial chain behind new telecommunications network fraud based on "Deepfake."

To prevent "Deepfake" new telecommunications network fraud, it is necessary to verify offline when suspicious points are discovered online, increase communication time, and adopt probing measures, such as requesting the other party to perform specific actions, to expose potential loopholes. Additionally, it is recommended that enterprises employ multiple technologies and methods. Furthermore, promoting the positive application of AI technology and rigorously cracking down on criminal activities are fundamental solution
### 1. Identification of "Deepfake" fraudulent videos: 
deos: During video chats, one can request the other party to press their nose or face to observe facial changes; pressing a real nose will cause deformation. Alternatively, requesting actions like eating or drinking to observe facial changes can be effective. Asking for unusual gestures or expressions, such as waving or difficult hand gestures, can also help distinguish authenticity. During waving, facial data interference occurs, causing slight tremors, flickers, or abnormal situatio
### 
2. Comparison and identification of device information, geographical location, and behavioral operations
ations: Dingxiang Device Fingerprinting distinguishes between legitimate users and potential fraudulent behavior by recording and comparing Device Fingerprinting. This technology uniquely identifies and recognizes each device, detecting manipulated devices like virtual machines, proxy servers, and emulators. It analyzes behaviors such as multiple account logins, frequent IP address changes, and device attribute changes that deviate from user habits, aiding in tracking and identifying fraudulent activitie
### 3. Recognition of abnormal account activities: 
ties: Increased verification is required for activities such as remote logins, device changes, phone number changes, or suddenly active dormant accounts. Continuous identity verification during sessions is crucial to maintain consistent user identity. Dingxiang atbCAPTCHA accurately differentiates between human operators and machines, precisely identifying fraudulent behaviors, and provides real-time monitoring and interception of abnormal activities.

![2024062705.png](https://cdn.aisecurius.com/images/e06/e06b5696-4cde-4099-bdfe-b33389a1def8.png)


### 4. Prevention of fraudulent "Deepfake" videos and images
: Dingxiang's comprehensive panoramic facial security threat perception solution employs multi-dimensional verification of device environments, facial information, image authenticity, user behavior, and interaction status. It quickly identifies injected attacks, live body forgeries, image forgeries, camera hijacking, debugging risks, memory tampering, Root/Jailbreak, malicious ROMs, simulators, and over 30 other types of malicious behaviors. Upon detecting forged videos, fake facial images, or abnormal interaction behaviors, it automatically blocks operations. The solution offers flexible configuration of video verification strength and user friendliness, implementing a dynamic mechanism for enhanced verification of abnormal users while employing atbCAPTCHA for normal users.


### 5. Unearthing potential fraud threats:
 Dingxiang Dinsight aids enterprises in risk assessment, anti-fraud analysis, and real-time monitoring, enhancing risk control efficiency and accuracy. Dinsight's average processing speed for daily risk control strategies is within 100 milliseconds, supporting configuration-based access and deposition of multi-source data. Leveraging mature indicators, strategies, models, and deep learning technology, it achieves self-monitoring and self-iterative mechanisms for risk control performance. Paired with the Xintell intelligent model platform, it optimizes security strategies automatically for known risks and configures support for different scenarios based on risk control logs and potential risk mining. Standardizing complex data processing, mining, and machine learning processes based on associative networks and deep learning technology, it provides end-to-end modeling services from data processing, feature derivation, model construction to final model deployment.

New Types of Telecommunications and Network Fraud: Four Types of People that Fraudsters Like to Impersonate

Ms. Li, a resident of Ordos City, Inner Mongolia, recently fell victim to a new telecommunications fraud scheme. 

Ms. Li, a resident of Ordos City, Inner Mongolia, recently fell victim to a new telecommunications fraud scheme. Fraudsters utilized DEEPFAKE technology to synthesize a video call resembling her old classmate, which became a crucial tool in deceiving Ms. Li's trust.

![2024062501.png](https://cdn.aisecurius.com/images/22b/22b8287a-1b65-44fb-97dc-74fd64cea3a7.png)

The impostor pretended to be Ms. Li's old classmate "Jia," establishing contact via WeChat and QQ platforms and using DEEPFAKE technology to forge a video call, successfully gaining Ms. Li's trust.

Ms. Li received a friend request on WeChat from an account with the nickname and profile picture matching her old classmate "Jia." After briefly confirming the identity through a QQ video call, the fraudsters claimed they needed money urgently and asked Ms. Li to transfer funds, providing a fake bank transfer screenshot. Without verifying the receipt, Ms. Li transferred 400,000 RMB into the fraudster's account. When they demanded more transfers, Ms. Li realized she might be deceived, immediately contacting her real old classmate and reporting the incident to the police.

![2024062503.png](https://cdn.aisecurius.com/images/621/6211a325-5eda-45f1-8b32-a56a16094924.png)

The Ordos police swiftly activated emergency measures to freeze the transaction and successfully returned the 400,000 RMB to Ms. Li. Through investigation, it was found that the WeChat account in question was linked to a person named Liu, who had registered 12 WeChat accounts under his name, engaging in suspicious activities. Liu eventually admitted to renting out his WeChat account to the fraudsters after seeing an opportunity in a QQ part-time job group about "earning money by renting out verified social media accounts."
![2024062502.png](https://cdn.aisecurius.com/images/5f5/5f54afe7-136d-4ce9-9ecc-f8fd89bbdc7b.png)
Mr. Li from Hebei also encountered a DEEPFAKE-based new telecommunications fraud case, where fraudsters induced him to engage in explicit conversations via a dating app, leading to the download of illegal chat software. They used AI technology to fabricate a pornographic video and extorted 120,000 RMB from him.


# Frauds Utilizing DEEPFAKE Technology


These incidents underscore the application of DEEPFAKE technology in fraudulent activities and the murky gray industry behind it.

Firstly, fraudsters target individuals on dating platforms using tactics like explicit conversations to lure victims into downloading illegal chat software, thereby illegally obtaining their contact lists, photo libraries, and other private information.

Secondly, DEEPFAKE technology captures facial features of victims through software, combining AI synthesis techniques to create fabricated video content.

Lastly, fraudsters utilize custom apps to distribute these videos, carrying out extortion and fraud.

The new telecommunications fraud has a complete industrial chain: upstream involves illegally obtaining citizens' private information, midstream utilizes custom scripts for fraud, and downstream involves money laundering and cashing out. Citizens' personal information is priced and sold on the dark web, with illegal links, QR codes, and apps becoming primary means of information extraction.


# Technical Measures to Prevent New Telecommunications Fraud Using DEEPFAKE


[Dingxiang Defense Cloud Business Security Intelligence Center has released an Intelligence Special Issue titled "DEEPFAKE Threat Research and Security Strategies," systematically outlining the industrial chain behind new telecommunications fraud based on DEEPFAKE technology.](https://www.aisecurius.com/blog/article/55)

To prevent DEEPFAKE-based new telecommunications fraud, it is essential to detect suspicious activities online and verify offline, increase communication time, and take probing measures such as requesting specific actions from the other party to expose potential loopholes. Additionally, it is recommended that enterprises adopt multiple technologies and methods. Furthermore, promoting the positive application of AI technology and harshly cracking down on criminal activities are fundamental solutions.


## 1. Identification of DEEPFAKE fraudulent videos: 
During video chats, request the other party to press their nose or face to observe facial changes. A real person's nose will deform when pressed. Alternatively, ask them to eat or drink and observe facial changes. Requesting unusual actions or expressions, such as waving or performing difficult gestures, can also help distinguish authenticity. These actions may cause facial data interference, resulting in slight tremors, flickers, or other anomalies during waving.


## 2. Comparison and identification of device information, geolocation, and behavioral operations can detect and prevent abnormal activities
. Dingxiang Device Fingerprinting identifies legitimate users and potential fraudulent behaviors by recording and comparing device fingerprints. This technology uniquely identifies each device, distinguishing manipulated devices like virtual machines, proxy servers, and emulators. It analyzes behaviors such as multiple account logins, frequent IP address changes, and device attribute changes that deviate from normal user habits, aiding in tracking and identifying fraudsters' activities.


## 3. Enhanced verification for actions such as remote login from different locations, device changes, 
phone number changes, and sudden activity spikes is crucial. Continuous identity verification during sessions is vital to ensure consistency throughout user interactions. Dingxiang atbCAPTCHA accurately distinguishes between human operators and machines, precisely identifying fraudulent behaviors, and monitors and intercepts abnormal activities in real-time.

![2024062705.png](https://cdn.aisecurius.com/images/e06/e06b5696-4cde-4099-bdfe-b33389a1def8.png)
## 4. AI technology and human verification systems for facial anti-fraud are employed to prevent DEEPFAKE fake videos and images.
 Dingxiang's comprehensive facial security threat perception solution employs multi-dimensional verification based on device environment, facial information, image authentication, user behavior, and interaction status. It swiftly identifies various malicious activities such as injection attacks, live forgery, image forgery, camera hijacking, debugging risks, memory tampering, Root/jailbreak, malicious ROMs, and emulators, automatically blocking operations upon detecting fabricated videos, fake facial images, or anomalous interaction behaviors. The system can dynamically adjust video verification strength and user-friendliness, implementing a mechanism for enhanced verification of abnormal users while employing atbCAPTCHA for regular users.


## 5. Unearthing potential fraud threats and thwarting complex DEEPFAKE attacks is achieved through 
Dingxiang Dinsight, which aids enterprises in risk assessment, anti-fraud analysis, and real-time monitoring to enhance risk control efficiency and accuracy. Dinsight processes daily risk control strategies in less than 100 milliseconds, supports configurable access and deposition of multi-party data, and optimizes security policies automatically based on known risks using risk logs and data mining. The Xintell intelligent model platform, in conjunction with Dinsight, enables automatic optimization of security strategies for various scenarios based on mature indicators, strategies, models, and deep learning technology, standardizing complex data processing, mining, and machine learning processes from data handling and feature derivation to model construction and deployment.


Continuous Fraud Cases! Rampant New Telecommunications Fraud Based on DEEPFAKE Technology

Facing these challenges, e-commerce platforms and regulatory authorities need to take more effective measures. On one hand, 

This year's "618" results are impressive. Data shows that mainstream e-commerce platforms have achieved remarkable performances, reflecting not only consumers' trust and support for these platforms but also their comprehensive upgrades in marketing strategies, supply chain management, and logistics. Livestream e-commerce, as a bridge connecting consumers with brands and products, has increasingly highlighted its role, making information exchange between market supply and demand more transparent and efficient.

With the rapid development of the e-commerce industry, issues related to illicit and gray practices are becoming increasingly prominent. During large-scale promotional events like "618," e-commerce platforms have had to relax risk control rules moderately to ensure user experience and system stability, inadvertently providing opportunities for illicit activities. Exploiting new technologies and methods, illicit actors launch attacks in more covert and sophisticated ways, posing significant challenges to both e-commerce platforms and consumers.

![2024062001.jpg](https://cdn.aisecurius.com/images/07c/07c28720-c4b9-4401-a009-a7ba145c71de.jpg)

Facing these challenges, e-commerce platforms and regulatory authorities need to take more effective measures. On one hand, e-commerce platforms need to strengthen technical defenses, enhance risk control capabilities, and promptly detect and intercept fraudulent activities. On the other hand, regulatory authorities need to intensify efforts against illicit practices, enforce laws to punish illegal behaviors, and maintain market order.


# Woolfarming Hidden in Livestream Rooms


During a liquor e-commerce livestream on May 20th, the host achieved sales exceeding 100 million yuan in just 43 minutes, selling 30,000 bottles of liquor. However, these "flash sale" special-priced goods have become channels for some "woolfarmers" to mass purchase. They infiltrate livestream rooms, use illegal plug-in software to snatch "flash sale" and "limited purchase" products, and then resell them at inflated prices, profiting immensely.

These "flash sale" special-priced goods have become channels for some "woolfarmers" to mass purchase. They infiltrate livestream rooms, use illegal plug-in software to snatch "flash sale" and "limited purchase" products, and then resell them at inflated prices, profiting immensely. "Woolfarmers" in e-commerce livestream rooms illegally profit through technical means, causing significant losses not only to e-commerce platforms but also disrupting the shopping experiences of ordinary consumers.
![2024061403.jpg](https://cdn.aisecurius.com/images/a39/a39c2f17-0df4-476a-a382-eb2e5d084965.jpg)

During shopping festivals, countless consumers eagerly anticipate purchasing items, yet many discounted or special-priced products always appear as "sold out" or "temporarily out of stock." What's the reason behind this? It turns out many popular, limited-supply, or high-value-for-money items are snatched away by woolfarmers.

Woolfarmers engaging in activities like grabbing coupons and snatching special-priced goods not only harm the legitimate interests of consumers but also cause major economic losses to consumers, merchants, and e-commerce platforms. Statistics show that 70% to 80% of e-commerce enterprises' marketing expenses are consumed by woolfarmers, resulting in annual losses amounting to hundreds of billions of yuan.


# Merchant Order Fakery Misleading Consumers


"If you fake orders, the worst that could happen is getting caught and banned by the platform; however, if you don't fake orders, you'll likely be starved to death by other fake order sellers or major sellers on the platform, or even starved to death on the platform," said a merchant on a livestreaming platform.

Based on the rules of livestream e-commerce platforms, when buyers search for a product using keywords on the platform, the e-commerce platform calculates the weight of all pages related to that product and presents them to the buyer in order of relevance. The higher a product ranks, the higher its exposure and click-through rates, leading to higher potential orders. Conversely, products that rank lower are rarely seen by buyers and thus have minimal chances of making sales.

There are hundreds, even thousands of indicators that affect the ranking of a product, with the three most important indicators being sales volume, conversion rate, and positive review rate. While this algorithm saves buyers a lot of time in choosing and comparing products, it has triggered a vicious cycle among merchants where "products ranking higher sell better, while products ranking lower sell worse." Therefore, to improve product rankings, many merchants have started focusing on these three indicators, giving rise to the practice of "faking orders."

Analysis by Dingxiang Defense Cloud Business Security Intelligence Center has found that e-commerce order fakery mainly occurs through software-based and human-based methods. Those involved in executing order fakery tasks in the gray market can earn commissions from merchants and profit from selling order faking cheating software.


## Software-based fakery involves account manipulation;
 gray market entities have a large number of e-commerce platform accounts. These accounts are obtained through mass registration on one hand and through illegal means such as the dark web or illegal methods like database breaches or brute-force attacks on already registered user accounts.

Upon receiving orders from merchants, gray market entities use cheating software to fake orders. This software can handle all operations from registration to purchase, including mass registration, intelligent account management, bulk linking of delivery addresses, bulk linking of credit cards, enabling two-step verification, direct reviews, leaving reviews, liking, adding to wish lists, adding to cart, and clearing cart, etc. Depending on the merchant's requirements, cheating software can simulate human behaviors to the maximum extent possible by randomly setting browsing paths, clicking on advertisements, answering questions, clicking on images, etc.
![2024061204.jpg](https://cdn.aisecurius.com/images/6cc/6cc16e27-c900-4a6e-96c1-0e0a813ac17c.jpg)

Cheating software from the gray market supports multiple accounts logging into a single device, isolates cookie data, modifies browser fingerprints, and assigns a foreign proxy IP address based on the logged-in account, ensuring each faked order account operates within a virtual "independent environment."


## Human-based crowdsourcing involves mobilizing real accounts to fake orders,
 with accounts not controlled by the gray market but operated by real individuals.

After a merchant issues an order fakery request, gray market entities immediately send product links in "order faking groups" and arrange batches of accounts to buy the merchant's products. In the process of faking orders, specific operational standards are set for order faking accounts to circumvent e-commerce platform risk control monitoring. For example, they search for keywords provided by the merchant on the e-commerce platform, randomly browse any product for 3 to 5 minutes, then find the target store's product needed for the faked order based on the merchant's information. After opening the designated product page provided by the merchant, they continue to browse the product details for 3 to 5 minutes before proceeding to place the order. These operations make the faked order appear as if it were a genuine customer's actual purchase behavior of "comparing products from three stores."

According to the merchant's requirements for shopping reviews and confirmation, the merchant must pay a certain percentage of "commission" to the gray market and provide "rebates" to the faked order account. There are various ways to provide rebates. Some gray market entities require merchants to directly "refund" to the faked order account after confirming the order.


# Merchant Order Fakery Misleading Consumers


"If you fake orders, the worst that could happen is getting caught and banned by the platform; however, if you don't fake orders, you'll likely be starved to death by other fake order sellers or major sellers on the platform, or even starved to death on the platform," said a merchant on a livestreaming platform.

Based on the rules of livestream e-commerce platforms, when buyers search for a product using keywords on the platform, the e-commerce platform calculates the weight of all pages related to that product and presents them to the buyer in order of relevance. The higher a product ranks, the higher its exposure and click-through rates, leading to higher potential orders. Conversely, products that rank lower are rarely seen by buyers and thus have minimal chances of making sales.

![2024061404.jpg](https://cdn.aisecurius.com/images/554/554fa25e-1802-4b68-85a8-0d29cf11a1f0.jpg)

There are hundreds, even thousands of indicators that affect the ranking of a product, with the three most important indicators being sales volume, conversion rate, and positive review rate. While this algorithm saves buyers a lot of time in choosing and comparing products, it has triggered a vicious cycle among merchants where "products ranking higher sell better, while products ranking lower sell worse." Therefore, to improve product rankings, many merchants have started focusing on these three indicators, giving rise to the practice of "faking orders."

Analysis by Dingxiang Defense Cloud Business Security Intelligence Center has found that e-commerce order fakery mainly occurs through software-based and human-based methods. Those involved in executing order fakery tasks in the gray market can earn commissions from merchants and profit from selling order faking cheating software.

Software-based fakery involves account manipulation; gray market entities have a large number of e-commerce platform accounts. These accounts are obtained through mass registration on one hand and through illegal means such as the dark web or illegal methods like database breaches or brute-force attacks on already registered user accounts.

Upon receiving orders from merchants, gray market entities use cheating software to fake orders. This software can handle all operations from registration to purchase, including mass registration, intelligent account management, bulk linking of delivery addresses, bulk linking of credit cards, enabling two-step verification, direct reviews, leaving reviews, liking, adding to wish lists, adding to cart, and clearing cart, etc. Depending on the merchant's requirements, cheating software can simulate human behaviors to the maximum extent possible by randomly setting browsing paths, clicking on advertisements, answering questions, clicking on images, etc.

Cheating software from the gray market supports multiple accounts logging into a single device, isolates cookie data, modifies browser fingerprints, and assigns a foreign proxy IP address based on the logged-in account, ensuring each faked order account operates within a virtual "independent environment."

Human-based crowdsourcing involves mobilizing real accounts to fake orders, with accounts not controlled by the gray market but operated by real individuals.

After a merchant issues an order fakery request, gray market entities immediately send product links in "order faking groups" and arrange batches of accounts to buy the merchant's products. In the process of faking orders, specific operational standards are set for order faking accounts to circumvent e-commerce platform risk control monitoring. For example, they search for keywords provided by the merchant on the e-commerce platform, randomly browse any product for 3 to 5 minutes, then find the target store's product needed for the faked order based on the merchant's information. After opening the designated product page provided by the merchant, they continue to browse the product details for 3 to 5 minutes before proceeding to place the order. These operations make the faked order appear as if it were a genuine customer's actual purchase behavior of "comparing products from three stores."

According to the merchant's requirements for shopping reviews and confirmation, the merchant must pay a certain percentage of "commission" to the gray market and provide "rebates" to the faked order account. There are various ways to provide rebates. Some gray market entities require merchants to directly "refund" to the faked order account after confirming the order.


# Preventing Various Types of Fraud on Platforms


Based on the cheating and fraud in livestream rooms, Dingxiang Defense Cloud Business Security Intelligence Center recommends integrating multiple product combinations on the business side for further control to ensure the normal order of livestream platforms.

Ensuring Client-Side Security. Dingxiang's Adaptive App Shielding, based on graph neural network technology, deeply analyzes and extracts code features. It automatically selects suitable methods for obfuscation based on the characteristics of different code blocks, significantly increasing the difficulty of reverse analysis and effectively reducing computational performance consumption by 50%. Through encryption and obfuscation engines, it enhances the security of App code, effectively preventing attacks such as cracking, copying, and repackaging by attackers.

Identification of Fraudulent Accounts. Dingxiang atbCAPTCHA, powered by AIGC technology, prevents AI brute-force cracking, automated attacks, phishing attacks, and other threats. It effectively prevents unauthorized access, account hijacking, and malicious operations, thereby protecting system stability. It integrates 13 verification methods and multiple control strategies, aggregating 4380 risk policies, 112 risk categories, covering 24 industries and 118 risk types. Its precision in risk control reaches up to 99.9%, with rapid transformation from risk to intelligence. It supports seamless authentication for secure users and achieves real-time response and disposal within 60 seconds, further enhancing user login service experience in terms of convenience and efficiency.

Identification of Fraudulent Devices. Dingxiang Device Fingerprinting integrates internal information of multi-platform devices to generate unified and unique Device Fingerprinting for each device. It builds multidimensional identification strategy models based on device, environment, and behavior to detect risks such as virtual machines, proxy servers, and emulators manipulated maliciously. It analyzes anomalies such as multiple account logins, frequent IP address changes, and device attribute changes that do not align with user habits, tracks and identifies fraudulent activities, and helps enterprises achieve unified operation of the same ID across all scenarios and channels, facilitating cross-channel risk identification and control.

Uncovering Potential Fraud Threats. Dingxiang Dinsight assists enterprises in risk assessment, anti-fraud analysis, and real-time monitoring, enhancing the efficiency and accuracy of risk control. Dinsight processes daily risk control strategy at an average speed of less than 100 milliseconds, supporting configurable access and sedimentation of multi-party data. It leverages mature indicators, strategies, and model experience reserves, as well as deep learning technology, to achieve self-monitoring and self-iteration mechanisms for risk control. Paired with the Xintell intelligent model platform, it optimizes security strategies automatically for known risks and configures support for risk control strategies across different scenarios based on risk control logs and data mining of potential risks. It standardizes complex processes of data processing, mining, and machine learning based on associative networks and deep learning technology, providing a one-stop modeling service from data processing, feature derivation, model construction to final model deployment.


Inventory: Fraudulent Practices in Livestream E-commerce

Following the conclusion of the mid-year promotion "618," concerns about the false prosperity behind livestreaming e-commerce are gradually emerging. 

Following the conclusion of the mid-year promotion "618," concerns about the false prosperity behind livestreaming e-commerce are gradually emerging. Many consumers have reported that the "popular items" in livestream rooms often have questionable quality, with some rooms using tools to artificially inflate traffic and positive reviews, creating a false impression of prosperity that misleads consumers into making purchases.
![2024062001.jpg](https://cdn.aisecurius.com/images/07c/07c28720-c4b9-4401-a009-a7ba145c71de.jpg)


Liu Tian (pseudonym), a Beijing office worker, expressed doubts about the authenticity of positive reviews due to the repetitive nature of comments and lack of genuine interaction in livestream rooms. Meanwhile, Li Xia (pseudonym), a "mommy" from Shanghai, encountered significant discrepancies between clothing purchased from livestreams and their descriptions. Media investigations have uncovered instances of abnormal data spikes in certain livestream rooms, where viewership surged from 20 to tens of thousands within just five minutes, inundating the barrage with irrelevant information while genuine inquiries about the products were scant. Even more shocking was a beauty device that purportedly sold over ten thousand units during the livestream but plummeted to just over one thousand after it ended.

Insiders revealed that some top livestreamers spend hundreds of thousands to boost their popularity, and livestream rooms with return rates exceeding 60% likely engage in data falsification.

Livestream viewership boosting is widespread, primarily achieved through two methods: automated software (botting) and manual group control. Bots simulate app interactions via computer programs to create artificial popularity, but these methods are increasingly thwarted as e-commerce platforms enhance their defenses. Gray market actors have shifted to using group control devices, operating multiple smartphones simultaneously to automate likes, follows, scripted messages, and other actions to fabricate the illusion of thriving livestream rooms.

Media outlets have contacted a water army team that offers services such as inflating livestream viewership, interactive comments, livestream donations, and even inflating product sales figures. The team leader claimed their accounts can mimic real users, making them difficult to distinguish. To create authentic livestream interactions, the water army team engages in scripted interactions based on questions and phrases provided by the livestreamers. Additionally, they provide services for maliciously reporting other livestream rooms and fabricating orders to boost sales.

The State Administration for Market Regulation recently published the "Interim Provisions on the Prohibition of Unfair Competition on the Internet," Article 9 of which specifically prohibits operators from engaging in false transactions, fictitious transaction volumes, fabricated user reviews, and falsified traffic data to ensure consumers can obtain genuine and accurate product information.


# Livestream Platforms: How to Identify Cheating in View Count


In response to cheating in livestream view counts, Dingxiang Defense Cloud Business Security Intelligence Center recommends deploying a combination of products on the business side for further control, ensuring the normal operation of livestream platforms.


## Ensuring Client-Side Security.
 Dingxiang's adaptive App shielding, based on graph neural network technology, enhances code obfuscation by analyzing and extracting code features deeply. It automatically selects appropriate methods for obfuscation based on different code characteristics, significantly increasing the difficulty of reverse engineering and effectively reducing computational consumption by 50%. Through encryption and obfuscation engines, it enhances the security of App code, preventing attacks such as cracking, duplication, and repackaging.


## Identification of View Count Manipulation Accounts. 
Dingxiang's atbCAPTCHA, employing AIGC technology, prevents AI-driven brute force attacks, automated assaults, and phishing threats. It safeguards against unauthorized access, account hijacking, and malicious operations, thereby ensuring system stability. It integrates 13 verification methods and multiple control strategies, encompassing 4380 risk policies, 112 risk categories, covering 24 industries and 118 risk types. It achieves precision in risk control up to 99.9% and swiftly transforms risks into actionable intelligence. It supports seamless authentication for secure users and offers real-time response capabilities within 60 seconds, further enhancing user login service convenience and efficiency.


## Recognition of View Count Manipulation Devices. 
Dingxiang Device Fingerprinting establishes unified and unique device fingerprints by internally integrating multi-device information. It constructs multidimensional recognition strategies based on device, environment, and behavior to identify risks such as virtual machines, proxy servers, and emulators manipulated maliciously. It analyzes abnormal behaviors such as multiple account logins, frequent IP address changes, and device attribute alterations that deviate from user habits, tracks and identifies fraudulent activities, and aids enterprises in unified operations under the same ID across all scenarios and channels, facilitating cross-channel risk identification and management.


## Detection of Potential Fraud Threats.
 Dingxiang Dinsight real-time risk control engine assists enterprises in risk assessment, anti-fraud analysis, and real-time monitoring, enhancing the efficiency and accuracy of risk control. Dinsight achieves an average processing speed of daily risk control strategies within 100 milliseconds, supports configurable access and deposition of multi-source data, and leverages mature indicators, strategies, models, and deep learning technology for self-monitoring and iterative enhancement of risk control mechanisms. Combined with the Xintell intelligent model platform, it optimizes security strategies automatically for known risks, configures support for risk control strategies across different scenarios based on risk control logs and data mining of potential risks. Standardizing complex processes of data handling, feature derivation, model building, and final model deployment, it provides an integrated modeling service from data processing to model deployment.


After 618: Consumers realize that the popularity of livestreaming e-commerce is "boosted" artificially

E-commerce shopping festivals are not only a feast for consumers but also a honey pot for black and grey industries. On the one hand, during major events like 618

E-commerce shopping festivals are not only a feast for consumers but also a honey pot for black and grey industries. On the one hand, during major events like 618, many e-commerce platforms lower their business security sensitivity and relax some risk control rules to avoid impacting customer experience and ensure stable business operations. On the other hand, black and grey industries employ new fraudulent methods in increasingly covert and sophisticated ways to maximize profits.
![2024061204.jpg](https://cdn.aisecurius.com/images/6cc/6cc16e27-c900-4a6e-96c1-0e0a813ac17c.jpg)
According to data previously released by Dingxiang Defense Cloud Business Security Intelligence Center, the highest risks occur during shopping events like Double 11 and 618, with November accounting for 39.72% and June for 20.69%.

The marketing cycle for e-commerce platforms spans from half a month to a month before the frenzy day, with marketing investments lasting 1-2 months. This ample time allows black and grey industries to study the processes and rules of various e-commerce platforms in preparation for subsequent fraudulent activities. Popular products and promotions become means for them to profit.


# Coupon farming: Popular products taken away by merchants


During shopping festivals, countless consumers eagerly anticipate, yet many discounted or special-priced items often show "sold out" or "temporarily out of stock". What causes this? It turns out that many popular, limited-sale, or high-value products are snapped up by coupon farmers.

Actions like coupon clipping and grabbing flash sale items not only harm the legitimate interests of consumers but also lead to significant economic losses for consumers, merchants, and e-commerce platforms. Data shows that 70%-80% of e-commerce enterprises' marketing expenses are consumed by coupon farmers, resulting in annual losses amounting to hundreds of billions.

Coupon farmers engage in activities such as grabbing coupons, seizing flash sale items, and registering fake users, causing significant economic losses to consumers, merchants, and e-commerce platforms.

Bulk registration of fake accounts. Registration is a key process for creating an account. Black and grey industries utilize registration tools for automated bulk account creation, registering hundreds to tens of thousands of accounts to facilitate mass purchases and hoarding.

Using group control to manipulate accounts. Black and grey industries utilize group control to operate tens, hundreds, or even thousands of devices from a single computer, performing unified actions such as registration, login, ordering, and purchasing. Group control also offers functions like simulated location, shake-to-refresh, bulk contact list import, and message pushing.

Forging IP locations. An IP address represents a user's network information when online. Black and grey industries use tools for rapid IP dialing, automatically calling dynamic IP addresses across regions, with features like automatic switching, reconnecting after disconnection, automatic browser cookie cache clearance, and virtual network card information, seamlessly switching between domestic and international IPs.

Forging GPS locations. GPS positioning indicates a user's geographical location during online service use. Black and grey industries utilize simulation software and third-party tools to change latitude and longitude, enabling instant "teleportation" to any location.

Forging device attributes. Device attributes like model, serial number, and IMEI are unique identifiers. Black and grey industries use device modification tools to intercept device interfaces at the system level. When applications call these interfaces to retrieve device parameters, they receive attributes forged by the modification tool. Typically, it takes 2-3 minutes for such tools to modify attributes for 1000 devices.

## # How merchants can prevent coupon farming


Business risk fraud constantly evolves with rapid changes in methods. New attack methods adjust existing preventive measures or render them immune. Traditional measures often fail to timely identify and alert new risks, and contradictions among various e-commerce platform security processes sometimes lead to direct impacts on order transactions and store operations.

![2024061205.png](https://cdn.aisecurius.com/images/215/215a050f-5ce6-4a3b-bba0-8981b3a891a8.png)

Identify and alert risk IP addresses. Integrate an IP risk database to match user-associated IPs for risk identification, identifying risks like proxies and rapid dialing IPs, and intercepting malicious IP addresses.

Identify and alert risk devices. Assess whether client device fingerprinting is legitimate, detecting risks like injection, hooking, and simulation software. Quickly recognize risks associated with rooted devices, jailbroken devices, or devices hijacked for injection. Rapidly identify abnormal behaviors such as repeated activations from the same device, abnormal IP clustering within a short time, unusual proportions of old device models or operating systems within the same channel.

Identify and intercept risky accounts. Analyze verification environment information and tokens during validation completion to promptly detect anomalies and risky operations. Monitor abnormal account behaviors. Deploy strategies based on user behavior, focusing on accounts that frequently switch from the same device during order initiation.

Analyze and forecast future behavioral risk changes. Establish and maintain a dynamic operation mechanism for local lists based on registration, login, and activation data, accumulating and maintaining corresponding black and white list data dimensions such as user ID, phone number, and device. After accumulating online data, use risk control and business accumulation data to model registration, login, ordering, and purchasing behaviors, directly incorporating model outputs into risk control strategies.


# Advertising fraud: causing colossal promotional efforts by e-commerce platforms to go down the drain


During online shopping festivals, e-commerce platforms heavily invest in advertising and native advertising to attract more consumers. Unscrupulous individuals exploit various methods such as ad clicking and falsifying promotional data to reap benefits. On one hand, after advertising campaigns, stakeholders only see corresponding click-through rates, activation rates, and registration rates, with very low retention rates. On the other hand, the data includes a large amount of fake traffic with no conversions from real users, and efforts to analyze failed conversions further cannot pinpoint the true reasons. This not only wastes advertising costs, time, and development expenses but also causes substantial asset losses and a plethora of valueless fake users for advertisers.

Analysis by Dingxiang Defense Cloud Business Security Intelligence Center indicates that perpetrators engage in e-commerce advertising fraud mainly through three forms: manual brushing, machine brushing, and wall brushing.

Manual brushing involves using real devices to manipulate key parameters like IMEI, phone numbers, IMSI, MAC addresses, etc., under human operation, to simulate multiple user downloads, activations, registrations, and usage behaviors for cheating traffic.

Machine brushing entails attackers using a large number of phones to repeatedly change their configurations, masquerading as new devices to repetitively "install-activate-delete-install-activate-delete," thereby inflating activation figures. These devices typically exhibit root access, emulator use, VPNs, abnormal charging states, GPS information, and SIM card behaviors, which can be detected and mitigated by combining device information, tracking user behavior, retention rates, and online durations.

Wall brushing involves genuine human and device operations where users on such platforms are real smartphone users. Although many users uninstall the app after completing tasks, there remains a certain conversion rate. Therefore, many advertisers still opt for wall brushing as a promotional method.

Because of actual rewards, users will go to great lengths to earn rewards through wall brushing, fostering fraudulent behaviors to exploit promotion fees. Some users manipulate batches of fake accounts to complete tasks fraudulently, resulting in heavily adulterated promotional trial data and causing significant losses to advertisers.


## How e-commerce platforms can prevent advertising fraud


Based on various advertising fraud methods, Dingxiang Defense Cloud Business Security Intelligence Center recommends that advertisers reinforce terminal protection while leveraging business-side combinations to further safeguard promotional effectiveness.

Ensure client-side security to prevent hijacking by black and grey industries. Dingxiang's adaptive App shielding employs graph neural network technology to analyze and extract code characteristics, automatically selecting suitable methods for obfuscation based on different code blocks to significantly increase the difficulty of reverse engineering, effectively reducing computational performance consumption by 50%. Through encryption and obfuscation engines, it enhances App code security, effectively preventing attacks such as cracking, duplication, and repackaging.

Secure account safety to prevent account theft. Dingxiang's atbCAPTCHA, utilizing AIGC technology, prevents AI-based brute force, automation attacks, and phishing threats, ensuring unauthorized access prevention, account protection from theft, and malicious operations to maintain system stability. It integrates 13 verification methods and multiple prevention strategies, encompassing 4380 risk policies, 112 risk intelligence categories, covering 24 industries and 118 risk types. Its precision in risk control reaches 99.9%, with rapid transformation from risk to intelligence. It supports seamless security verification for users while achieving response times within 60 seconds, further enhancing user login service experience convenience and efficiency.

Identify forged devices to prevent various malicious abnormal behaviors. Dingxiang's Device Fingerprinting integrates multiple device information to generate unified and unique Device Fingerprints for each device. It builds multi-dimensional identification strategy models based on devices, environments, and behaviors to recognize risks like virtual machines, proxy servers, and emulators manipulated maliciously. It analyzes device behavior anomalies such as multiple account logins, frequent IP address changes, and abnormal device attribute changes that do not match user habits, tracking and identifying fraudulent activities to assist enterprises in managing operations under the same ID across all scenarios and channels, enhancing cross-channel risk identification and management capabilities.

Uncover potential fraud threats to prevent various new fraud attacks. Dingxiang's Dinsight aids enterprises in risk assessment, anti-fraud analysis, and real-time monitoring to enhance risk control efficiency and accuracy. Dinsight processes daily risk control strategy with an average speed of less than 100 milliseconds, supporting configurable access and sedimentation of multi-source data. It uses mature metrics, strategies, and deep learning technology to automatically optimize security strategies against known risks, configuring anti-fraud strategies based on risk logs and data mining potential risks. Its standardized data processing, feature derivation, model building, and one-stop modeling service from data processing to final model deployment enhance overall security measures effectively.

Advertising fraud and coupon farming: making the promotion and marketing of e-commerce 618 futile

Behind the lively scene of live streaming sales, some criminals exploit technical means to illegally purchase "flash sale" bargain items from live streaming rooms and profit handsomely, s

With the commencement of the 618 online shopping festival, live streaming rooms on major e-commerce platforms once again captured consumer attention. In a liquor e-commerce live stream on May 20th, the host achieved sales exceeding 100 million yuan in just 43 minutes, selling 30,000 bottles of liquor. However, these "flash sale" bargain items have become a channel for some scalpers to bulk purchase. They infiltrate live streaming rooms, use illegal plug-in software to snap up "flash sale" and "limited purchase" products, then resell them at marked-up prices to make huge profits.

![2024061403.jpg](https://cdn.aisecurius.com/images/a39/a39c2f17-0df4-476a-a382-eb2e5d084965.jpg)

Behind the lively scene of live streaming sales, some criminals exploit technical means to illegally purchase "flash sale" bargain items from live streaming rooms and profit handsomely, seriously disrupting a fair shopping environment.


# Scalpers in Live Streaming Rooms


The police in Ningbo, Zhejiang Province, reported a case of illegal purchase and profiteering from "flash sale" goods, successfully arresting suspects Sheng and Zheng. The police confiscated 25 mobile phones used in the crime, 3 remote servers, and 2 computers, along with seizure of one set of snatch software and its source code.

![2024061404.jpg](https://cdn.aisecurius.com/images/554/554fa25e-1802-4b68-85a8-0d29cf11a1f0.jpg)

Investigation revealed that the suspects illegally cracked the security algorithms of e-commerce platforms and used artificial intelligence technology to develop purchase programs. These programs could simultaneously bulk purchase "limited purchase" items from different live streaming rooms and resell them at inflated prices for profit. For instance, they sold precious liquor purchased for 9.9 yuan or "welfare price" at market rates, earning over 1000 yuan profit per bottle; and high-end phones purchased at official guided prices were sold at market rates, yielding over 3000 yuan profit per unit.

The suspects set up multiple layers of stealth "barriers." They purchased hundreds of accounts and "cloud phones," used automated snatch programs to "flash sale" items before they officially opened for sale. This was achieved through methods such as changing proxy IPs, diversifying platform accounts, obscuring order addresses, frequently changing phone numbers, and using fictitious recipient names. As of December 20, 2023, Sheng and Zheng had sold over 570 bottles of precious liquor and over 650 high-end phones illegally, with profits nearing 1 million yuan.


# Anti-Fraud Measures: Targeted Solutions for E-commerce Platforms


"Scalpers" in e-commerce live streaming rooms exploit technical means for illegal profit, causing substantial losses to e-commerce platforms and undermining the shopping experience for ordinary consumers. E-commerce platforms need to enhance security measures and improve technological capabilities to prevent similar incidents. Dingxiang provides targeted identification and prevention products to address the use of various illegal techniques by suspected criminals.

Addressing the issue of "scalpers" frequently changing phone numbers and modifying IP addresses, Dingxiang Device Fingerprinting generates unique identifiers for user devices based on hardware, network, and environmental characteristics. This effectively identifies whether client Device Fingerprinting is legitimate, detecting risks such as emulators, device rooting, jailbreaking, and injection hijacking, enabling effective monitoring and early warning. Additionally, monitoring abnormal behaviors such as multiple activations from the same device, anomalous IP associations with the same device, and disproportionate usage of older device models within the same channel further enhances risk identification and interception capabilities.

To tackle the problem of "scalpers" engaging in bulk registration and snap-up purchases, Dingxiang atbCAPTCHA provides an effective means to distinguish between human users and automated bots. It verifies and determines in real-time behaviors such as bulk registration, login, claims, queries, and applications, enabling direct interception or secondary verification for suspicious accounts. This method effectively prevents automated purchase behaviors by "scalpers."

Further control measures are implemented against "scalpers" through comprehensive management of account activities. By leveraging registration, login, and activation data, coupled with black and white list data maintenance and risk control strategies, Dingxiang's Dinsight and Xintell intelligent model platforms employ data mining techniques for behavioral modeling. This includes analysis and prediction of behaviors related to registration, login, ordering, and purchasing, thereby supporting direct implementation of risk control strategies.

Additionally, consumers should remain vigilant to avoid becoming victims of "scalpers" seeking illicit profits. Only through collaborative efforts can a fair, trustworthy, and secure online shopping environment be cultivated.

"Scalpers" Hiding in Live Streaming Rooms, Invisible to Ordinary Consumers

With the rise of short videos and live streaming for sales, some criminals have begun to use this emerging platform for selling counterfeit goods. Recently, 

With the rise of short videos and live streaming for sales, some criminals have begun to use this emerging platform for selling counterfeit goods. Recently, the Baoshan police in Shanghai have successively cracked three cases involving counterfeit goods sold through "live streaming sales" in the "Sword 2024" series of actions. The involved goods range from cosmetics to sports products, and even to well-known brand handbags, with a total amount involved exceeding 100 million yuan.

![2024061211.jpg](https://cdn.aisecurius.com/images/fd6/fd6a4d4a-9c6b-4a07-9e5c-7c75e2098b52.jpg)

Case one: Counterfeit brand sunscreen live sales. At the end of 2023, Ms. Li purchased a sunscreen at a price much lower than the market price in a live streaming room of a well-known sales anchor, only to later discover that the product was counterfeit. After investigation by the Baoshan police, a criminal gang involved in the entire chain of counterfeiting and selling, led by Li and Lin, was discovered. This gang obtained distribution permits by purchasing genuine products, then contacted counterfeiters to produce counterfeit products, and sold them through live streaming sales by internet celebrities. Currently, the main suspects such as Li and Lin have been criminally detained according to law.

Case two: Counterfeit well-known brand badminton bags. In March of this year, the Baoshan police received reports from the public that a certain online store was selling counterfeit well-known brand badminton bags at prices lower than the market price. After three months of investigation, with the cooperation of the Guangdong police, the police successfully destroyed a criminal gang involved in manufacturing and selling counterfeit goods, led by Li, arresting more than 10 criminal suspects and seizing nearly 10,000 counterfeit products, involving an amount of over 10 million yuan. This gang set up underground counterfeit manufacturing bases in the outskirts of Guangzhou, selling counterfeit products at one-third the price of genuine ones through live streaming sales.

Case three: Live selling of counterfeit well-known brand leather bags. At the end of 2023, a well-known leather bag brand reported to the Baoshan police, stating that they had received numerous consumer complaints about product quality issues. After investigation by the Baoshan police, a counterfeiting gang led by Liu and more than 20 production, packaging, and other links involved in counterfeiting were identified. On April 11, the police launched a crackdown operation, dismantling more than 20 production bases and arresting more than 20 criminal suspects, involving an amount of over 50 million yuan. This gang purchased components and packaging materials from brand-authorized processing plants, manufactured products through scattered small workshops, and then sold them through live streaming sales at half the price of genuine products.


# Judicial case: Warning of related case judgments


Recently, the Beijing Internet Court concluded a contract dispute case arising from live streaming sales. Ms. Xia sued the sales anchor Ma and seller Company A to the court for issues such as inconsistent shipping locations, exaggerated sales volume, and false advertising related to the liquor purchased through a certain platform's live streaming room.

In the case, Ms. Xia purchased six bottles of liquor from Company A sold through Ma's live streaming room link, paying 1197 yuan. However, after receiving the goods, Ms. Xia found that the actual shipping location was inconsistent with the shipping location stated on the product details page, believing that Company A engaged in fraud. In addition, Ma repeatedly claimed in the live stream that "it's over after selling 1000 units," etc., which Ms. Xia believed to be fictitious and exaggerated sales volume. At the same time, Ma claimed that the market price of the liquor involved was six to seven hundred yuan per bottle, which was inconsistent with the actual market price, and Ms. Xia believed it to be false advertising.

![2024061210.jpg](https://cdn.aisecurius.com/images/618/618d3d09-aea9-4f65-8c3a-372cd6b0c624.jpg)

Ms. Xia requested the court to revoke the order in question, require Ma and Company A to refund the price, bear the return shipping costs of the goods, and provide triple compensation. After trial, the Beijing Internet Court found that the behavior of Ma and Company A constituted fraud and supported Ms. Xia's lawsuit.

The conclusion of this case highlights the issues of false advertising and fraud in live streaming sales, reminding consumers to enhance their legal awareness and safeguard their legitimate rights and interests while enjoying the convenience of live shopping. At the same time, it also raises higher requirements for the regulation and supervision of the live streaming sales industry, urging relevant companies and individuals to comply with laws and regulations and operate with integrity.


# Consumers: How to Protect Personal Rights


Consumers, while enjoying the convenience of shopping, also face new consumption risks. When encountering scams during live shopping, how can consumers efficiently protect their legal rights? Below is a detailed step-by-step guide.

Step One: Preserve Evidence. Once consumers find that the goods do not match the description in the live stream or they have not received the goods after payment, they should immediately preserve electronic evidence. This includes screenshots of the live stream product display, promises made by the host, screenshots of the payment process, transaction records, and chat records with customer service, among others. Detailed electronic evidence is crucial for successful rights protection.

Step Two: Contact the Operations Team and Customer Service. After discovering the problem, consumers should promptly contact the live streaming platform's operations team, host, or customer service to clearly state the issue and provide the collected evidence. If the issue cannot be satisfactorily resolved, consumers should immediately resort to the consumer protection system provided by the live streaming platform.

Step Three: Utilize Third-Party Payment Platforms. Many live shopping transactions are conducted through third-party payment platforms such as Alipay and WeChat Pay. Consumers can use the dispute resolution mechanisms provided by these platforms for rights protection. In fraudulent transactions, consumers may have the right to receive compensation in advance.

Step Four: Seek Assistance from Consumer Associations. If the amount involved is large or if the issue cannot be resolved through coordination, consumers can complain to local consumer associations or seek assistance from professional lawyers to prepare for litigation. The Consumer Rights Protection Law grants consumers core rights such as the right to know, the right to choose, and the right to fair transactions, which are also protected by law in live shopping.

Step Five: Authority Inspection and Legal Litigation. Taking the example of purchasing pure gold jewelry but receiving gold-plated products, consumers can submit the goods to authoritative quality inspection agencies for testing while conducting basic rights protection operations. They can obtain legally effective appraisal reports to serve as powerful evidence for rights protection.

Faced with fraudulent behavior in live shopping, consumers need to calmly and efficiently collect and preserve key evidence, lodge complaints and appeals through various channels such as live streaming platforms, third-party payment platforms, and consumer rights protection organizations. They should also take legal measures in a timely manner to actively protect their legitimate rights and interests. This not only protects consumers' own interests but also helps promote the healthy development of the entire live shopping industry.

Dingxiang Defense Cloud Business Security Intelligence Center reminds consumers to remain vigilant while enjoying the convenience and benefits brought by live streaming sales, avoiding purchasing counterfeit and inferior products. Consumers should purchase goods through formal channels and pay attention to details such as the price, packaging, and quality of the products during the purchasing process. If problems are discovered, they should immediately report them to the relevant authorities.


Live e-commerce: If you buy counterfeit goods, consumers should follow these five steps to claim compensation

maintain a fair market order, encourage innovation, protect the legitimate rights and interests of operators and consumers, and promote the sustained and healthy development of the digital economy.


Recently, the State Administration for Market Regulation of China announced the "Provisions on Combating Unfair Competition in Cyberspace (Interim)," which will take effect from September 1, 2024. These provisions aim to prevent and halt unfair competition behaviors in cyberspace, maintain a fair market order, encourage innovation, protect the legitimate rights and interests of operators and consumers, and promote the sustained and healthy development of the digital economy.

![2024060303.png](https://cdn.aisecurius.com/images/bc4/bc4e8788-31d3-4668-a17f-69455e665231.png)

The provisions target various unfair competition practices in the online market such as fake reviews, order brushing, e-commerce exclusivity, counterfeiting, false advertising, etc., and propose explicit prohibitions and regulatory measures.

Addressing the issues of fake reviews and order brushing, Article 9 of the provisions explicitly prohibits operators from engaging in activities like false transactions, fabricated transaction amounts, fabricated user reviews, fabricated traffic data, etc., to ensure consumers can access genuine and accurate product information.

Regarding counterfeiting and confusion tactics, Article 7 of the provisions prohibits operators from engaging in confusion behaviors, including unauthorized use of others' influential logos, domain names, page designs, etc., as well as producing and selling products that could easily be mistaken for those of others.

False advertising is also a focal point for crackdown. Article 8 of the provisions mandates that operators refrain from engaging in false or misleading commercial propaganda through websites, clients, live broadcasts, platform recommendations, etc., to avoid misleading consumers.

Addressing the issue of "e-commerce platform exclusivity," the provisions provide clear regulations. Article 24 stipulates that platform operators must not utilize means such as service agreements, transaction rules, etc., to impose unreasonable restrictions or attach unreasonable conditions on operators within the platform, including but not limited to forcing the signing of exclusive agreements, imposing unreasonable restrictions on product prices and sales conditions, etc.

The State Administration for Market Regulation has stated that the "Provisions on Combating Unfair Competition in Cyberspace (Interim)" are formulated based on laws and regulations such as the Anti-Unfair Competition Law and the E-commerce Law.


# Dingxiang: Providing Professional Anti-Fraud Solutions


Fake reviews and order brushing have long been persistent issues in the e-commerce industry, forming an entire industry chain. Attackers possess numerous accounts, continuously update professional tools, and employ new tactics, making it challenging to identify and prevent risks due to their complex origins. A misjudgment by platforms can directly impact user experience.

To effectively address these challenges, Dingxiang offers a comprehensive anti-fraud solution that employs dynamic strategies and deep protection. It not only identifies and prevents various types of fraudulent activities but also ensures that normal user experiences remain unaffected.


## 1. Identification and Early Warning of Risky IP Addresses:
 By integrating with an IP risk database, Dingxiang matches abnormal and suspicious accounts with risky IPs. By identifying risks such as proxies and rapid IP changes, the system intercepts malicious IP addresses, thereby preventing fraud at its source.

## 
2. Recognition and Early Warning of Risky Devices:
 Through Dingxiang Device Fingerprinting, the system conducts in-depth analysis of account device information. It identifies risky behaviors such as injection, hooking, and emulation, as well as suspicious patterns like multiple activations on the same device or abnormal IP associations. By generating unified and unique device fingerprints through cross-device information, Dingxiang tracks and identifies fraudulent activities, effectively warning against risky devices.

## 
3. Identification and Interception of Risky Accounts: 
Based on behavioral and device analysis, Dingxiang strategically deploys controls against suspicious accounts involved in order brushing and fake reviews. By leveraging Dingxiang atbCAPTCHA combined with AIGC technology, it effectively intercepts malicious accounts and abnormal operations without compromising user experience. This process not only enhances the convenience and efficiency of user login services but also significantly strengthens real-time countermeasures.


## 4. Analysis and Prediction of Future Risk Behaviors: 
Establishing a dynamic operation and maintenance mechanism for local whitelists, Dingxiang maintains blacklisted data corresponding to order brushing and fake reviews by accumulating registration, login, and activation data. As online data accumulates, Dingxiang utilizes risk control and business data to model user behavior. The model outputs can be directly applied to risk control strategies, further enhancing the foresight and precision of risk prevention.

Dingxiang Dinsight, combined with the Xintell intelligent modeling platform, provides enterprises with a powerful tool for risk assessment, anti-fraud analysis, and real-time monitoring. Dinsight processes data in less than 100 milliseconds, supporting configurable access and accumulation of data from multiple sources. Leveraging deep learning technology, Dinsight achieves self-performance monitoring and iterative mechanisms for risk control. The Xintell platform optimizes security policies for known risks, uncovers potential risks, and provides end-to-end modeling services, from data processing to model deployment, comprehensively enhancing risk control efficiency and accuracy.

Through dynamic strategies and deep protection, Dingxiang not only effectively identifies and prevents fraudulent activities such as order brushing and fake reviews but also provides solid security assurance for the healthy development of the digital economy while protecting user privacy and enhancing user experience.

Preventing E-commerce Fraud: China's "Provisions on Combating Unfair Competition in Cyberspace" to Be Implemented on September 1

In 2023, the Hangzhou Intermediate People's Court concluded two cases involving "store relocation software" related to unfair competition.

In 2023, the Hangzhou Intermediate People's Court concluded two cases involving "store relocation software" related to unfair competition. The plaintiff in this case was the operating entity of a well-known domestic large-scale e-commerce platform, while the defendant developed a replica software called "a certain moving quick goods listing batch release", which was accused of illegally obtaining platform product information and selling it in other service markets.

According to the plaintiff's claim, its platform and merchants invested a large amount of costs in operating product, transaction, and logistics data information, and took various measures to protect these data resources, prohibiting unauthorized access, copying, storage, and use. The defendant's software provided services to paying users for a long time, illegally capturing the plaintiff platform's product links, titles, images, details, parameters, prices, inventory, and other information, and promoted that it can be copied and transported to other platforms with a single click, resulting in large sales volume.

After trial, the court found that the defendant, without authorization from the plaintiff, illegally obtained and uploaded platform product information to other competing shopping platforms, violating relevant provisions of the "PRC Anti-Unfair Competition Law" and constituting unfair competition on the internet. After the court learned of the plaintiff's willingness to mediate, it actively explained the legal principles and consequences to the defendant, making the defendant clearly aware of the seriousness of its infringement.

Ultimately, the parties voluntarily reached a settlement agreement. According to the agreement, the defendant promised to delete the relevant data and derivative data, and guaranteed that the software would no longer have the functionality to illegally obtain relevant data. In addition, the defendant also paid 100,000 yuan in compensation for economic losses to the plaintiff.


# Steps of website scraping to steal e-commerce data


As a data acquisition tool, web scraping is gradually becoming a major hazard for e-commerce platforms. Website scraping not only enables the acquisition of crucial merchant information and the proliferation of counterfeit websites but also collects sensitive user information, posing a serious threat to users' financial security and privacy. Additionally, web scraping attacks can disrupt normal promotional activities and cause irreversible damage to merchant credibility.

![2024060501.png](https://cdn.aisecurius.com/images/395/395e3eb6-85f4-4873-ada7-9641468c7bb8.png)

Illegal actors utilize website scraping to steal e-commerce data, primarily through the following steps:


## 1. Selecting target websites and platforms:
 The first step in scraping e-commerce data is selecting target websites. Researchers carefully analyze the request characteristics of the target website, including headers, cookies, parameters, etc., to construct subsequent spider requests.


## 2. Constructing requests and executing scraping: 
Using tools such as Python's requests library or Selenium library, spider engineers construct requests and send them out for scraping. These requests are designed to retrieve e-commerce platform product data, bypassing traditional anti-crawler techniques to obtain the required information.


## 3. Data retrieval and storage:
 Once product data is successfully scraped, the spider saves the data to local files or databases for subsequent analysis and use. Common data storage methods include CSV files, JSON files, and MySQL databases.


## 4. Data cleaning and processing:
 Scraped data often contains noise and redundancy, requiring cleaning and processing. Numeric data such as prices and sales can undergo statistical and visual analysis to gain insights into market dynamics and consumer behavior.


## 5. Bypassing anti-crawler mechanisms:
 During scraping, various anti-crawler mechanisms may be encountered, such as IP blocking, CAPTCHA, etc. To bypass these challenges, spider engineers employ a series of measures, such as using proxy servers, adjusting request frequencies, using CAPTCHA recognition technology, etc. Additionally, some spiders even use distributed IP proxy pools, simulate human behavior, and set random time intervals, among other strategies.


# E-commerce Platform: How to Detect Website Scraping?


Modern website scraping programs possess features such as random IP addresses, anonymous proxies, identity modification, and emulation of human operation behavior, making them very difficult to detect and block. Detection and analysis need to be carried out across multiple dimensions.

![2024060414.png](https://cdn.aisecurius.com/images/56e/56eb2103-ef1d-42f3-89c9-369ea046462f.png)
## 1. Targeted Access: 
ss: Malicious website scraping aims to obtain core information from websites and apps, such as user data, product prices, and review content. Therefore, they typically only access pages containing this information while ignoring irrelevant pages
## 
2. Access Behavior: 
or: Website scraping is automated, following preset processes and rules for access. As a result, their behavior exhibits noticeable regularity, rhythm, and consistency, differing significantly from the randomness, flexibility, and diversity of normal user behavior.
## 3. Device Access Patterns
erns: Malicious website scraping seeks to gather the maximum amount of information in the shortest time possible. Thus, they utilize the same device for a large number of access operations, including browsing, querying, and downloading, leading to abnormal indicators such as access frequency, duration, and depth.
## 4. IP Address Access:
ess: To avoid detection and banning by websites, malicious website scraping employs various methods to change IP addresses, such as using cloud services, routers, and proxy servers. This can result in inconsistencies in information such as the geographic origin, ISP, and network type of the IP address, or noticeable deviations from the distribution of normal users.
## 5. Access Timeframes:
mes: To minimize the risk of detection, malicious website scraping typically operates during periods of low website traffic and weak monitoring, such as late at night or early morning. This can lead to abnormal indicators such as access volume and bandwidth usage during these timeframes.
## 6. Analysis and Mining: 
ng: By collecting, processing, mining, and modeling data on the access patterns of normal users and website scraping, it is possible to construct spider recognition models tailored to the website itself, thereby improving recognition accuracy and efficiency
# 
Dingxiang: Assisting E-commerce Platforms in Intercepting Malicious Website Scraping
ping

The attack methods of website scraping are becoming increasingly intelligent and complex. Relying solely on limiting access frequency or encrypting frontend pages is no longer sufficient for effective defense. It is necessary to enhance human-machine recognition technology and increase the ability to identify and intercept abnormal behaviors, thereby limiting website scraping access and raising the cost of malicious data theft. Dingxiang provides comprehensive solutions for enterprises to effectively prevent malicious website scraping behaviors.

Dingxiang atbCAPTCHA, based on AIGC technology, can prevent threats such as AI brute force attacks, automated attacks, and phishing attacks, effectively preventing unauthorized access and intercepting website scraping theft. It integrates 13 verification methods and multiple prevention and control strategies, supports seamless verification for legitimate users, and reduces real-time response and handling time to within 60 seconds, further enhancing the convenience and efficiency of login services.

Dingxiang Device Fingerprinting generates unified and unique device fingerprints for each device by internally linking multi-device information. Based on multidimensional identification strategies involving device, environment, and behavior, it identifies risky devices such as virtual machines, proxy servers, and emulators manipulated by malicious actors. It analyzes whether devices exhibit abnormal or inconsistent behaviors with user habits, such as multiple account logins or frequent changes in IP addresses, and quickly identifies whether access page scrapers originate from malicious devices.

Dingxiang Dinsight assists enterprises in risk assessment, anti-fraud analysis, and real-time monitoring, improving the efficiency and accuracy of risk control. The average processing speed of Dinsight's daily risk control strategies is within 100 milliseconds, supporting the configuration and precipitation of multi-party data and enabling self-monitoring and self-iteration of risk control performance based on mature indicators, strategies, models, and deep learning technology.

Paired with Dinsight, the Xintell intelligent modeling platform automatically optimizes security strategies for known risks and supports risk control strategy configuration in different scenarios based on risk control logs and data mining. Using associated networks and deep learning technology, it standardizes complex processes such as data processing, feature derivation, model construction, and final model deployment, providing a one-stop modeling service. This effectively identifies potential malicious scraping threats and further enhances the recognition of malicious data theft behaviors and the interception effects on malicious website scraping.

618: Can e-commerce platforms fend off malicious website scraping attacks?

The "only refund" clause has been exploited by unlawful elements and evolved into a criminal chain of interests. Major e-commerce platforms are frequently disturbed 

"Only refund" service has become a "standard configuration" for major e-commerce platforms. However, the pursuit of undue benefits in a way that "shears wool" has caused both economic and reputational losses to merchants, sparking widespread attention and controversy.

In January 2024, the Second People's Court of Zhongshan City heard a case of e-commerce "only refund", which aroused widespread attention. In this case, buyer Li purchased a blouse priced at 20 yuan from a certain platform store, but applied for an "only refund" citing quality issues. The merchant Yang did not agree to the request and asked Li to withdraw the application or choose to return the goods for a refund. The court ruled that Li's behavior exceeded the normal limit of rights protection, used platform rules to forcibly refund, and obtained undue benefits. In the end, the court ordered Li to return the purchase price of 20 yuan to the merchant and compensate for the investigation and evidence collection costs of 500 yuan.

![2024060201.jpg](https://cdn.aisecurius.com/images/18a/18ab004d-a4fe-490b-ac87-e0369790835b.jpg)

Behind the trial of this case, it sparked attention and discussion on the refund rules of e-commerce platforms. Some merchants complained that customers abused the "only refund" rule to obtain refunds with untrue reasons, causing losses to the merchants. It was disclosed by some merchants that a certain customer, after purchasing four products, refused to accept one, but successfully applied for a full refund, with a total amount exceeding 1800 yuan. It is said that this customer had taken similar actions many times before, and the merchant's appeal to the platform was unsuccessful. The matter has been reported to the police for handling.

On the one hand, the current application rules of "only refund" service are not clear enough, providing loopholes for consumers to exploit. On the other hand, merchants lack the right to speak in the process of rights protection, and the cost of rights protection is extremely high, putting them in a passive position. In this situation, the trilateral relationship between e-commerce platforms, consumers, and merchants is difficult to balance, causing the intensification and deepening of conflicts.

# Unlawful Elements Profit from New Means: After Online Shopping, Only Refund

The "only refund" clause has been exploited by unlawful elements and evolved into a criminal chain of interests. Major e-commerce platforms are frequently disturbed by the "wool-shearing" industry chain, and both merchants and consumers are in trouble.

![2024060202.jpg](https://cdn.aisecurius.com/images/7c5/7c55939d-7796-4de7-80f3-0aeccc45915a.jpg)

According to investigations, many professional "wool-shearing" organizations have organized so-called "compensation projects," which are promoted through social media and groups. These projects guide participants in how to use loopholes and fraudulent means to obtain undue compensation in the form of tutorials. They often attract participants with small fees or commissions, promising high returns.

Specifically, these criminal gangs will engage in evasive operations against the return and refund policies of e-commerce platforms, such as creating quality issues or questioning the authenticity of goods, in order to obtain refunds and earn compensation. For merchants, such behavior not only leads to huge economic losses but also damages their reputation and credibility. Consumers are also affected because such behavior leads to price increases and decreases in service quality.

Illegal Profit-making Methods: Unlawful elements profit by quickly applying for "only refunds" after purchasing goods, or questioning the authenticity of goods sold by merchants, among other methods, to earn compensation. They also exploit platform vulnerabilities, such as delayed shipments, to engage in return and refund operations.

Social Media Fuels the Fire: There is a plethora of tutorials and dissemination of information on "only refund" strategies and "compensation projects" on major social media platforms. These pieces of information induce more people to join the ranks of illegal profit-making, forming a vast industry chain.

Severe Damage to E-commerce Merchants: Many e-commerce merchants have suffered huge economic losses due to the impact of the "wool-shearing" industry chain. Some new stores and new links have become the focus of attention for unlawful elements, leading to operational difficulties for merchants.

Recently, the Shanghai police arrested multiple suspects. Investigations revealed that these criminals were not only active on one e-commerce platform but also scattered across multiple provinces and cities to commit crimes, with involved amounts reaching millions of yuan.


# E-commerce Platforms' "Only Refund" Terms Are Ambiguous


Major e-commerce platforms have introduced "only refund" services, but there are certain differences in their specific application and rule execution.

In 2021, Pinduoduo took the lead in introducing the "only refund" model, stipulating that users can initiate refund requests within 15 days of confirming receipt of the goods. However, regarding the specific application of refund requests, customer service responses stated that it needs to be based on the actual situation, creating some ambiguity.

E-commerce platforms such as Taobao, JD.com, Douyin, and Kuaishou have also released related "only refund" rules. Among them, Taobao and Douyin's new regulations focus on protecting consumer rights, determining refund applicability based on platform data capabilities and multiple dimensions of merchant operations. JD.com stipulates that if merchants have serious quality issues or discrepancies in goods, the platform will consider it as merchants refusing to fulfill their obligations and support "only refund" or return and refund.


# Merchants: How to Prevent Fraud in "Only Refund"


Unlawful elements exploit e-commerce platforms' "only refund" policies, registering multiple accounts or using software/scripts for automated operations to gain more benefits, causing significant economic losses to platforms and merchants. Merchants not only need to establish more comprehensive business control systems but also integrate risk prevention into every aspect of daily operations.

Firstly, identify and alert risky IP addresses. Access IP risk libraries to match the associated IP addresses of suspicious "only refund" users for risk identification, recognizing risks such as proxies and high-speed dial-up IPs, and intercepting malicious IP addresses.

Secondly, identify and alert risky devices. Verify whether the Device Fingerprinting of suspicious "only refund" users' client is legitimate and detect risks such as injection, hooking, and emulators. Recognize risks such as device rooting, jailbreaking, and hijacking. Identify abnormal behaviors such as multiple activations of the same device, abnormal concentration of the same IP in a short time, abnormal proportion of old device models, and abnormal proportion of old operating systems in the same channel.


## Furthermore, identify and intercept risky accounts. 
Detect abnormal account behaviors of suspicious "only refund" users and deploy strategies based on user behavior, controlling accounts that switch between devices frequently to initiate orders. Identify abnormal verification environment information and tokens during verification completion to promptly detect abnormal and risky operations.


## Lastly, analyze and predict future changes in risky behaviors.
 Establish a dynamic operation and maintenance mechanism for local blacklists based on registration, login, and activation data. Model registration, login, ordering, and purchasing behaviors based on accumulated risk control and business data. Model outputs can be directly used in risk control strategies.

Fraud in "only refund" schemes constantly evolves with rapidly changing methods. New attack methods adjust existing preventive measures or even become immune to them. Traditional measures may not promptly identify and warn against new risks. Moreover, many e-commerce platform security processes are prone to contradictions or conflicts, which can directly impact order transactions and store operations in case of misjudgment. Therefore, merchants need to actively comply with regulations, cooperate with refunds, and actively safeguard their legitimate interests when encountering issues.


E-commerce Only Refund: Nightmare for Some Chinese Merchants

Auto insurance fraud, due to its characteristic of organized groups, often involves perpetrators gathering together to commit fraud in an organized manner. 

The Liang Insurance Fraud Case published on the official WeChat account of the People's Procuratorate of Qingdao City reveals that since 2020, certain collusion between Liang, the head of a car repair shop, Sun, the head of a car service company, and Zhao, an individual from an insurance company, has occurred. They purchased second-hand new energy vehicles of brands like Beiqi and procured collision insurance for these vehicles. Subsequently, they deliberately staged vehicle flooding accidents during heavy rain, and then instructed an assessor named Hua, arranged by Zhao, to provide false assessment reports on behalf of the insurance company.

![2024052001.jpg](https://cdn.aisecurius.com/images/af1/af1865f1-d383-468d-ad3f-641b5e72616e.jpg)

A recent case from Pingdu Court shows that in just a few years, criminals, along with relatives, friends, and employees of auto repair shops, fabricated over 40 car accidents and fraudulently obtained over 1.5 million yuan in claims. Specifically, the defendant Wang operates an auto repair shop. Leveraging his "professional knowledge," he pre-replaces original factory parts of high-end vehicles with lower-priced aftermarket parts and then fabricates traffic accidents in remote, unmonitored locations to fraudulently obtain insurance money through false litigation or direct claims.

In the schemes of insurance fraud syndicates, not only are there fake "collision participants" and "actors," but also some repair shop technicians find themselves unable to stay away and become involved in the chain of insurance fraud.

Data released by the People's Court of Shunyi District, Beijing, indicates that from January 2019 to October 2023, the court heard 26 cases of motor vehicle insurance fraud involving 52 defendants. Among them, there were 23 cases of collusion between auto repair personnel and customers bringing their vehicles for repairs, accounting for a staggering 



# Analysis: Reasons for Rampant Auto Insurance Fraudce Fraud


Auto insurance becomes a hotbed for fraud due to its simple claims procedures and short payout cycles. The following reasons mainly contribute to why auto insurance fraud occurs repeatedly and becomes a prevalent phenomenon.

1. The lure of "low cost, high return" tempts fraudsters to take risks willingly. The root of the myriad of fraud schemes behind auto insurance fraud may lie in the powerful drive for profits. Some unscrupulous individuals rack their brains to design various complex scams to gain illicit wealth. Moreover, there exists a significant information asymmetry between insurance companies and policyholders, with policyholders often having a better understanding of the actual condition of the vehicle. This provides them with the opportunity to exploit this information advantage for fraud.
![2024052002.jpeg](https://cdn.aisecurius.com/images/ab7/ab7f2934-edbb-4c7b-adcb-e31a9cfda369.jpeg)
2. In selecting accident locations, perpetrators tend to carefully fabricate accidents on roads lacking surveillance, such as highway exits or areas near villages. They often opt for rear-end collisions, a collision form relatively easy to assign responsibility for. They exploit the absence of surveillance in these areas to conceal their illegal activities, increasing the chances of successful fraud.

3. In insurance fraud cases, after fabricating accidents, sometimes neither party will choose to report to the police but instead directly fill out a "Self-quick Handling Agreement for Road Traffic Accidents." After completing this operation, the party deemed fully responsible for the accident will report the case to the customer service hotline of their own insurance company. Subsequently, both parties will drive their vehicles to the assessment institution to determine the extent of the loss, thereby obtaining relevant documents and assessment prices issued by the insurance company. Sometimes, the accident locations they report to the insurance company are entirely fabricated. Due to the large volume of accident assessments handled by insurance companies daily, negligence and carelessness may inevitably lead to incomplete and inaccurate inspections of the damaged vehicles at the scene of the accident.

4. Due to intense competition among insurance companies, customer information is generally treated as proprietary and protected, creating insurmountable data barriers between them. Insurance companies operate relatively independently in various stages of underwriting, risk assessment, and claims processing, mainly based on the limited information they possess to make business judgments. This information asymmetry provides favorable space for fraudulent activities by unscrupulous individuals. Additionally, insurance companies often fail to adequately assess risks during underwriting and still do not perform well in key aspects such as on-site inspections and physical examinations after accidents, all of which provide ample opportunities for recurring auto insurance fraud.


# Features: Challenges in Identifying Auto Insurance Fraud


Auto insurance fraud, due to its characteristic of organized groups, often involves perpetrators gathering together to commit fraud in an organized manner. There is collusion both internally and externally, with insiders from insurance companies providing "professional guidance" for insurance fraud or supplying false documentation. Furthermore, the continual evolution of insurance fraud forms makes fraudulent activities increasingly concealed and complex, making them difficult to easily detect.
![2024052003.jpeg](https://cdn.aisecurius.com/images/01f/01f5eae4-f8f0-47ed-b57d-04435631c46e.jpeg)
Additionally, methods of defrauding insurance are becoming more complex and covert. Fraudsters utilize AI technology to forge images, addresses, information, and device registration data, making it difficult for traditional security measures to confirm authenticity, necessitating the use of more advanced technology for analysis and tracing.

At the same time, due to the specific requirements of insurance claims, many cases of fraud are only discovered after claims have been settled. Furthermore, when pursuing historical cases for recovery, there is a need to face the issue of statute of limitations, posing significant challenges to recovery efforts.


# Solution: Collaborative Efforts to Combat Auto Insurance Fraud


Preventing auto insurance fraud requires cooperation among insurance companies, traffic management departments, procuratorates, and other relevant agencies. By strengthening management, refining procedures, fulfilling responsibilities, improving coordination, advancing governance, and utilizing big data, a comprehensive prevention system can be established to effectively curb insurance fraud and uphold the healthy and stable development of the insurance industry.


## Insurance companies need to enhance their anti-fraud capabilities:


1. Establish robust data risk control and analysis models to monitor abnormal patterns in insurance data and promptly detect signs of fraudulent behavior. Dingxiang Dinsight assists enterprises in risk assessment, anti-fraud analysis, and real-time monitoring, enhancing the efficiency and accuracy of risk control. Dinsight's average processing speed for daily risk control strategies is within 100 milliseconds, supporting the configuration and deposition of multi-party data, and leveraging mature indicators, strategies, models, and deep learning technology for risk control self-monitoring and iterative mechanisms. Paired with the Xintell intelligent model platform, which automatically optimizes security strategies for known risks and configures support for risk control strategies in different scenarios based on risk control logs and data mining potential risks. Its standardized processing, mining, and machine learning processes based on associative networks and deep learning technology provide one-stop modeling services from data processing, feature derivation, model construction to final model deployment.

2. Utilize identity verification technology to ensure the authenticity of policyholders' identities and analyze their behavioral patterns to identify abnormal activities. Dingxiang Device Fingerprinting generates unified and unique device fingerprints for each device by internally connecting information from multiple devices. By analyzing behaviors such as multiple account logins, frequent IP address changes, and frequent device attribute changes that are abnormal or inconsistent with user habits, it tracks and identifies fraudulent activities. Paired with Dingxiang's frictionless verification based on AIGC technology, it prevents threats such as brute force cracking, automated attacks, and phishing attacks, effectively preventing unauthorized access, account theft, and malicious operations, thus protecting system stability.

3. Utilize image recognition technology to analyze photos and videos in claims documents to detect possible forgery or tampering. Dingxiang's comprehensive panoramic face security threat perception solution intelligently verifies through multi-dimensional information such as device environment, facial information, image authentication, user behavior, and interaction status, quickly identifying more than 30 categories of malicious behaviors such as injection attacks, live forgery, image forgery, camera hijacking, debugging risks, memory tampering, Root/jailbreak, malicious Rom, simulator operation, etc. Upon discovering forged videos, fake facial images, or abnormal interaction behaviors, it automatically blocks operations. It also dynamically adjusts video verification strength and friendliness, achieving seamless verification for normal users and strengthening verification for abnormal users.

4. Establish cooperative mechanisms with law enforcement agencies, other insurance companies, and third-party data providers to share information and intelligence, strengthen monitoring and crackdown on fraudulent activities.

## 
Regulatory authorities need to enhance collaborative efforts:


1. Traffic management departments should strengthen business guidance and refine accident liability determination procedures. Strengthening guidance for traffic accident assessment service centers, urging them to fulfill their duties strictly, and using appropriate accident liability determination procedures depending on the situation to lay the foundation for subsequent case handling.

2. Procuratorates should play a leading role and increase supervision over cases. Strengthen cooperation, communication, and information exchange with traffic management, control application, and other departments to discover and transfer supervision clues of insurance fraud to promote case handling, and promptly transfer issues such as law enforcement unfairness to relevant functional departments.

3. Ensure smooth execution of sentences and ensure full coverage of administrative and criminal penalties. For individuals involved in cases who do not meet the criminal filing standards, they should be lawfully transferred to administrative departments for administrative penalties to avoid law enforcement and judicial vacuums.

4. Promote enterprise compliance construction and source governance in view of the common occurrence of insurance fraud involving employees of automobile sales and service enterprises and insurance companies. Procuratorates should actively promote legal publicity and cooperation between law enforcement and enterprises.

5. In response to the concentrated fraud in the auto insurance field, develop supervision models for vehicle insurance fraud cases, establish information sharing and cooperation mechanisms, achieve data interoperability, establish an information database for insurance fraudsters, enhance automatic data screening and comparison functions, timely initiate review mechanisms, and increase analysis efforts to discover clues and promote case handling.

In-depth Analysis: Reasons Behind Rampant Auto Insurance Fraud in China

With the advancement of technology, fraudsters are continually updating their methods, utilizing AI technologies including generative models

Recently, The Guardian reported that some fraudsters are utilizing artificial intelligence photo editing software to manipulate photos for insurance fraud activities. This discovery has alarmed insurance companies as it could lead to a surge in car insurance premiums to unprecedented levels.

![2024052301.png](https://cdn.aisecurius.com/images/13d/13d99d3a-e6d3-4c51-ab1f-da75e342020b.png)

Allianz Insurance stated that incidents of tampering with real-life images, videos, and documents using applications have increased by 300% from 2021 to 2023. They stated, "This indicates that this could be one of the latest major scams facing the insurance industry."

Zurich UK Insurance also noted that they have seen a growing number of claims cases involving the use of artificial intelligence technology for tampering. A representative from the company stated, "From an anti-fraud perspective, this is becoming one of the emerging threats."

Although DEEPFAKE is typically created using artificial intelligence for prank images, videos, or documents, traditional editing software on smartphones and applications like Photoshop can also be used for forgery. Allianz stated that the rise of image editing and DEEPFAKE "poses significant risks to UK consumers."

Scott Clayton, Head of Claims Fraud at Zurich UK, stated that data forgery is very common in car insurance. Fraudsters use image editing software to add false license plates to deregistered vehicles.

He said, "We're finding more and more people finding total loss vehicles on broker websites and then implanting a registration onto that vehicle. They then make a claim against that vehicle, and claims handlers will value it at the surface value."

Clayton added that in the past, criminals intending to defraud insurance companies through "crash for cash" claims would need to have the vehicles collide with each other. "Now people can entirely create fraudulent claims behind a computer and profit from it, as these vehicles are already completely damaged."

The increase in fraudulent activities is one of the factors contributing to the rise in car insurance premiums. According to data from the Association of British Insurers, the average price of comprehensive car insurance in the UK rose by about one-third (33%) in the first quarter of this year compared to the same period last year, amounting to £157.


# Insurance Companies: Enhancing Anti-Fraud Capabilities


With the advancement of technology, fraudsters are continually updating their methods, utilizing AI technologies including generative models, machine learning, and data analysis tools to deceive insurance companies. The application of AI technologies has become their new tool, making their criminal activities more covert and complex, challenging the anti-fraud measures of the insurance industry.

To effectively address this challenge, insurance companies need to continuously improve their anti-fraud technologies and utilize advanced data analysis and machine learning algorithms to identify and prevent potential fraudulent behaviors. At the same time, regulatory authorities and industry associations also need to strengthen regulation and cooperation in combating the increasingly serious issue of insurance fraud.

1. Establishing robust data risk control and analysis models to monitor abnormal patterns in insurance data and detect signs of fraudulent behavior promptly. Dingxiang Dinsight assists enterprises in risk assessment, anti-fraud analysis, and real-time monitoring, enhancing the efficiency and accuracy of risk control. Dinsight's average processing speed for daily risk control strategies is within 100 milliseconds, supporting configurable access and deposition of multi-source data. It can achieve self-performance monitoring and self-iterative mechanisms based on mature indicators, strategies, models' experience reserves, and deep learning technology. Xintell intelligent model platform, in conjunction with Dinsight, automatically optimizes security strategies for known risks and configures support for risk control strategies in different scenarios based on risk control logs and data mining of potential risks. It standardizes complex data processing, mining, and machine learning processes based on association networks and deep learning technology, providing one-stop modeling services from data processing, feature derivation, model construction to final model deployment.

2. Adopt identity verification technologies to ensure the authenticity of policyholders' identities while analyzing their behavioral patterns to identify anomalies. Dingxiang Device Fingerprinting generates unified and unique Device Fingerprinting for each device by internally connecting the information of multi-end devices. Analyzing whether there are abnormal behaviors such as multiple account logins, frequent IP address changes, and frequent device attribute changes that do not conform to user habits, it tracks and identifies fraudulent activities. Paired with Dingxiang's seamless verification based on AIGC technology, it prevents threats such as AI brute force attacks, automated attacks, and phishing attacks, effectively preventing unauthorized access, account theft, and malicious operations, thus protecting system stability.

3. Utilize image recognition technology to analyze photos, videos, and other materials in claim documents to detect possible forgery or tampering. Dingxiang's comprehensive panoramic face security threat perception solution intelligently verifies multidimensional information such as device environment, facial information, image authenticity, user behavior, and interaction status, quickly identifying more than 30 types of malicious attack behaviors such as injection attacks, live forging, image forgery, camera hijacking, debugging risks, memory tampering, Root/jailbreak, malicious ROM, simulator, etc. After timely discovering forged videos, false facial images, and abnormal interaction behaviors, it can automatically block operations. It can also flexibly configure the strength and friendliness of video verification, achieving dynamic mechanisms of seamless verification for normal users and enhanced verification for abnormal users.

4. Establish cooperation mechanisms with law enforcement agencies, other insurance companies, and third-party data providers to share information and intelligence, strengthening the monitoring and crackdown on fraudulent activities.

By adopting the above methods and building a secure system of multi-channel, all-scenario, and multi-stage protection, insurance companies can efficiently prevent various types of new fraud risks brought about by AI, safeguarding their own and their customers' interests.



Warning! Cases of AI-enabled insurance fraud have emerged in the UK

Insurance fraud methods are complex, covert, and constantly evolving. Tactics such as pseudo base stations, coding platforms,

On April 11, 2024, the official website of the China Banking and Insurance Regulatory Commission (CBIRC) released a public notice regarding the "Draft Measures for Combating Insurance Fraud (for Solicitation of Comments)".

![202405205.png](https://cdn.aisecurius.com/images/9f2/9f23ef40-33c6-48f9-854e-77cd9d032077.png)

The "Draft Measures" consist of 6 chapters and 37 articles, explicitly stating that the goal of combating insurance fraud is to establish a "regulatory guidance, institution-led, industry cooperation, and multi-party coordination" integrated working system. It aims to establish a basic and sound anti-fraud institutional mechanism, effectively curb the trend of fraudulent illegal activities, significantly enhance the industry's ability to prevent and resolve fraud risks, and visibly strengthen consumer awareness of anti-fraud.


# Car Insurance Fraud: Exhibiting Organized Collusion


Insurance fraud methods are complex, covert, and constantly evolving. Tactics such as pseudo base stations, coding platforms, and point walls make it difficult for insurance companies to prevent, with fraud methods being flexible, diverse, and rapidly iterating. Moreover, there's a complete fraud chain with clear division of labor upstream and downstream, forming specialized industry chains including intelligence acquisition, infrastructure services, fraud application services, monetization, and arbitrage. For instance, professional gangs in the car insurance and health insurance sectors operate across regions and companies, while "injury scalpers" form professional criminal gangs, and "proxy cancellations" form black industry chains, among others.

![2024052001.jpg](https://cdn.aisecurius.com/images/af1/af1865f1-d383-468d-ad3f-641b5e72616e.jpg)

For instance, in some cases related to car insurance, the crimes often occur late at night in blind spots of surveillance, with vehicles usually showing signs of previous collisions or accidents. Perpetrators meticulously conspire to fabricate accidents, report false claims, apply for claims, and submit claim materials, thus increasing the difficulty of detection. Moreover, collusion between internal and external parties is also quite common. Some cases indicate that internal surveyors and loss adjusters of insurance companies provide "professional guidance" for insurance fraud, or provide false documentary evidence during accident surveys and loss assessments.

Car insurance fraud cases exhibit several characteristics:


## 1. Clear indications of organized crime, 
with perpetrators often gathering together to commit fraud in an organized manner. For instance, in some car insurance cases, perpetrators operate relatively fixedly, choose to commit crimes late at night in surveillance blind spots, and vehicles often have old marks, making it difficult to detect due to meticulous conspiracy.


## 2. Covert methods of operation
, utilizing late-night hours and surveillance blind spots to increase stealth.


## 3. Collusion with internal personnel of insurance companies,
 facilitating convenience. Internal personnel of insurance companies provide "professional guidance" for insurance fraud or provide false documentary evidence.


## 4. Certain degree of unreported crimes
, with many cases being detected by insurance companies through abnormal reports. There may be numerous instances of unreported fraudulent claims, indicating the existence of a large number of potential fraudulent activities that have not been discovered.


# Preventive Measures Against Car Insurance Fraud


Car insurance has become a hotspot due to its simple claims procedures and short payout cycles. Preventing car insurance fraud requires coordinated efforts from insurance companies, traffic management departments, prosecution agencies, and other relevant authorities. Through strengthening management, refining procedures, leveraging functions, ensuring coordination, advancing governance, and utilizing big data, a comprehensive preventive system should be established to effectively curb insurance fraud and maintain the healthy and stable development of the insurance industry.
## Insurance Companies: Enhancing Anti-Fraud Capabilities
ties

1. Establishing sound data risk control and analysis models to monitor abnormal patterns in insurance data and promptly identify signs of fraudulent behavior. Dingxiang Dinsight assists companies in risk assessment, anti-fraud analysis, and real-time monitoring, enhancing the efficiency and accuracy of risk control. Dinsight's average processing speed for daily risk control strategies is within 100 milliseconds, supporting configurable access and deposition of multi-party data. It leverages mature indicators, strategies, model experiences, and deep learning technology to achieve self-monitoring and self-iterative mechanisms for risk control. Paired with the Xintell intelligent model platform, it automatically optimizes security strategies for known risks and configures different scenario support for risk control strategies based on risk logs and data mining of potential risks.

2. Employing identity verification technology to ensure the authenticity of policyholders' identities while analyzing their behavioral patterns to identify anomalies. Dingxiang Device Fingerprinting generates unified and unique device fingerprints for each device by internally connecting multi-device information. It analyzes behaviors such as multiple account logins, frequent IP address changes, and device attribute alterations that deviate from user habits, tracking and identifying fraudulent activities. Complemented by Dingxiang atbCAPTCHA based on AIGC technology, it prevents threats such as AI brute force attacks, automated attacks, and phishing attacks, effectively safeguarding against unauthorized access, account theft, and malicious operations to protect system stability.

3. Utilizing image recognition technology to analyze photos and videos in claim documents to detect potential forgery or tampering. Dingxiang's comprehensive facial security threat perception solution employs intelligent verification based on multidimensional information such as device environment, facial information, image authentication, user behavior, and interaction status. It quickly identifies over 30 types of malicious behaviors, such as injection attacks, live counterfeiting, image tampering, camera hijacking, debugging risks, memory tampering, rooting/jailbreaking, malicious ROMs, emulators, etc. After promptly discovering forged videos, fake facial images, or abnormal interaction behaviors, it automatically blocks operations while flexibly configuring the intensity and friendliness of video verification, implementing a dynamic mechanism for strengthening verification for abnormal users.

4. Establishing cooperative mechanisms with law enforcement agencies, other insurance companies, and third-party data providers to share information and intelligence, enhancing monitoring and combating fraudulent act
## Regulatory Departments: Strengthening Cooperative Efforts
tive Efforts

1. Traffic management departments should enhance business guidance and refine accident liability determination procedures, providing strict guidance to accident loss assessment service centers and urging them to strictly perform their duties, adopting appropriate accident liability determination procedures as needed to lay the foundation for subsequent case handling.

2. Prosecution agencies should play a leading role and intensify supervision of cases. Strengthening cooperation, communication, and information exchange with traffic management and control departments to identify and transfer supervision clues of insurance fraud cases to promote case resolution, promptly referring issues such as unfair law enforcement discovered to relevant functional departments.

3. Ensuring coordination between administrative and criminal penalties to achieve full coverage. Unlawfully transferring individuals involved in cases that do not meet criminal filing standards to administrative departments for administrative penalties to avoid law enforcement and judicial vacuums.

4. Promoting compliance construction and source control in line with industry characteristics. Given the widespread involvement of automobile sales and service enterprises and insurance company employees in insurance fraud, prosecution agencies should actively promote legal publicity and joint construction with enterprises.

5. Developing supervision models for automobile insurance fraud cases, establishing information sharing and cooperation mechanisms, achieving data interoperability, establishing databases of insurance fraudsters, enhancing automatic data screening and comparison functions, promptly initiating review mechanisms, and intensifying analysis efforts to discover clues and promote case resolution.

It is worth noting that the Economic Crime Investigation Bureau of the Ministry of Public Security and the Inspection Bureau of the China Banking and Insurance Regulatory Commission have jointly convened a special mobilization deployment meeting for cracking down on insurance fraud crimes. A seven-month special crackdown will be carried out from the end of April 2024 to the end of November 2024.


China: Four Trends in Car Insurance Fraud

Car insurance fraud is rampant due to its low cost and high profitability. Additionally, some members of the public have insufficient understanding of insurance regulations and weak legal awareness. 

In March 2024, the Beijing police smashed a professional insurance fraud criminal gang that deliberately staged accidents to defraud insurance claims. In this case, a certain insurance company colluded with current and former employees, as well as designated auto repair shops, to fraudulently obtain claims payments through fabricated or intentionally staged vehicle accidents. Not long ago, the Liaoning police also cracked a case of self-staged fake accidents for insurance fraud, resulting in 10 criminal suspects being taken into custody according to law.

![2024051410.jpg](https://cdn.aisecurius.com/images/dbd/dbd0b21e-1297-4d33-9188-da423182f0d9.jpg)

On January 12, 2024, the official WeChat account of the Qingdao People's Procuratorate showed that since 2020, Liang and others have purchased second-hand new energy vehicles and staged flooding accidents to defraud insurance claims. In a case heard by Pingdu Court, Wang operated an auto repair shop and colluded with others to fabricate more than 40 accidents to defraud over 1.5 million yuan. Data from Shunyi Court shows that from January 2019 to October 2023, 23 out of 26 cases of motor vehicle insurance fraud involved collusion between auto repair personnel and customers. The modus operandi of car insurance fraud is becoming more covert, with the industry believing that factors such as low cost, high returns, and information asymmetry contribute to its frequent occurrence and variety, such as manufacturing accidents in remote areas at night, according to Liu Chunsheng, who pointed out underlying motives such as profit incentives.

Let's look at another fraud case in Guangzhou. From April 2017 to December 2020, Zhou and Wang conspired to stage car damage accidents, which were then sent to Wang's car beauty service department for assessment and repair to defraud insurance. During this period, Zhou committed 10 instances of insurance fraud, successfully obtaining over 720,000 yuan and attempting to obtain over 30,000 yuan. The People's Court of Sanshui District, Foshan City, ruled that Zhou, with the purpose of illegal possession, conspired with others to stage insurance accidents for fraudulent purposes, with a huge amount involved. He was sentenced to six years and eight months in prison and fined 60,000 yuan.

Tianjin also revealed a similar insurance fraud case. Ms. Zhou and Mr. Liu separately discovered abnormal accident records for their vehicles, and the Tianjin Municipal Public Security Bureau's Economic Investigation Intelligence Liaison Center captured relevant clues through big data screening. The police found suspicious accidents related to luxury cars, and Pan, the person in charge of an auto repair shop in Nankai District, came under scrutiny. It turned out that since the end of 2015, Pan had been planning insurance fraud, registering multiple auto repair companies in the name of employees, deliberately staging accidents, and impersonating drivers to file claims.

Since 2016, this gang has committed over 100 insurance fraud crimes involving nearly tens of millions of yuan. They replaced luxury car parts and staged "collisions" in repair shops, fabricated scene evidence to obtain appraisal reports, and then applied for claims. They used old parts to impersonate original parts and defrauded high insurance claims. Dong, an employee of Pan, cooperated for a long time, and some insurance company agents became "moles." Some vehicle owners cooperated in insurance fraud due to inducements, such as Guo agreeing to save money and receive red envelopes, but the auto repair shop used his car to defraud 400,000 yuan in claims. Finally, this criminal gang was apprehended.


# Why is Car Insurance Fraud So Rampant?


Car insurance fraud is rampant due to its low cost and high profitability. Additionally, some members of the public have insufficient understanding of insurance regulations and weak legal awareness. Furthermore, some insurance companies engage in extensive operations and overlook risk assessment, which is another significant reason for the high incidence of such cases. In pursuit of business volume, insurance companies simplify claims procedures excessively, overly relying on third-party evidence. Traditional methods of fraud prevention are inefficient, and there is a lack of information sharing among insurance institutions, resulting in inadequate efforts to combat and prevent motor vehicle insurance fraud.

Analysis shows that there is a noticeable trend towards organized fraud in the car insurance sector, with perpetrators often gathering together to commit fraud in an organized manner. Furthermore, collusion with internal personnel of insurance companies facilitates fraud, leading to a large number of potential fraudulent activities going undetected.

Preventing insurance fraud requires collaborative efforts from insurance companies, traffic management departments, prosecution agencies, and other relevant departments. Through strengthening management, refining procedures, leveraging functions, ensuring coordination, promoting governance, and utilizing big data, a comprehensive prevention system should be established to effectively curb insurance fraud and maintain the healthy and stable development of the insurance industry.


China: Some Common Cases of Car Insurance Fraud

CCTV's "Today's Verdict" program recently reported a case in which a programmer developed illegal video reposting software, earning over 7 million yuan in profit and ultimately receiving a prison sentence.


CCTV's "Today's Verdict" program recently reported a case in which a programmer developed illegal video reposting software, earning over 7 million yuan in profit and ultimately receiving a prison sentence.

A well-known domestic short video platform reported to the police that someone was selling a video reposting software online. Users of the software could bypass the platform's review mechanism, easily "repost" and steal others' works for illegal submission. Police investigations revealed a chain of illegal activities behind this, with criminal groups upstream developing and producing illegal software, altering the codes of short video platforms to evade supervision.

![2024050602.png](https://cdn.aisecurius.com/images/76d/76d73194-3d02-43af-b6c5-546b4d276660.png)

The suspect, a post-90s programmer named Zhou, confessed that the software was mainly used for illegal video reposting. It supported video mirroring, watermark removal, draft replacement, camera replacement, and other functions. Modified videos could be easily published on mainstream video platforms such as Kuaishou, Douyin, Bilibili, Xiaohongshu, and Xigua Video, assisting others in quickly increasing their follower counts. From May 2022 to March 2023, Zhou accumulated profits of over 7 million yuan. Currently, Zhou has been sentenced to three years in prison with a five-year probation period. Chen, an accomplice responsible for selling the software, was sentenced to three years in prison with a two-month probation period.


# Unveiling: Illegal Video Reposting Software Is a Web Crawler


In this case, the software used for illegally downloading video content was a web crawler. A web crawler, also known as a web spider or web robot, is a program or script that automatically retrieves information and data from the internet according to certain rules.

![2024050604.png](https://cdn.aisecurius.com/images/d73/d73d90f0-bf47-4e72-be76-0b5083b8dcfe.png)
![2024050605.png](https://cdn.aisecurius.com/images/42a/42ac0682-511f-449e-b068-6228044ad00a.png)

In November 2022, Dingxiang Defense Cloud Business Security Intelligence disclosed that a social media platform suffered from persistent web crawler theft. A large amount of user information and original content was stolen by web crawlers and resold to competitors or used directly for malicious marketing.

Data shows that the global data theft volume in 2023 will reach 190 billion records, with over 80% of the data coming from web crawlers. Web crawlers typically programmatically access websites to retrieve user information or data. Such behavior not only violates users' privacy but also causes significant economic losses to businesses.


# Question: How to Detect Web Crawlers Stealing Videos?


Current web crawler programs have features such as random IP addresses, anonymous proxies, identity modification, and imitation of human operational behavior, making them extremely difficult to detect and block, requiring identification and analysis from multiple dimensions.

![2024050607.png](https://cdn.aisecurius.com/images/f17/f1782141-99e8-4160-995c-6b0b72b7b960.png)

First is the target of access. Malicious web crawlers aim to obtain core information from websites or apps, such as user data, product prices, and comment content. Therefore, they typically only access pages containing such information while ignoring other irrelevant pages.

Second is access behavior. Web crawlers are automatically executed by programs, following preset processes and rules for access. Hence, their behavior exhibits obvious regularity, rhythm, and consistency, contrasting with the randomness, flexibility, and diversity of normal user behavior.

Third is access devices. The goal of malicious web crawlers is to gather the most information in the shortest time. Therefore, they use the same device for a large number of access operations, including browsing, querying, and downloading, leading to abnormal indicators such as access frequency, duration, and depth of that device.

Fourth is access IP addresses. To evade website identification and blocking, malicious web crawlers employ various methods to change IP addresses, such as using cloud services, routers, and proxy servers. This results in inconsistent information about the geographical origin, service provider, and network type of the IP address, or significant deviations from the distribution of normal users.

Fifth is access time periods. To minimize the risk of detection, malicious web crawlers typically choose periods with low website traffic and weak monitoring for bulk crawling, such as late at night or early morning. This leads to abnormal indicators such as access volume and bandwidth usage during those time periods.

Sixth is big data modeling and mining. By collecting, processing, mining, and modeling access data of normal users and web crawlers, dedicated crawler identification models specific to the website itself can be constructed, thereby enhancing recognition accuracy and efficiency.


# Dingxiang Solution: Effective Anti-crawler Measures


The tactics employed by web crawlers are becoming increasingly intelligent and complex. Relying solely on limiting access frequency or encrypting frontend pages is no longer sufficient for effective defense. There is a need to enhance human-machine recognition technology, increase the ability to identify and intercept abnormal behaviors, and thereby restrict web crawler access, raising the cost of malicious data theft attacks. Dingxiang provides comprehensive, all-process defense solutions for enterprises, effectively preventing malicious data theft by web crawlers.

Dingxiang atbCAPTCHA, based on AIGC technology, prevents threats such as AI brute force attacks, automated attacks, and phishing attacks, effectively thwarting unauthorized access and intercepting web crawler theft. It integrates 13 verification methods and multiple control strategies, supporting seamless passage for secure users. Real-time response and disposal capabilities have been reduced to within 60 seconds, further enhancing the convenience and efficiency of digital login service experiences.

Dingxiang Device Fingerprinting unifies and generates a unique device fingerprint for each device by internally linking multi-platform device information. Utilizing multidimensional identification strategy models based on device, environment, and behavior, it identifies risky devices manipulated by malicious entities such as virtual machines, proxy servers, or emulators. It quickly identifies whether access page crawlers originate from malicious devices by analyzing abnormal or non-user habitual behaviors such as multiple account logins, frequent IP address changes, or frequent device attribute changes.

Dingxiang Dinsight assists enterprises in risk assessment, anti-fraud analysis, and real-time monitoring, enhancing risk control efficiency and accuracy. Dinsight's average processing speed for daily risk control strategies is within 100 milliseconds. It supports configuration-based access and sedimentation of multi-party data, leveraging mature indicators, strategies, models, and deep learning technology for risk control self-performance monitoring and self-iterative mechanisms.

Paired with Dinsight, the Xintell Intelligent Model Platform automatically optimizes security strategies for known risks and configures support for various risk control strategies based on risk control logs and potential risks identified through data mining. Leveraging associative networks and deep learning technology, it standardizes complex data processing, mining, and machine learning processes, providing end-to-end modeling services from data processing, feature derivation, model construction, to final model deployment. This effectively uncovers potential malicious scraping threats, further enhancing the recognition of malicious data theft behaviors and the interception effectiveness against malicious web crawlers.

CCTV: Post-90s Programmer Sentenced for Illegally Reposting Original Videos Using Software

Knowledge sharing is an essential part of internet culture, and it is enabled by sharing licenses that allow people to access content and create derivative works.

 Knowledge sharing is an essential part of internet culture, and it is enabled by sharing licenses that allow people to access content and create derivative works. However, the rise of generative AI and large language models is threatening the principles of knowledge sharing.

The large-scale collection of online content, which has become commonplace with the advent of generative AI and large language models, is threatening the principles of knowledge sharing. This data, which is collected by web scrapers, is used to develop AI models. However, this content is often not fully authorized by the creators, which raises concerns about the principles of knowledge sharing and is contrary to the idea of knowledge sharing.

![2024051301.png](https://cdn.aisecurius.com/images/228/228276c8-7bc1-4ae6-9662-3de2a110e812.png)

Generative AI and large models have unprecedented data requirements. The market is gradually reaching a consensus that whoever has the data owns the world, and data is the key to competition for large models. There are two main sources of AI training data: self-collection and crawling. Self-collecting data requires a lot of manpower, material resources, and time, and is expensive. Scraping data, on the other hand, is relatively easy to obtain. In 2023, the amount of data collected by web scrapers worldwide will reach 190 billion records, of which more than 80% is unauthorized. Web scrapers typically use programming to automatically access websites and obtain user information or data. This behavior not only violates user privacy but also causes huge economic losses to enterprises. It is estimated that in 2024, the illegal collection of data by web scrapers will continue to increase.


# Measures to Combat Large-Scale Plagiarism in News Website


In the face of this problem, news websites can take the following measures to combat large-scale plagiarism:


## Utilize robots.txt。
News websites can use the robots.txt file to block access by web scrapers. However, this method is not foolproof as determined web scrapers can bypass these rules.


## Implement anti-scraping technology。
Employ anti-scraping technology to detect and prevent unauthorized access by web scrapers. Dingxiang provides a comprehensive anti-scraping solution for news and information platforms, effectively preventing malicious data theft by web scrapers. Their Dingxiang Invisible Verification, based on AIGC technology, can prevent threats such as AI brute force cracking, automated attacks, and phishing attacks. This effectively prevents unauthorized access and intercepts web scraper theft.


## trengthen copyright protection。 
Strengthen copyright protection measures to safeguard the rights of content creators. This includes enforcing copyright laws and pursuing legal action against copyright infringers.

The rise of AI poses new challenges to the protection of intellectual property and the promotion of knowledge sharing. News websites, along with the technology industry and content creators, must work together to explore effective solutions to combat large-scale plagiarism and protect the rights and interests of content creators.hts and interests of content creators.

How News Websites Can Prevent Large-Scale Plagiarism of Original Content in the AI Era

he application of AI has become their new tool, making their criminal activities more covert and complex, challenging the anti-fraud measures of the insurance industry.


Insurance fraud has long been one of the significant challenges facing the insurance industry, especially with technological advancements enabling fraudsters to continually update their methods. They exploit AI technologies, including generative models, machine learning, and data analytics tools, to deceive insurance companies. The application of AI has become their new tool, making their criminal activities more covert and complex, challenging the anti-fraud measures of the insurance industry.

![2024040207.png](https://cdn.aisecurius.com/images/1a9/1a987802-f054-4655-85bc-3fbea4e7821e.png)


## 1. Voice Cloning
: Fraudsters obtain voice samples from social media and use AI technology to generate believable voice clones. These clones can be used to impersonate clients and communicate with insurance agents, making false claim requests.


## 2. DEEPFAKE Photos and Videos:
 Fraudsters use AI technology to create fake photos and videos, conducting fraudulent activities through social networks or video calls. These DEEPFAKE media make it harder for insurance company personnel to identify them.


## 3. Fabricated Medical Evidence:
 AI-driven image generators can create realistic medical evidence such as CT scans or X-rays. This false evidence can be used to submit fraudulent medical claims.


## 4. Phishing Emails
: Fraudsters use AI robots like ChatGPT to generate realistic phishing attempts and email scam messages to deceive victims.


## 5. Data Theft for Insurance Applications
: Previously, fraudsters manually filled out false information to submit insurance applications, but now they can automate this process using AI technology. They can extract customer data from multiple sources and automatically fill out application forms. This enables fraudsters to submit a large number of false applications, expanding their fraud scale and making it more challenging to detect.


## 6. Bypassing Security System
s: Insurance companies typically rely on security systems to detect abnormal data to identify potential fraudulent behavior. However, fraudsters use AI technology, such as manipulation tools for IP addresses and device IDs, to bypass these security triggers. They can quickly switch IP addresses or change device IDs to make it appear as if they are legitimate requests from different sources. This makes it more challenging for insurance companies to detect and prevent fraud.


# Automobile Insurance Fraud: Common Types of Fraud


Automobile insurance fraud is one of the most prevalent and costly types of insurance fraud worldwide. In the United States alone, approximately 20% of automobile insurance claims paid out each year fall into the hands of fraudsters, resulting in an average additional expenditure of $400-700 per household annually in premiums.

Automobile insurance fraud can be categorized into soft fraud and hard fraud. Soft fraud typically involves exaggeration of events or claims, such as misrepresenting a minor dent in a bumper caused by scraping a garage wall as a hit-and-run accident. Hard fraud, on the other hand, is usually more severe and involves larger payouts, such as staging accidents or falsely claiming vehicle theft. Specifically, they include:


## False Claims: 
Individuals may make false or exaggerated claims for losses or damages that did not actually occur under automobile, property, or health insurance.


## Staged Accidents:
 Some individuals intentionally cause accidents or collisions to fraudulently claim compensation for bodily injury or property damage.


## Identity Theft: 
Fraudsters may use stolen identities to apply for insurance policies or make claims on behalf of others.


## Arson
: Arson is a common form of fraud in property insurance, where policyholders intentionally set fire to property to claim insurance money.


## Exaggerated Losses
: Policyholders may exaggerate the extent of their losses when filing claims to seek payouts beyond reasonable costs.

To address these challenges, insurance companies need to continuously enhance technological preventive measures and improve the accuracy and efficiency of fraud detection. Additionally, cooperation with other industries is necessary to collectively combat insurance fraud.

Insurance fraud is an evolving problem, and the advent of artificial intelligence technology has made it more complex and widespread. To effectively address this challenge, insurance companies must continually improve their anti-fraud technologies and utilize advanced data analytics and machine learning algorithms to identify and prevent potential fraudulent activities. At the same time, regulatory bodies and industry associations need to strengthen oversight and cooperation in combating insurance fraud to collectively address this increasingly serious issue.


# Insurance Company: Effective Defense Strategies


[The Intelligence Special Issue on "DEEPFAKE Threat Research and Security Strategies" ](https://www.aisecurius.com/blog/article/55)suggests that combating AI fraud requires not only effectively identifying and detecting AI-generated content but also preventing the exploitation and proliferation of AI fraud. This requires not only technological countermeasures but also complex psychological warfare and an increase in public safety awareness.
![2024040206.png](https://cdn.aisecurius.com/images/9ce/9cec24d7-8246-4d6d-9e55-57af93c5dd34.png)

## 1. Data Analysis and Pattern Recognition
: Data analysis and pattern recognition can help insurance companies identify potential anomalies and behaviors, thereby improving the accuracy of fraud detection. By utilizing AI technology to analyze large amounts of data, abnormal patterns and behaviors can be identified. Monitoring customer historical data and behavior patterns can facilitate the detection of potential fraudulent activities.


## 2. Behavioral Identification and Analysis:
 Machine learning algorithms can analyze customer behavior patterns, including purchasing behavior, claims history, and interactions with the insurance company. Anomalies in behavior may indicate the presence of fraud. Behavioral analysis can uncover abnormal customer behavior, providing vital clues for fraud detection.

Dingxiang Device Fingerprinting bridges internal information of multi-terminal devices to generate unified and unique device fingerprints for each device. It constructs a multi-dimensional identification strategy model based on devices, environments, and behaviors to identify risk devices such as virtual machines, proxy servers, and emulators manipulated maliciously. It analyzes whether there are abnormal behaviors such as multiple account logins, frequent IP address changes, and frequent device attribute changes that do not conform to user habits, tracks and identifies fraudulent activities, helps enterprises achieve operation of the same ID in all scenarios and channels, and assists in cross-channel risk identification and control.


## 3. Identity Verification:
 Adopting biometric technology or other advanced identity verification methods to ensure the authenticity of customer identities, thereby reducing the risks of identity theft and impersonation.

Dingxiang atbCAPTCHA, based on AIGC technology, can prevent threats such as AI brute force attacks, automated attacks, and phishing attacks, effectively preventing unauthorized access, account hijacking, and malicious operations, thereby protecting system stability. It integrates 13 verification methods and various prevention and control strategies, consolidating 4380 risk policies, 112 categories of risk intelligence, covering 24 industries and 118 risk types. Its precision in prevention and control reaches 99.9%, and it can quickly achieve the transformation from risk to intelligence. It also supports secure user passes without perception, and the real-time counter-disposal capability is reduced to within 60 seconds, further improving the convenience and efficiency of digital login services.


## 4. Image and Facial Recognition
: Analyze images and photos involved in claims to confirm their authenticity. For example, image recognition technology can detect editing traces or signs of modification in images. Image recognition technology can verify the authenticity of images in claims.

Dingxiang's comprehensive panoramic facial security threat perception solution intelligently verifies multidimensional information such as device environment, facial information, image authentication, user behavior, and interaction status, quickly identifying more than 30 types of malicious attacks such as injection attacks, live forgery, image forgery, camera hijacking, debugging risks, memory tampering, Root/jailbreak, malicious ROM, and simulator operations and systems. After discovering forged videos, fake facial images, and abnormal interactive behaviors, it can automatically block operations. It can also flexibly configure video.


## 5. Natural Language Processing
: Utilize natural language processing technology to analyze claim documents, application forms, and other text information to explore any inconsistent or suspicious language patterns. Natural language processing helps uncover suspicious patterns in textual information.


## 6. Real-time Monitoring and Warning Systems
: Construct real-time monitoring systems to observe potential fraudulent behavior and take corresponding measures. Warning systems can automatically issue alerts based on predefined rules and patterns. Real-time monitoring and warning systems can promptly detect potential fraudulent activities for intervention.

Dingxiang Dinsight helps enterprises conduct risk assessment, anti-fraud analysis, and real-time monitoring, improving the efficiency and accuracy of risk control. The average processing speed of Dinsigh's daily risk control strategy is within 100 milliseconds, supporting configuration-based access and precipitation of multi-party data. It can realize self-performance monitoring and self-iteration mechanisms based on mature indicators, strategies, models, and deep learning technology. Paired with Dinsight, the Xintell intelligent model platform can automatically optimize security strategies for known risks, support risk control strategies in different scenarios based on risk control logs and data mining potential risks. Based on association network and deep learning technology, it standardizes complex data processing, mining, and machine learning processes, providing one-stop modeling services from data processing, feature derivation, model construction to final model online.


## 7. Collaborative Anti-fraud Networks:
 Build partnerships with other insurance companies, financial institutions, and government agencies to share information and experiences, jointly combatting fraudulent activities. Collaborative anti-fraud networks enhance fraud prevention capabilities through information sharing and collaborative work.

By employing these methods and building a multi-channel, full-scenario, multi-stage security system, effectively combating new threats brought by AI, insurance companies can more efficiently prevent fraudulent risks and safeguard their interests and those of their customers.


Attacker: Launching Large-Scale Fraud Against Insurance Companies Using AI

Despite the significant investment of manpower, time, and resources by insurance companies to address and solve fraud issues, fraud detection remains a major bottleneck for insurance companies. 

Insurance fraud refers to the intentional fabrication or concealment of information by individuals or organizations in an attempt to obtain insurance benefits or payments they are not entitled to. This is a serious illegal activity that not only harms the interests of insurance companies but also affects the legitimate rights and interests of policyholders. Insurance fraud appears in various unexpected forms. Claimants may fabricate false injury experiences, falsify the extent of damage to vehicles or property, submit false documents, forged medical records, receipts, etc., or even make blatantly false statements.
![2024042602.jpg](https://cdn.aisecurius.com/images/80e/80e30e83-1fd5-45ef-a4e6-85ddf6926979.jpg)

This intentional fraudulent behavior may involve multiple parties, including policyholders, insurance agents, brokers, financial planners, medical personnel, legal advisors, and organized criminal organizations, among others. Although insurance fraud takes various forms, they all have one common characteristic: dishonesty and deception.


# Insurance Fraud: Why is it Complex


Despite the significant investment of manpower, time, and resources by insurance companies to address and solve fraud issues, fraud detection remains a major bottleneck for insurance companies. The methods used by fraudulent claimants are becoming increasingly sophisticated and covert, making it exceptionally difficult to differentiate between fraudulent and non-fraudulent claims.

1. Historically fraudulent behavior has not been accurately identified, making it significantly more difficult to predict future fraudulent events.

2. Insurance companies frequently change their fraud detection methods, making it extremely challenging to identify fraud based on historical data.

3. The inability to accurately assess the relationships and connections between suspicious entities and scenarios.

4. Lack of sufficient resources to quickly and effectively investigate each suspicious claim.

Due to the manual processing of large volumes of documentation by claims handlers, it is common for key information to be overlooked. Moreover, in many cases, the assessment of claims often relies on the professionalism and experience of claims handlers, resulting in claim evaluations being based more on subjective judgment rather than objective facts.



# Insurance Fraud: Common Anomalous Signals


While insurance fraud often involves complex networks of deception and falsification, some danger signals and warning signs can help us identify potential fraudulent activities.

1. Inconsistencies in information on application forms or medical records may indicate dishonest behavior by the claimant.

2. Unusual behavioral patterns, such as frequent claims for the same type of injury, may suggest the presence of fraudulent activity.

3. Fraudsters are more likely to fabricate false claims when there are no witnesses to accidents or injuries.

4. Suspicious healthcare providers who charge for services not provided, overcharge, or prescribe unnecessary treatments are likely involved in fraud.

5. Using unusual payment methods may also indicate someone attempting to conceal their identity.

As one of the most heavily regulated industries globally, the insurance industry faces significant challenges in adopting new technologies and innovations. Nevertheless, in the face of the stubborn problem of insurance fraud, insurance companies must continue to explore and innovate to identify and prevent fraudulent activities more effectively, safeguarding the healthy and stable development of the insurance industry.


Insurance fraud is difficult to identify, but there are several anomalous features that serve as signals.

Olga claims that her likeness may be used for fraudulent promotional activities, becoming a means for profiteers to make money.


Earlier this year, 20-year-old Ukrainian internet sensation Olga Loiek encountered a group of AI-generated clones of herself online. "I was really scared. I saw my face being used to promote products in Chinese," she told the media. These presenters selling products on social media platforms have their faces and voices generated using AI face-swapping technology, portraying themselves as foreigners in China selling foreign specialties. Accounts using Olga's likeness and voice number close to a hundred, spanning across mainstream social media platforms in China, with followers ranging from thousands to hundreds of thousands. One fake account even amassed 300,000 followers.
![2024040807.png](https://cdn.aisecurius.com/images/4df/4df3e33c-51dc-4bf2-b41e-43fecb3e45c5.png)

Olga claims that her likeness may be used for fraudulent promotional activities, becoming a means for profiteers to make money.

# Stolen Faces: Becoming Profitable Anchors for Others

Livestream e-commerce typically involves hosts showcasing a range of products to their followers, who can then purchase them through integrated buy buttons. Despite sounding somewhat mundane, these shows attract a large audience: last year, over 1 trillion yuan worth of goods were sold through live commerce, and there are now over 1 million people acting as livestream hosts in China.
![2024041602.png](https://cdn.aisecurius.com/images/4ce/4ceee8b2-4394-4b2f-92d7-8fcc4368ef34.png)
The integration of AI clones and face-swapping-generated virtual influencers into Chinese e-commerce and livestreaming represents a significant evolution in brand engagement strategies. These AI digital humans can be tailored to clients' needs, capable of livestreaming 24 hours a day, achieving a "non-stop" work ethic to mimic the effect of human hosts even during "non-peak hours." Moreover, these virtual hosts enable brands to precisely control their messaging, ensuring consistent brand representation and operational efficiency. Compared to traditional influencer partnerships, they offer potentially lower costs, less unpredictability, and very low operating costs. If there are no bizarre interaction issues, digital personas might never reveal themselves.

Creating virtual hosts is extremely simple, requiring only a single photo for cloning, with the fastest turnaround being just 60 seconds.

However, this innovative sales approach brings about numerous complex implications. Worse, it has sparked the misuse of technology, cloning faces without the consent of the individuals involved for commercial purposes or fraud. Olga Loiek's experience is a notable example. Attackers only need to download photos, videos, audio, and activities shared by the victim on social media, requiring only 30 seconds to 1 minute of sample material to create highly realistic voices and images, thus recreating a presenter for video livestreaming.

[Dingxiang Defense Cloud Business Security Intelligence Center's Intelligence Digest on "DEEPFAKE" Threat Research and Security ](https://www.aisecurius.com/blog/article/55)Strategies points out that another major factor contributing to the proliferation of AI fraud is the accessibility of AI tools, making the creation of false videos and images even easier. Especially with the emergence of Cybercrime as-a-Service, ordinary people can easily purchase AI fraud services or technologies. Attackers use a range of media, including social media, email, remote meetings, online recruitment, and news information, to carry out various AI fraud attacks against businesses and individuals.


# User: How to prevent face theft?


The security threats brought by AI are increasingly attracting attention and quickly becoming a new means of threat, posing unprecedented risks to both businesses and individual users.

[The Intelligence Digest on "DEEPFAKE" Threat Research and Security Strategies suggests that](https://www.aisecurius.com/blog/article/55) to prevent and combat AI fraud, it is necessary to effectively identify and detect AI-generated content on one hand, and prevent the exploitation and spread of AI fraud on the other. This requires not only technical countermeasures but also complex psychological warfare and an increase in public security awareness. Therefore, enterprises need to strengthen digital identity recognition, review account access permissions, and minimize data collection. Additionally, enhancing employees' awareness of how to detect AI threats is crucial.


## 1. Establish an AI-driven security tool system. 
Implement anti-fraud systems that combine manual review with AI technology to enhance automation and efficiency in detecting and responding to AI-based cyber attacks.


## 2. Strengthen identity verification and protection.
 This includes enabling multi-factor authentication, encrypting data at rest and in transit, and implementing firewalls. Strengthen frequent verification for account activities such as remote logins, device changes, phone number changes, and sudden account activity spikes to ensure consistent user identity during usage. Compare and identify device information, geographic locations, and behavioral operations to detect and prevent abnormal activities.


## 3. Enhance account authorization control. 
Restrict access to sensitive systems and accounts based on the principle of least privilege, ensuring access only to the resources necessary for their role, thereby minimizing the potential impact of account hijacking and preventing unauthorized access to your systems and data.


## 4. Stay informed about the latest technology and threats.
 Stay updated on the latest developments in AI technology to adjust security measures accordingly. Continuous research, development, and updates of AI models are crucial to maintaining a leading position in the increasingly complex security landscape.


## 5. Reduce the sharing of sensitive information on social media.
 Minimize or eliminate sharing sensitive information such as account services, family members, transportation, job positions, etc., on social media to prevent fraudsters from downloading and forging images and voices for identity fraud.

Traditional security tools and measures are no longer effective in defending against fraud threats based on AI. [Comprehensive risk prevention and control measures are required before, during, and after identifying fraud risks. Dingxiang's latest upgraded anti-fraud ](https://www.aisecurius.com/blog/article/60)technology and security products can help enterprises build a multi-channel, all-scenario, multi-stage security system, effectively combating new threats brought by AI. This includes App reinforcement for App security, atbCAPTCHA to prevent malicious AI registration and login, device fingerprinting to identify AI-forged devices, Dinsight to discover potential fraud threats and prevent complex AI attacks, and a comprehensive facial security threat perception solution to intercept "DEEPFAKE" attacks along the entire chain.

Through a multi-channel, all-scenario, multi-stage security system based on threat perception, security protection, data precipitation, model construction, and strategy sharing, various security services can meet different business scenarios, possess industry-specific strategies, and achieve precipitation and iteration based on their own business characteristics, achieving precise platform control to rapidly and effectively 

New Threat: Someone is Using Your Face for Online Livestreaming

In the state of New Jersey, insurance fraud ranks as the second largest white-collar non-violent crime, trailing only behind tax evasion. 

In the state of New Jersey, insurance fraud ranks as the second largest white-collar non-violent crime, trailing only behind tax evasion. A report from insurancefraud.org indicates that insurance fraud amounts to a staggering $308.6 billion annually. This not only results in a significant increase in insurance costs but also places a higher premium burden on policyholders.

![2024041003.jpg](https://cdn.aisecurius.com/images/34d/34d30220-cf1a-4e91-b8e0-2fc64e337a52.jpg)

Prosecutors responsible for handling insurance fraud cases emphasize in their report that the scale of perpetrators involved in insurance fraud is increasingly vast, severely impacting the normal operation of the insurance industry.

An investigation into insurance fraud reveals that nearly 9% of respondents do not view insurance fraud as a wrongdoing or criminal activity. They perceive it either as insurance companies extorting people or as rightful compensation for having paid sufficient premiums. Meanwhile, a significant proportion of respondents (28.6%) hold the viewpoint that insurance fraud is either not truly a crime (8.5%) or constitutes a business practice without genuine victims (20.1%). Additionally, defenders of insurance fraud may argue on behalf of deceiving insurance companies, viewing it as a victimless crime.

For the insurance industry, agents and companies must be prepared for the increasing frequency of claims. The emergence of AI tools brings about a series of complex issues. It not only creates more sophisticated fraudulent content for phishing but also has the capability to steal sensitive data such as insurance policy accounts, thereby affecting the accuracy of insurance assessments and impacting claims payout rates.

Of particular note is the fact that AI has become a powerful tool for insurance fraud. For instance, some insurance companies no longer accept photo claims as they could be doctored. Falsified photos depicting damages to specific makes and models of cars, which are not genuine, can be used to extract claim payments. AI fraud also extends to life insurance scams. Attackers leverage AI to easily mimic a person's photo and voice, fabricating a wealth of data to bypass insurance companies' voice recognition technologies and initiate policies for non-insured individuals or non-beneficiaries.

In the face of numerous threats posed by AI, the insurance industry needs to strengthen preventive measures and enhance fraud detection capabilities. Additionally, cooperation with technology companies is crucial to jointly address this challenge.

AI: Making Insurance Fraud More Complex, Now the Second Largest White-Collar Non-Violent Crime in the US

In mainstream scenarios, AI poses a significant threat to digital trust and integrity and has the potential to disrupt the relationship between customers and financial institutions.

According to McKinsey & Company's data, AI-based identity fraud has become the fastest-growing type of financial crime in the United States and is on the rise globally. Research from the UK's GDG indicates that over 8.6 million people in the UK use false or others' identities to obtain goods, services, or credit.

![2024041001.png](https://cdn.aisecurius.com/images/400/400b5b8f-6ad4-4105-9d72-fa87c4ff4ecf.png)

[The US Department of the Treasury recently emphasized in a report titled](https://www.aisecurius.com/blog/article/63) "Specific AI-Enabled Custodial Cybersecurity Risks in the Financial Services Industry" that AI's development makes it easier for cybercriminals to use deepfakes to impersonate financial institution clients and access accounts. The report states, "Generative AI can help existing threat actors develop and test more sophisticated malicious software, providing them with complex attack capabilities previously available only to the most resourceful actors. It can also help lower-skilled threat actors develop simple but effective attacks." According to Deloitte's data, 91% of cyberattacks start with phishing emails.

Undoubtedly, in today's digital environment, AI has altered the nature of identity fraud. In mainstream scenarios, AI poses a significant threat to digital trust and integrity and has the potential to disrupt the relationship between customers and financial institutions.


# Attackers: Five Common AI Attack Methods


AI provides new tools for all threat actors, and attackers are using AI to target employees, create phishing emails, impersonate supply chain partners, and even create deepfake chief financial officers for video conferences. Analysis from the Dingxiang Defense Cloud Business Security Center has identified the following five types of AI-based attacks used by attackers for forging digital identities, credit card theft, forging documents, and other financial fraud.

![2024041601.png](https://cdn.aisecurius.com/images/e3e/e3eeb951-aeac-45bc-93ec-4411d5c0790b.png)
## 1. AI Cloned Voice for Phone Scams: 
Attackers mimic the voices of company executives or colleagues, requesting victims to transfer funds or provide account passwords, potentially resulting in theft of enterprise funds or leakage of sensitive information, causing financial losses and reputational damage to the company.


## 2. AI Face Swapping for Online Meetings and Video Calls:
 Attackers engage in remote business meetings or video calls using forged identities, requesting victims to transfer funds or provide account passwords, potentially resulting in theft of personal or enterprise funds, leakage of information related to significant contracts or transactions, and subsequently affecting business interests and reputation.


## 3. AI-Generated Fake Financial Websites and Clone Financial Apps
: Victims may provide personal bank account information or credit card information under the mistaken belief that they are accessing legitimate financial institution websites or apps, resulting in theft of funds or identity theft, causing significant financial losses and credit crises.


## 4. AI-Enhanced Phishing Emails:
 Attackers use AI to generate more realistic email content, making it easier to deceive victims' trust, such as spoofing emails from legitimate institutions, requesting victims to click on malicious links or download attachments, thereby disclosing sensitive information or controlling victims' computers, leading to financial losses.


## 5. AI-Generated Fake Images, Text, and Videos: 
False financial information may spread widely on social media, news websites, and other platforms, misleading consumers into purchasing fraudulent financial products or insurance, resulting in investment losses or purchasing unsecured fake insurance, leading to significant financial losses.

These fraudulent methods pose extremely serious financial risks, exploiting people's trust in identity recognition and information authenticity, resulting in issues such as financial losses and personal information leaks.


# Banks: Defense Strategies Needed


[The Intelligence Special Issue "AI Face-Swapping"](https://www.aisecurius.com/blog/article/55) Threat Research and Security Strategies believe that to prevent and combat AI fraud, it is necessary to effectively identify and detect AI-generated content while also preventing the exploitation and spread of AI fraud. This requires not only technical countermeasures but also complex psychological warfare and an increase in public awareness of security. Therefore, enterprises need to strengthen digital identity recognition, review account access permissions, and minimize data collection. At the same time, enhance employee awareness of how to detect AI threats.


## 1. Preventing Business-Focused Fraud Activities Based on AI:
 AI can monitor and analyze various aspects of the process from supplier interaction to payment, providing real-time risk assessments and trust scores, issuing alerts to warn of potential fraudulent activities, and seamlessly integrating into current processes. AI can also automatically monitor and analyze large amounts of transaction data, allowing human investigators to focus more on actual fraud events. This automation can reduce manual workload, decrease the likelihood of errors and fraud, and ensure faster response times. By analyzing received invoices, new supplier requests, emails, documents, and bank statements in real-time, AI can help prevent fraud and detect abnormal communication patterns, payment details, and document structures.


## 2. Building a Multi-Channel, Comprehensive, and Multi-Stage Security System:
 [Dingxiang's latest upgraded anti-fraud technology and security products can help enterprises establish a multi-channel, ](https://www.aisecurius.com/blog/article/60)comprehensive, and multi-stage security system to combat new threats posed by AI. This includes securing apps with app reinforcement, preventing malicious registration and login with atbCAPTCHA, identifying AI-forged devices with device fingerprinting, uncovering potential fraud threats, preventing complex AI attacks with Dinsight, and intercepting "AI face-swapping" attacks with a comprehensive facial security threat perception solution. Through a multi-channel, comprehensive, and multi-stage security system based on threat perception, security protection, data accumulation, model construction, and strategy sharing, various security services can meet different business scenarios, possess industry-specific strategies, and achieve accumulation and iteration based on their own business characteristics, enabling precise control and protection of the platform to effectively respond to AI attacks and provide personalized protection.


## 3. Strengthening Identity Verification and Protection:
 This includes enabling multi-factor authentication, encrypting data at rest and in transit, and implementing firewalls. Strengthen frequent verification for activities such as account logins from different locations, device changes, phone number changes, and sudden activity on dormant accounts to ensure consistency in user identity during usage. Comparing device information, geographic location, and behavioral operations can identify and prevent abnormal operations.

## 
4. Strengthening Account Authorization Control:
 Limit access to sensitive systems and accounts based on the principle of least privilege, ensuring access to only the resources required by their roles, thereby reducing the potential impact of account theft and preventing unauthorized access to your systems and data.


## 5. Continuously Understanding Latest Technologies and Threats
: Stay updated on the latest developments in AI technology as much as possible to adjust safeguard measures accordingly. Continuous research, development, and updates to AI models are crucial for maintaining a leading position in an increasingly complex security situation.


## 6. Continuously Educating and Training Employees on Security
: Continuously provide training on AI technology and related risks, help employees identify and avoid AI attacks and other social engineering risks through simulated attacks, vulnerability discovery, security training, etc., remain vigilant, report anomalies quickly, and significantly improve the organization's ability to detect and respond to deepfake threats.

Banks: The Five Most Common AI Attack Methods

like a coin with two sides, AI is both a boon and a threat to companies and their clientele in security. AI can analyze data patterns and identify anomalies

With the rapid evolution of cyber threats, experts project that by 2024, cybercrime will cost US companies up to $452 billion, a figure expected to escalate rapidly in the coming years. Beyond just cybersecurity measures, critical to secure operations is business security. Today, AI has become an indispensable tool in security, ranging from phishing detection tools to AI chatbots capable of responding to cybersecurity queries.
![2024040207.png](https://cdn.aisecurius.com/images/1a9/1a987802-f054-4655-85bc-3fbea4e7821e.png)

However, like a coin with two sides, AI is both a boon and a threat to companies and their clientele in security. AI can analyze data patterns and identify anomalies, alerting to potential risks that might otherwise be overlooked, aiding in proactive measures and propelling the adoption of cutting-edge defensive network technologies. Yet, simultaneously, AI has become a tool for attackers to craft genuine scams and penetrate corporate networks.

# 
Discovering Potential Threats -> Becoming an Internal Threat


One of AI's notable advantages in cybersecurity is its ability to predict and preempt potential threats. By analyzing data patterns and recognizing anomalies, it can provide alerts on risks, preventing crucial information from slipping through the cracks. However, this predictive capability also makes it a useful tool for attackers.

It's precisely this predictive capability that makes AI a useful tool for attackers. They can exploit AI to craft more sophisticated scams, circumventing traditional security measures like email anti-spam filters. Additionally, attackers can leverage AI-driven social engineering strategies to create seemingly personalized and convincing false messages or requests, enticing users to divulge sensitive information or access secure systems.


# Precise Threat Detection -> Disrupting Normal Operations


AI in threat detection is a double-edged sword. It excels at providing real-time responses to security incidents, automating processes, saving time, and minimizing damage.

However, this rapid response system can sometimes backfire. The automated, swift response system can sometimes be counterproductive because AI's determination of what constitutes a threat isn't always accurate, potentially leading to false positives and disrupting business operations. Moreover, if threat actors understand its response patterns, they could manipulate the system to trigger desired reactions, rendering AI's speed counterproductive.

# 
Deep Learning -> Data Poisoning


Furthermore, AI systems continuously learn and evolve, making them more effective at handling increasing volumes of data, better identifying and addressing the ever-changing landscape of network risks.

Yet, paradoxically, this power can be exploited. If threat actors feed manipulated data to AI systems (i.e., "data poisoning"), it could distort their learning process, leading to inaccurate models unable to detect actual threats, even considering malicious activities as safe.

In conclusion, while AI brings numerous benefits to cybersecurity, each advantage comes with a set of challenges. We need to judiciously employ AI technologies and continually strengthen cybersecurity defense measures to ensure these powerful tools enhance security rather than become vulnerabilities.

AI: A Double-Edged Sword in Enterprise Security

The report shows that among various AI threats facing financial institutions, the most significant threats are social engineering attacks and forged identity fraud.

In November 2023, fraudsters disguised themselves as a close friend of Mr. Guo from a certain tech company, swindling 4.3 million RMB. In December 2023, parents of a student studying abroad received a ransom video claiming their child was kidnapped, demanding a ransom of 5 million RMB. In January 2024, 19 multinational corporation employees in Hong Kong fell victim to phishing scams, resulting in a loss of 200 million HKD. In February 2024, the AI virus "GoldPickaxe" emerged, capable of stealing facial information and transferring users' bank balances...

![2024041001.png](https://cdn.aisecurius.com/images/400/400b5b8f-6ad4-4105-9d72-fa87c4ff4ecf.png)


According to McKinsey & Company, AI-based identity fraud has become the fastest-growing type of financial crime in the United States and is on the rise globally. Research by the UK's GDG indicates that over 8.6 million people in the UK are using false or others' identities to obtain goods, services, or credit.

A recent report from the US Department of the Treasury titled "Specific AI-Related Custodial Cybersecurity Risks in the Financial Services Industry" emphasizes that the development of AI makes it easier for cybercriminals to use deepfakes to impersonate financial institution customers and access accounts. The report states, "AI can help existing threat actors develop and test more sophisticated malicious software, providing them with complex attack capabilities previously only available to the most resourceful actors. It can also assist less skilled threat actors in developing simple but effective attacks."

The report shows that among various AI threats facing financial institutions, the most significant threats are social engineering attacks and forged identity fraud. On one hand, attackers use generative AI to create more convincing phishing emails, write phishing website content, and deceive financial institution staff. On the other hand, attackers leverage AI to mimic customers' photos, voices, and videos to bypass identity verification processes.

Related statistics indicate that in 2023, fraud incidents based on "AI face swapping" grew by 3000%, and the number of AI-based phishing emails increased by 1000%.

# Attacker: Process Record of Utilizing AI Threats

Attackers employ a series of media including social media, emails, remote meetings, online recruitment, news updates, etc., to conduct AI attacks on financial institutions. In a recent intelligence digest titled ["AI Face-Swapping Threat Research and Security Strategies" ](https://www.aisecurius.com/blog/article/55)released by the Dingxiang Defense Cloud Business Security Intelligence Center, a detailed analysis of a financial fraud case involving "AI face-swapping" was conducted, outlining the entire process of fraud by the scammers.
![2024031205.jpg](https://cdn.aisecurius.com/images/a7d/a7df2f97-bcaf-4a48-aba2-9a4e9a4342fb.jpg)


## Phase One:
 Gaining the victim's trust. The fraudsters contact the victim through methods such as text messages, social tools, social media, phone calls, etc. (for example, being able to directly mention the victim's name, family, phone number, workplace, address, ID number, colleagues or partners, and even some experiences), and gain trust.


## Phase Two:
 Recording the victim's facial information. The fraudsters engage with the victim via social tools, video conferences, video calls, etc., to capture the victim's facial information during the video call (facial expressions, nodding, tilting, opening mouth, blinking, etc.) for the creation of fake videos and portraits using "AI face-swapping" technology. During this process, the victim may also be induced to set up call forwarding or download malicious app software, which can forward or intercept calls from the bank's customer service or calls.


## Phase Three
: Logging into the victim's bank account. The fraudsters use the bank's app to log into the victim's bank account, submit the fake video and portrait created through "AI face-swapping" for facial recognition authentication by the bank, and intercept the SMS verification code and risk alerts sent by the bank to the victim's phone.


## Phase Four:
 Draining the victim's bank balance. The calls from the bank are redirected to a virtual number pre-set by the fraudster, impersonating the victim. After successfully passing the manual verification by the bank's customer service personnel, the balance in the victim's bank card is smoothly transferred out.

Overall, utilizing AI for fraud is divided into the above four steps, each intricately linked. Victims who cannot discern or judge effectively, and platforms that cannot provide efficient warnings and interceptions, are susceptible to falling into the trap set by fraudsters.


# Banks: How to Guard Against AI Attacks


This is a new challenge facing the entire industry. Faced with attackers who have industrialized and mature attack patterns, the intelligence digest "AI Face-Swapping Threat Research and Security Strategies" suggests that to prevent and combat AI fraud, it is necessary to effectively identify and detect AI-generated content on one hand, and prevent the exploitation and spread of AI fraud on the other. This requires not only technical countermeasures but also complex psychological warfare and an increase in public safety awareness. Therefore, enterprises need to strengthen digital identity recognition, review account access permissions, and minimize data collection. Additionally, enhancing employee awareness on detecting AI threats is crucial![2024041002.jpg](https://cdn.aisecurius.com/images/9aa/9aa8d1b6-7509-474b-9f40-3b11f89bb7ea.jpg).
## 1. Build a comprehensive security system with multi-channel
annel, full-scenario, and multi-stage protection. Covering various channel platforms and business scenarios, providing security services such as threat perception, security protection, data deposition, model construction, and strategy sharing, capable of meeting different business scenarios, possessing industry-specific strategies, and achieving sedimentation and iteration evolution based on its own business characteristics, realizing precise platform prevention and control. This involves deploying multiple security solutions at different points in the network to protect oneself from various threats, devising comprehensive incident response plans to quickly and effectively respond to AI attacks, and implementing personalized protection
## 2. Construct an AI-driven security tool system
ystem. A fraud prevention system combining manual review with AI technology to enhance automation and efficiency, detecting and responding to AI-based cyber attacks
## 3. Strengthen identity verification and protection.
tion. This includes enabling multi-factor authentication, encrypting data at rest and in transit, and implementing firewalls. Strengthen frequent verification for account remote logins, device changes, phone number changes, and sudden activity in dormant accounts to ensure consistency of user identity during usage. Compare and identify device information, geographic location, and behavioral actions to detect and prevent abnormal operations
## 4. Strengthen account authorization control. 
rol. Restrict access to sensitive systems and accounts based on the principle of least privilege to ensure access to resources required by their role, thereby minimizing the potential impact of account hijacking and preventing unauthorized access to your systems and data
## 5. Continuously stay informed about the latest technologies and threats
reats. Keep abreast of the latest developments in AI technology as much as possible to adjust security measures accordingly. Continuous research, development, and updating of AI models are crucial to maintaining a leading position in an increasingly complex security landscape
## 6. Continuously educate and train employees on security.
rity. Provide ongoing training on AI technology and its related risks, helping employees identify and avoid AI attacks and other social engineering risks through simulated attacks, vulnerability discovery, security training, etc. Encourage vigilance and prompt reporting of abnormal situations, significantly improving the organization's ability to detect and respond to deepfake threats.

To guard against AI-based threats, effective identification and detection of AI threats are necessary on one hand, while preventing the exploitation and spread of AI fraud on the other. This requires not only technical countermeasures but also complex psychological warfare and an increase in public safety awareness.


# Security: Defense Products Against AI Fraud


Traditional security tools and measures are no longer effective in defending against fraud threats brought by AI. [To timely identify fraud risks, comprehensive risk prevention and control need to be carried out before, during, and after events. Dingxiang's latest upgraded anti-fraud technology and security products can build a multi-channel, full-scenario, and multi-stage security system for enterprises, effectively combating new threats brought by AI](https://www.aisecurius.com/blog/article/60).


## Securing App Security to Prevent AI Automation Crackdown
. Dingxiang's adaptive iOS reinforcement, based on graph neural network technology, automatically selects appropriate methods for obfuscation based on the characteristics of different code blocks, significantly increasing the difficulty of reverse analysis and reducing computational overhead by 50%. Through encryption and obfuscation engines, App code can be encrypted, obfuscated, and compressed, greatly increasing the security of App code, effectively preventing products from being cracked, copied, or repackaged by attackers.


## Securing Account Security to Prevent AI Malicious Registration and Login
. Dingxiang atbCAPTCHA, based on AIGC technology, prevents AI brute force cracking, automated attacks, and phishing attacks, effectively preventing unauthorized access, account hijacking, and malicious operations, thereby protecting system stability. It integrates 13 verification methods and multiple control strategies, aggregating 4380 risk policies, 112 risk intelligence categories, covering 24 industries and 118 risk types. Its precision control accuracy reaches 99.9%, and it can quickly realize the transformation from risk to intelligence. It also supports seamless verification for secure users and reduces real-time countermeasures to within 60 seconds, further improving the convenience and efficiency of digital login services.


## Identifying AI-Forged Devices to Prevent AI Malicious Fraud
. Dingxiang device fingerprinting generates a unified and unique device fingerprint for each device by internally linking multiple device information. It builds a multi-dimensional identification strategy model based on devices, environments, and behaviors to identify risk devices such as virtual machines, proxy servers, and manipulated emulators. It analyzes whether devices have multiple account logins, frequent IP address changes, or frequent device attribute changes, detecting abnormal behaviors or behaviors inconsistent with user habits, tracking and identifying fraudster activities, helping enterprises achieve operational consistency across all channels under the same ID, and assisting in cross-channel risk identification and control.


## Unearthing Potential Fraud Threats to Prevent Complex AI Attacks.
 Dingxiang Dinsight helps enterprises conduct risk assessment, anti-fraud analysis, and real-time monitoring to improve the efficiency and accuracy of risk control. The average processing speed of Dinsight's daily risk control strategies is within 100 milliseconds, supporting configuration-based access and sedimentation of multi-party data. It can achieve self-performance monitoring and self-iteration of risk control mechanisms based on mature indicators, strategies, models, and deep learning technologies. Paired with the Xintell intelligent model platform, it can automatically optimize security policies for known risks and configure support for risk control strategies for different scenarios based on risk control logs and data mining of potential risks. Its standardized approach to complex data processing, mining, and machine learning processes provides one-stop modeling services from data processing, feature derivation, model construction to final model deployment.


## Intercepting "AI Face-Swapping" Attacks to Ensure Facial Application Securit
y. Dingxiang's full-chain, panoramic facial security threat perception solution intelligently verifies multiple dimensions of information such as device environment, facial information, image authenticity, user behavior, and interaction status. It quickly identifies over 30 types of malicious attacks, such as injection attacks, live spoofing, image forgery, camera hijacking, debugging risks, memory tampering, Root/Jailbreak, malicious Rom, and simulator operations, and automatically blocks operations after discovering forged videos, fake face images, or abnormal interactive behavior. It can also flexibly configure the intensity and friendliness of video verification, achieve seamless verification for normal users, and dynamically strengthen verification for abnormal users.


Two Major AI Threats Facing Banks: Identity Forgery and Social Engineering Attacks

This new type of AI-based threat not only poses a threat to users' personal information and data security but may also seriously affect users' safety and privacy rights. 


In recent years, with the widespread application of artificial intelligence technology, the security threats it brings have increasingly attracted people's attention. In February 2024, [a malicious software named "GoldPickaxe" appeared, which, in its iOS version](https://www.aisecurius.com/blog/article/51), deceives users into performing facial recognition and submitting identity documents, using deepfake technology to create false facial videos, thus defrauding users' bank account information, posing significant security risks to users.
![2024040802.jpeg](https://cdn.aisecurius.com/images/ca4/ca454210-b1c3-48d4-9361-ccef4ca47758.jpeg)
This new type of AI-based threat not only poses a threat to users' personal information and data security but may also seriously affect users' safety and privacy rights. The development of AI makes it easier for false information and fraudulent activities to spread. Attackers may use this technology to create false videos or images, damaging the reputation of individuals or organizations, and even causing social chaos. Additionally, social engineering attacks are becoming increasingly rampant, with attackers using AI-generated fake accounts or information for phishing and online scams, leading to privacy breaches and financial losses for users.

To address the new security challenges brought by AI, the ShikaoBang App has decided to adopt [Dingxiang iOS Reinforcement. Dingxiang iOS Reinforcement is an adaptive reinforcement solution based on graph neural network technology](https://www.aisecurius.com/blog/article/60). It analyzes and extracts features from the App code, automatically selecting appropriate methods for encryption, obfuscation, and compression, increasing the difficulty of reverse engineering the code, effectively preventing the App from being cracked, duplicated, or repackaged by attackers. Additionally, the use of encryption and obfuscation engines further enhances the security of the App.
![2024040806.jpg](https://cdn.aisecurius.com/images/427/42701886-d567-4eb5-8a7e-aa5294dc1abe.jpg)

As a specialized learning client for professional exams and surrounding public recruitment exams, the ShikaoBang App is developed and operated by Jinbiaoshai Education, providing rich learning resources for exam candidates. It integrates specialized question banks, past exam papers, series of books, and high-quality courses, and also provides personalized learning services based on users' learning situations and needs. Users can comprehensively prepare for exams through functions such as video explanations, question bank exercises, exam information, and participate in learning discussions, exchange experiences, and interact with other candidates, enhancing the fun and motivation of learning.

Dingxiang iOS Reinforcement provides powerful security protection for the ShikaoBang App. The reinforced App not only becomes more secure and stable but also better meets the requirements of the Apple Store review, providing users with a reliable learning environment and learning experience, helping users prepare for exams smoothly.

Dingxiang iOS Reinforcement to Provide a Secure Learning Platform for ShikaoBang Students

Additionally, research by Home Security Heroes found that out of 95,820 Deepfake online videos, 98% were categorized as pornographic content. Furthermore,


Deepfake is a technology that utilizes artificial intelligence to replace one person's face with another in images or videos. The "Deepfake" technique involves various technologies and algorithms that can generate highly realistic visuals. By combining elements of Deepfake's false content with genuine information, it can be used for identity forgery, spreading misinformation, creating fake digital content, and various forms of deception.
![2024032501.png](https://cdn.aisecurius.com/images/c74/c741d39b-04b0-4a84-b517-a4487252b3c5.png)

According to a report by KPMG, there has been a 900% increase in online-provided Deepfake videos. Additionally, research by Home Security Heroes found that out of 95,820 Deepfake online videos, 98% were categorized as pornographic content. Furthermore, 99% of these Deepfake videos featured women, with 94% of them involving individuals from the entertainment industry. A study by cybersecurity provider ESET revealed that 61% of women in the UK are concerned about becoming victims of Deepfake pornography.

A special edition o[ "DEEPFAKE Threat Study and Security Strateg" ](https://www.aisecurius.com/blog/article/55y)egy" released by the Dingxiang Defense Cloud Business Security Intelligence Center highlights that another major factor contributing to the proliferation of Deepfake is the accessibility of tools that make creating fake videos and images easier. Home Heroes' survey indicates that there are currently at least 42 user-friendly tools available for making Deepfake, with these tools being searched online tens of millions of times each month. Furtherm[with the emergence of Cybercrime-as-a-Service, ordinary individuals can easily purchase Deepfake services or technolo.](https://www.aisecurius.com/blog/article/32gy)logy.


# Security Measures: How to Prevent the Spread of AI-Generated Pornographic Videos?


Attackers employ various mediums such as social media, email, remote meetings, online recruitment, and news sources to conduct a range of Deepfake fraudulent attacks on both enterprises and individuals.

["DEEPFAKE Threat Study and Security Strateg"](https://www.aisecurius.com/blog/article/55y)egy" intelligence digest suggests that combating Deepfake fraud requires effective identification and detection of Deepfake manipulated content, as well as measures to prevent the exploitation and dissemination of Deepfake fraud. This not only demands technical countermeasures but also potentially involves complex psychological tactics and raising public awareness of safety. Additionally, regulatory bodies need to impose penalties on producers of "Deepfake" pornography.


## Individuals should take precautions to prevent the theft and replication of their information. 
Avoid sharing personal photos, voice recordings, and videos on social media platforms, minimize the disclosure of sensitive information such as account details, family members, travel schedules, and job positions, to mitigate the risk of fraudsters stealing and forging identities. In the event of identifying false Deepfake videos and images, promptly report them to social media administrators and law enforcement agencies.


## AI tools can enhance facial security usage. 
The ["European Union Artificial Intelligence Act"](https://www.aisecurius.com/blog/article/56) mandates that providers of AI tools have an obligation to design these tools in a manner that permits detection of synthetic/fake content, adding digital watermarks, and timely restricting the use of digital identity cards for identity verification purposes. The principle behind AI watermarking involves embedding unique signals into the output of artificial intelligence models, which can be images or text, aimed at identifying the content as AI-generated to aid in effective recognition by others.


## Social media platforms should restrain the dissemination of pornographic videos.
 Social media platforms typically position themselves merely as conduits for content. The Australian Competition and Consumer Commission (ACCC) believes that platforms like Facebook should be held accountable as accessories to frauds and should promptly remove Deepfake content used for fraudulent purposes.

YouTube stipulates that anyone uploading videos to the platform must disclose certain uses of synthetic media to inform viewers that what they are witnessing is not genuine, thereby reducing the fraudulent exploitation of Deepfake videos. Similarly, on Facebook, any content generated by Deepfake may display an icon clearly indicating that the content is AI-generated.


## Media services should enhance facial application security.
 The Dingxiang Comprehensive Face Security Threat Perception Solution is capable of effectively detecting and identifying false Deepfake videos and images. It conducts real-time risk monitoring for facial recognition scenarios and critical operations (such as hijacked cameras, device forgery, screen sharing, etc.), then verifies through multi-dimensional information such as facial environment monitoring, liveness recognition, image authentication, and intelligent verification. Upon discovering forged videos or abnormal facial information, it can automatically block abnormal or fraudulent operations.
![2024031903.png](https://cdn.aisecurius.com/images/e06/e06b5696-4cde-4099-bdfe-b33389a1def8.png)

## Establishing a multi-channel, full-scenario, multi-stage protection security system. 
Covering various channel platforms and business scenarios, providing security services such as threat perception, security protection, data precipitation, model construction, strategy sharing, etc., can meet different business scenarios, possess industry-specific strategies, and achieve precipitation and iteration based on its own business characteristics, enabling precise platform prevention and control, thereby effectively addressing Deepfake attacks and providing personalized protection. Dingxiang's latest upgraded anti-fraud technology and security products build a multi-channel, full-scenario, multi-stage security system for enterprises to systematically combat new threats brought by AI, including: [iOS reinforcement with adaptive confusion reinforcement, seamless verification capable of generating unlimited images, device fingerprints supporting cross-platform unified device information, and Dinsight risk control engine capable of intercepting complex attacks and uncovering potential threats.](https://www.aisecurius.com/blog/article/60)


## Regulatory bodies should strengthen penalties for the production and dissemination of Deepfake pornography.
 Imposing legal consequences on individuals who create or disseminate [Deepfake manipulated content can prevent the spread of harmful content and hold perpetrators accountable for their actions](https://www.aisecurius.com/blog/article/56). Qualifying Deepfake fraud as a criminal offense can serve as a deterrent, preventing the misuse of this technology for fraudulent or other malicious purposes. It is an effective method to mitigate the harmful impacts of Deepfake technology, and those who intentionally create or promote harmful Deepfake dissemination should be subject to criminal penalties.

Deepfake pornography troubles the UK, with 60% of women fearing becoming victims

By conducting comprehensive risk prevention and control measures before, during, and after incidents, Dingxiang aims to prevent AI threats that have formed industrialized and mature attack patterns.

On April 2nd, in the latest installment of the "Business Security Seminar," Dingxiang's data scientist, Yilong, and senior solution expert, Fengyu, delved into the current highly scrutinized AI threats and facial recognition risks, providing in-depth insights and detailed introductions to the latest anti-fraud technologies and security products aimed at combating AI threats.
![2024040209.jpg](https://cdn.aisecurius.com/images/abd/abd96723-306e-4b50-99c5-1757852a5e20.jpg)


# AI Threats: Posing New Challenges for Enterprises


In November 2023, fraudsters disguised themselves as a close friend of Mr. Guo from a certain tech company, swindling 4.3 million RMB. In December 2023, parents of a student studying abroad received a ransom video claiming their child was kidnapped, demanding a ransom of 5 million RMB. In January 2024, 19 multinational corporation employees in Hong Kong fell victim to phishing scams, resulting in a loss of 200 million HKD. In February 2024, the AI virus "GoldPickaxe" emerged, capable of stealing facial information and transferring users' bank balances...
![2024030101.png](https://cdn.aisecurius.com/images/bd5/bd5e497c-540f-4253-af1b-57048fb6bce2.png)

With the widespread application of AI, its associated security threats are increasingly drawing attention and rapidly becoming a new modus operandi, posing unprecedented risks to both enterprises and individual users. Data indicates that in 2023, fraud incidents based on "DEEPFAKE" increased by 3000%, while the number of AI-generated phishing emails grew by 1000%.

[Dingxiang's Defense Cloud Business Security Intelligence Center has released the "DEEPFAKE Threat Research and Security Strategy" ](https://www.aisecurius.com/blog/article/55)intelligence bulletin, which outlines that attackers can utilize AI to fabricate text, images, videos, and audio, creating realistic fake photos, videos, identification documents, financial accounts, statistical data, as well as generating new types of Trojan viruses, cracking account passwords, and launching automated cyber attacks. By combining AI-fabricated false content with elements of real information, attackers can spread rumors, impersonate acquaintances for telecommunications fraud, apply for bank loans under false pretenses, impersonate others for job interviews and employment, mimic logins to steal bank balances, and engage in phishing fraud.
![2024040207.png](https://cdn.aisecurius.com/images/1a9/1a987802-f054-4655-85bc-3fbea4e7821e.png)

Through a range of media platforms such as social media, email, remote meetings, online recruitment, and news outlets, and with the aid of emulators, device alteration software, IP spoofing, registration generators, and group control tools, fraudsters can conduct various types of scams.


# Defending Against AI Attacks Requires New Security Strategies


Enterprises will need to strengthen digital identity recognition, review account access permissions, and minimize data collection. Additionally, there's a need to enhance employee awareness regarding the detection of AI threats.

Multi-channel, comprehensive, multi-stage security system. Covering various platform channels and business scenarios, providing security services such as threat perception, security protection, data accumulation, model construction, and strategy sharing, capable of meeting different business scenarios, possessing industry-specific strategies, and being able to accumulate and evolve based on their own business characteristics, achieving precise platform control. This involves deploying multiple security solutions at different points in the network to protect against various threats, devising comprehensive incident response plans to effectively and swiftly respond to AI attacks, and implementing personalized protection.


## AI-driven security tool system. 
Anti-fraud systems that combine manual review with AI technology to enhance automation and efficiency in detecting and responding to AI-based cyber attacks.


## Enhanced identity authentication and protection. 
This includes enabling multi-factor authentication, encrypting data at rest and in transit, and implementing firewalls. Strengthening frequent verification for activities such as account login from remote locations, device changes, phone number changes, and sudden activity on dormant accounts, with persistent checks to ensure consistency of user identity during usage. Comparing and identifying device information, geographic location, and behavioral operations to detect and prevent abnormal activities.
![2024040206.png](https://cdn.aisecurius.com/images/9ce/9cec24d7-8246-4d6d-9e55-57af93c5dd34.png)

## Principle of least privilege for account authorization.
 Restricting access to sensitive systems and accounts based on the principle of least privilege to ensure access to resources required by their roles, thereby reducing the potential impact of account hijacking and preventing unauthorized access to your systems and data.


## Continuous understanding of the latest technology and threats.
 Keeping abreast of the latest developments in AI technology to adjust security measures accordingly. Continuous research, development, and updating of AI models are crucial to maintaining a leading position in the increasingly complex security landscape.


## Continuous security education and training for employees.
 Ongoing training on AI technology and its associated risks, using methods such as simulated attacks, vulnerability discovery, and security training to help employees identify and avoid AI attacks and other social engineering risks. Maintaining vigilance and promptly reporting anomalies significantly enhances an organization's ability to detect and respond to deepfake threats.

To mitigate AI-based threats, it's necessary to effectively identify and detect AI threats while also preventing the exploitation and proliferation of AI fraud. This requires not only technological countermeasures but also complex psychological warfare and the enhancement of public safety awareness.


# Dingxiang: Full Product Line Upgrade to Combat AI Threats


In this live broadcast, Dingxiang's data scientist, Yilong, and senior solution expert, Fengyu, provided detailed insights into Dingxiang's latest upgraded anti-fraud technology and security products, aimed at systematically combating new threats posed by AI: adaptive obfuscation strengthening for iOS, seamless verification capable of generating infinite images, device fingerprinting supporting unified device information across platforms, Dinsight risk control engine capable of intercepting complex attacks and uncovering potential threats, and Dingxiang's comprehensive panoramic facial security threat perception solution tailored to address "DEEPFAKE" risks. These enhancements aim to establish a multi-channel, comprehensive, multi-stage security system for enterprises.
![2024040204.png](https://cdn.aisecurius.com/images/84e/84e46951-2573-4065-97a9-fee5bcf93f1b.png)
## Safeguarding App Security to Prevent Automated AI Hacking.
ing. Dingxiang's adaptive iOS reinforcement, based on graph neural network technology, analyzes and extracts code features to automatically select appropriate obfuscation methods based on different code blocks, significantly increasing the difficulty of reverse engineering and reducing computational performance consumption by 50%. Through encryption and obfuscation engines, App code is encrypted, obfuscated, and compressed to greatly enhance code security, effectively preventing attacks such as cracking, duplication, and repackaging.


## Ensuring Account Security to Prevent Malicious AI Registration and Login Attempts.
 Dingxiang's atbCAPTCHA, based on AIGC technology, prevents AI brute force attacks, automated attacks, and phishing attacks, effectively preventing unauthorized access, account hijacking, and malicious operations, thus protecting system stability. It integrates 13 verification methods and multiple prevention and control strategies, aggregating 4380 risk policies, 112 types of risk intelligence, covering 24 industries and 118 risk types. With a precision control rate of up to 99.9%, it enables rapid transformation from risk to intelligence. It also supports seamless verification for secure users, with real-time response and disposition capabilities within 60 seconds, further improving the convenience and efficiency of digital login services.


## Identifying AI-fabricated Devices to Prevent Malicious AI Frauds.
 Dingxiang Device Fingerprinting generates unified and unique device fingerprints for each device by integrating internal information from multiple devices. It builds multi-dimensional recognition strategy models based on device, environment, and behavior to identify risks such as virtual machines, proxy servers, and manipulated simulators. By analyzing whether devices exhibit abnormal behaviors or deviations from user habits, it tracks and identifies fraudulent activities to help enterprises achieve unified operation of the same ID across all scenarios and channels, facilitating cross-channel risk identification and management.

## 
Uncovering Potential Fraud Threats to Prevent Complex AI Attacks. 
Dingxiang Dinsight assists enterprises in risk assessment, anti-fraud analysis, and real-time monitoring to enhance risk control efficiency and accuracy. Dinsight's average processing speed for daily risk control strategies is less than 100 milliseconds, supporting configurable access and accumulation of data from multiple sources. It utilizes mature metrics, strategies, and model experience reserves, along with deep learning technology, to achieve self-performance monitoring and iterative risk control mechanisms. Paired with the Xintell intelligent model platform, it automatically optimizes security policies for known risks and configures different scene support risk control strategies based on risk control logs and potential risk data mining. With its standardized data processing, mining, and machine learning processes based on associative networks and deep learning technology, it provides a one-stop modeling service from data processing, feature derivation, model construction to final model deployment.


## Intercepting "DEEPFAKE" Attacks to Ensure Facial Application Security. 
Dingxiang's comprehensive panoramic facial security threat perception solution intelligently verifies multiple dimensions of information such as device environment, facial information, image authentication, user behavior, and interaction status. It rapidly identifies over 30 types of malicious attacks including injection attacks, live forgery, image forgery, camera hijacking, debugging risks, memory tampering, Root/jailbreak, malicious Rom, simulators, and system operation risks. Upon detecting forged videos, fake facial images, or abnormal interactions, it automatically blocks operations. It can flexibly configure video verification strength and user-friendliness to achieve seamless verification for normal users and dynamically strengthen verification for abnormal users.

By conducting comprehensive risk prevention and control measures before, during, and after incidents, Dingxiang aims to prevent AI threats that have formed industrialized and mature attack patterns.


Dingxiang: Full Product Line Upgrade to Combat AI Threats

overseas game apps face many security risks, such as account theft, password leakage, in-game payment fraud, and unauthorized charges. The leakage and loss of player personal information and game data are also issues that cannot be ignored.

According to an industry analysis report, the global mobile game market size is expected to reach $116.4 billion in 2023, of which overseas markets account for nearly 70%. In overseas markets, mature markets such as the United States, Japan, and South Korea are still the main sources of revenue, while emerging markets such as Southeast Asia, the Middle East, and Latin America are growing rapidly.
![2024033102.png](https://cdn.aisecurius.com/images/53a/53ab9033-398f-4008-824d-9b6e830d0685.png)

Medium and heavy games are performing strongly overseas, with strategy, role-playing, and shooting games being popular. Casual games are popular with players, but the market competition is fierce. High-quality games are more popular, and the excellence in game content, gameplay, art, and other aspects has become key.

To stand out in the highly competitive overseas game market, game products need to be competitive enough. It is crucial to carry out differentiated operations and localization adjustments for players with different regional, language, and cultural backgrounds. At the same time, using data analysis tools to understand player behavior and needs and optimize the game experience is also key to success.
![2024033103.jpg](https://cdn.aisecurius.com/images/e8a/e8a136cb-c6d7-4156-9a6f-62a66ceb008c.jpg)
However, overseas game apps face many security risks, such as account theft, password leakage, in-game payment fraud, and unauthorized charges. The leakage and loss of player personal information and game data are also issues that cannot be ignored.

Shenzhen Tiantianwan Company, as a professional overseas mobile game publisher, is committed to providing professional operation services for a wide range of game manufacturers. The company is based on products, with operation as the core and service as the purpose. It has a complete distribution process and working mode.

To ensure App security and improve App developer efficiency, Tiantianwan Company has configured Dingxiang App Security for its distributed games. This reinforcement technology can perform security checks on existing applications, discover and fix risk vulnerabilities in applications, and protect sensitive data, code obfuscation, code integrity, memory data, etc. It can prevent system vulnerabilities from affecting application security from the source and effectively prevent data information from leaking. After App security, the machine audit pass rate is increased, attracting more players to download.

Through Dingxiang App Security technology, Tiantianwan Company has provided solid security guarantees for overseas game operations, helping game developers achieve greater success in overseas markets. It provides a solid security guarantee for overseas game operations and provides comprehensive operation support for game manufacturers, providing strong support for improving the competitiveness and market share of game products.

App Security Empowers Overseas Game Operations

Commercial Espionage: The Full Process of Impersonating a Job Seeker in an Interview

July 2022,  the U.S. Federal Bureau of Investigation's Internet Crime Complaint Center (IC3) issued a new public service  announcement warning that scammers are increasingly using "deepfake" technology to impersonate job seekers in remote job  interviews, defrauding companies of payroll and stealing their trade secrets.
![2024031806.png](https://cdn.aisecurius.com/images/4fd/4fd34d1a-175b-4bce-a775-d9277239f31a.png)

The COVID-19 pandemic has driven the popularity of remote work. According to statistics, as of 2022, approximately 35% of people can work remotely full-time.  The rise of remote work has brought many benefits to employers  and employees.  However, it has also created opportunities for fraudsters to exploit.


# Commercial Espionage: The Full Process of Impersonating a Job Seeker in an Interview


From posting fake job postings to using "deepfake" technology to fake interviews,  to finally impersonating others to get hired, scammers have orchestrated a detailed fraud process.

![2024031807.png](https://cdn.aisecurius.com/images/7af/7afd55df-f839-42dc-b0d3-d3a99af80805.png)

First, scammers will cleverly post fake job postings on recruitment websites,  using them as bait to attract real job seekers to apply for their resumes.  This step is key for them to collect  information on real job seekers.

Next,  scammers will cleverly disguise themselves as job seekers and submit these fake resumes to real companies' job openings.   Their goal is to deceive the company's initial screening and get an interview opportunity.

Once they successfully pass the resume screening and receive an interview notification,  the scammers will use "deepfake" technology to precisely superimpose the job seeker's face onto their own,  so that the virtual interviewee's actions and lip movements perfectly match the speaker's audio.  Even coughing,  sneezing, or other subtle auditory actions can be precisely aligned with the visual content,  creating the appearance of a seemingly real interviewee that passes the company's remote interview.

Through such a well-designed fraud process, scammers successfully enter the company as job seekers.


# Commercial Espionage: The Harm to Enterprises


The FBI did not specify the ultimate goal of the scammers in the announcement. However, the agency pointed out that these fake job seekers, once successfully interviewed and hired, will be allowed to access sensitive data such as "company personal identifiable information, financial data, company IT databases and/or proprietary information".

![2024031808.png](https://cdn.aisecurius.com/images/3de/3de1aed9-6bd5-4332-9fe6-3f56033fbcea.png)


## Stealing confidential company information. 
Once scammers are successfully employed, they may gain access to key confidential business information such as new products, confidential technologies, password accounts, and database information, and they can sell or leak this information at any time, causing huge financial losses to the company. For example, in the financial industry, data is usually heavily protected and can be difficult to access through traditional scams, but security or IT positions can access it directly.


## Increase the company's recruitment costs.
 When a company discovers that it has hired a fake job seeker, it not only needs to immediately dismiss the current imposter, but also needs to quickly start a new recruitment process to find a suitable replacement. This not only leads to a loss of salary, but also increases the time cost of recruitment and employment. Moreover, the re-recruitment process itself can be very expensive, and the longer it takes to find new candidates, the greater the loss to the company.


## Conduct internal sabotage on the company. 
Using traditional methods to install ransomware or shut down systems can be time-consuming and labor-intensive for scammers. However, once they successfully enter the target company by impersonating a job seeker, they can exploit security vulnerabilities in the company's system to install malware on the intranet and office system at any time.


## Impersonating others for background checks. 
The FBI pointed out that some victims reported that their personal information was stolen and used for pre-employment background checks on other job seekers. This suggests that scammers may use the stolen personal information to impersonate the victim to accept the investigation of the employer or background check agency. They will attempt to provide false or tampered information to deceive investigators in order to obtain the opportunity to pass the background check. Companies may make inappropriate hiring decisions based on false background check results, and other honest job seekers may lose the opportunity to compete fairly.


## Affect the company's brand reputation. 
The fraudulent behavior of impersonating others to enter the company can also cause serious damage to the company's reputation and trust. Customers and partners may have doubts about the company's security management capabilities, which may affect business cooperation and market competitiveness.


# How HR Can Identify Fake Job Seekers

The Dingxiang Defense Cloud Business Security Intelligence Center released a special intelligence report "Research on  "DEEPFAKE" Threats and Security Strategies" (free download),  which systematically introduces the composition of "DEEPFAKE" threats, the harm of "DEEPFAKE", Which systematically introduces the composition of "deepfake" threats, the harm of "deepfake ", the process of "AI face-changing" fraud, typical threat patterns, the chain behind it, the current mainstream identification and detection strategies,  regulatory regulations of various countries on "DEEPFAKE",  and the responsibilities that each party needs to bear in "DEEPFAKE" fraud cases.

In response to the threat of being impersonated in remote interviews,  [the Dingxiang Defense Cloud Business Security Intelligence Center recommends that companies can adopt multiple  technologies and methods.](https://www.aisecurius.com/blog/article/55)


## 1. Identification of "DEEPFAKE" fraudulent videos


(1) During video interviews,  the other party can be asked to press their nose and face to observe the changes in their face. If it is a real person's  nose,  it will deform when pressed. The other party can also be asked to eat food and drink water to observe the changes in  their face. Alternatively, ask them to do some strange actions or expressions, such as waving,  making a difficult gesture, etc., to distinguish the truth from the false. During the waving process,  the data of the face will be interfered, which will produce certain jitter, flicker, or some abnormal situations.

(2) "DEEPFAKE" can copy voices, but it may also contain unnatural tones, rhythms,  or subtle distortions that will be particularly noticeable after careful listening. At the same time,  voice analysis software can help identify voice abnormalities.

(3) The generative adversarial network (GAN) based on deep learning can train a neural network model called  "discriminator". Through training,  the "discriminator" can more accurately identify fake images. The goal is to distinguish real from fake image videos and  identify any differences between the real version and the created version.

## 
2. Prevention of "DEEPFAKE" utilization


(1) By comparing and identifying device information, geographic location, and behavioral operations,  abnormal operations can be detected and prevented. Dingxiang device fingerprint can identify legitimate users and  potential fraudulent behaviors by recording and comparing device fingerprints. Its technology uniquely identifies and  identifies each device, identifies virtual machines, proxy servers, emulators and other maliciously controlled devices,  analyzes whether the device has abnormal behaviors such as multiple account logins, frequent IP address changes,  and frequent device attribute changes, and helps Track and identify fraudulent activity.

(2) Frequent verification is required for account logins in different places, device changes,  mobile phone number changes, dormant account activation, etc. In addition,  continuous identity verification during the session is crucial,  and persistent checks should be maintained to ensure that the user's identity remains consistent during use. Dingxiang's  frictionless verification can quickly and accurately distinguish whether the operator is a human or a machine,  accurately identify fraudulent behavior, and monitor and intercept abnormal behavior in real time.
![2024031903.png](https://cdn.aisecurius.com/images/e06/e06b5696-4cde-4099-bdfe-b33389a1def8.png)

(3) Face anti-fraud system based on AI technology and artificial review to prevent fake videos and pictures of  "DEEPFAKE". Dingxiang's full-link panoramic face security threat perception solution conducts real-time risk monitoring  of face recognition scenarios and key operations (such as camera hijacking, device counterfeiting, screen sharing,  etc.), and then performs face environment monitoring, liveness detection, image authentication,  and intelligent verification. After verifying multiple dimensions of information,  if forged videos or abnormal face information are found,  abnormal or fraudulent operations can be automatically interrupted.

On-site interviews are an effective way to prevent fraud by impersonating others. Arrange on-site interviews as much as  possible to communicate with job seekers face-to-face and better observe their words and deeds and their real situation.  However, if on-site interviews are not possible,  we can ask in-depth and specific questions during the interview. These questions may be difficult for scammers to  answer, or may take a long time to think about and respond. In this way,  companies can better identify the true abilities and background of job seekers.


Commercial Espionage: Using "Deepfake" Technology to Infiltrate Your Company

The digital wave is surging,  and traditional business models are facing unprecedented challenges and opportunities. In this digital age,

The digital wave is surging,  and traditional business models are facing unprecedented challenges and opportunities. In this digital age,  the key for businesses and merchants to expand sales channels and seize market share is to accurately locate customer  groups and adopt appropriate advertising channels and marketing methods. This way,  they can achieve twice the result with half the effort, winning more new customers at a lower cost.

The "Smart Life" platform, with the help of the "production + supply + sales" integrated digital consumption system,  helps merchants expand sales channels and promote efficient and convenient transactions. Through various digital tools,  the platform helps merchants achieve data precipitation and repurchase conversion,  and further flows back to online and offline, realizing multi-channel sales conversion and scene re-reach,  forming a virtuous cycle of closed-loop ecosystem with strong "blood-making" ability.

The "Smart Life" platform provides a variety of services, including food, desserts, drinks, daily necessities,  personal care, health care products and medicines. Through the diversified physical and online business formats,  the platform has established a wider intersection with consumers,  promoting the rapid expansion of the product side and channels to meet the needs of consumers in more regions.

The "Smart Life" platform is built on H5,  which can achieve good cross-platform adaptation. It can be seamlessly embedded in App, mini program,  or it can be opened directly on PC end as a page with independent link address. However,  while H5 brings convenience to users, it also brings many business risks to enterprises, such as information leakage,  account cracking, collecting wool, etc. According to the data of Dingxiang Defense Cloud Business Security Intelligence Center, in the attack of malicious crawler stealing data, H5 pages account for 46% of the total attack volume,  mini program accounts for 29%, Web end accounts for 18%, App end accounts for 5%, and other platforms account for 2%.

In order to prevent network crawler risks and ensure user account security,  the "Smart Life" platform is equipped with Dingxiang atbCAPTCHA code,  which is used for the registration and login verification of various services,  so as to ensure the security of the pl![2024032101.jpg](https://cdn.aisecurius.com/images/b63/b63730ca-f154-4c68-9339-fc3b3e3bebfa.jpg)atform.

The verification code can effectively prevent automated programs from maliciously registering, batch login attempts,  and malicious attacks during registration,  login and password retrieval. It ensures that the users who try to perform these operations are real human users,  not malicious programs or hackers. At the same time, by requiring users to enter the verification code,  it increases the difficulty of the account being cracked or stolen. Remind users to pay attention to account security  and enhance users' trust in the platform.

atbCAPTCHA, as one of Dingxiang Defense Cloud modules,  is an effective way to distinguish human users from automated robots. By requiring users to enter the verification code  when registering or logging in,  it can ensure that the account and password entered by the user are not obtained by malicious means,  thus effectively preventing unauthorized access and attacks. atbCAPTCHA can not only prevent malicious users or  advertisers from entering a large amount of spam information or abusing the registration and login window through  automated means, effectively blocking malicious crawlers from misappropriating and stealing data,  but also in the registration, login, query and other key links, real-time verification,  judgment and interception of malicious accounts and malicious crawling behaviors.

The "Smart Life" platform is launched by Shushang Cloud,  which mainly provides enterprises with full-chain digital operation services such as supply chain, supplier,  procurement, channel distribution, B2B e-commerce system development, etc. Through technical data,  it helps enterprises achieve digital transformation and create digital value, and meet the challenges of the times. of the times.

atbCAPTCHA Secures the "Smart Life" Platform

they were arrested by the Miami-Dade Police Department on charges of making nude photos of their classmates using "DEEPFAKE" technology.

On December 6, 2023, two students from Pinecrest Cove Academy in Miami, Florida, USA, were suspended. A few days later, on December 22, they were arrested by the Miami-Dade Police Department on charges of making nude photos of their classmates using "DEEPFAKE" technology.
According to information released by the police, the two 13- and 14-year-old middle school boys were accused of using an unnamed "artificial intelligence application" to generate nude images of other students aged 12 to 13.
According to a law passed in Florida in 2022, they were charged with a third-degree felony for transmitting pornographic images of "DEEPFAKE" without the victim's consent.
Media reports suggest that this case may be the first criminal case in the United States involving AI-generated nude images.
[The Dingxiang Defense Cloud Business Security Intelligence Center released the "Research on "Deepfake" Threats and Security Strategies", ](https://www.aisecurius.com/blog/article/55)which specifically mentioned that by imposing legal consequences on individuals who create or distribute deepfakes, the spread of harmful content can be prevented and those responsible for their actions can be held accountable. Criminalizing deepfake fraud can have a deterrent effect and prevent the abuse of this technology for fraud or other malicious purposes. It is an effective way to mitigate the harmful effects of deepfake technology and those who intentionally create or facilitate the spread of harmful deepfakes must be subject to criminal penalties.

# EU's AI Act: Strengthening Regulation on "DEEPFAKE"


The governance of artificial intelligence (AI) is crucial to the future of humanity, and it is a common challenge faced by countries worldwide. Many countries and organizations have introduced initiatives or regulations, unanimously calling for strengthened security oversight of AI. Therefore, it is necessary for countries to implement strong legal measures to address this urgent challenge.
![2024031901.png](https://cdn.aisecurius.com/images/69d/69d38348-a893-4e01-b046-8b89ab710862.png)
On March 13 local time, the European Parliament officially voted and approved the Artificial Intelligence Act (AIA), aiming to strictly regulate the use of AI. It is expected to come into effect in early 2025 and be implemented in 2026, with some provisions taking effect earlier. The draft of the AI Act was first proposed by the European Commission in April 2021. The Act will regulate high-impact, general-purpose AI models, and high-risk AI systems, which must comply with specific transparency obligations and EU copyright law.
Regarding high-risk AI systems, the AI Act formulates a series of safety provisions and requirements, mainly including the following four aspects.

## Data governance:
 Ensure the legality, fairness, and security of data collection, processing, and use. Data subjects enjoy rights such as data access, correction, deletion, and restriction of processing.

## Risk assessment: 
Developers must conduct risk assessments to identify and mitigate potential risks posed by AI systems. The risk assessment must cover aspects such as the accuracy, reliability, security, fairness, and privacy protection of the AI system.

## Transparency: 
Provide information about the functionality, performance, and risks of AI systems to enable users to make informed choices. The information must be presented to users in a clear and understandable manner.

## Human-machine interaction: 
Be designed for human-machine interaction to ensure that users can understand and control the behavior of the AI system. Users must be able to terminate the operation of the AI system at any time.

![2024031902.png](https://cdn.aisecurius.com/images/b4e/b4e26b43-ecf6-434a-b6e6-c30670fcc732.png)
The AI Act also requires that AI providers establish effective supervision and accountability mechanisms to ensure the safe and reliable operation of AI systems. Developers and users must be held responsible for any harm caused by AI systems. Penalties for violations of the regulations can reach up to 6% of the global turnover of the illegal act or 30 million euros, whichever is higher.


# Countries: Strengthening the Security Regulation of "DEEPFAKE"


"DEEPFAKE" involves a variety of technologies and algorithms,  which can work together to generate highly realistic images or videos. By piecing together the false content of  "DEEPFAKE" with the elements of real information, it can be used to forge identities, spread misinformation,  create false digital content, and engage in various frauds. This is a threat in the digital age,  where attackers are unseen and elusive. They not only create information but also manipulate the reality structure  perceived by each participant. Therefore,  using laws to regulate the abuse of "DEEPFAKE" is a unified action of all countries.
As early as November 2022, the Cyberspace Administration of China, the Ministry of Industry and Information Technology,  and the Ministry of Public Security jointly issued the "Regulations on the Management of Deep Synthesis of Internet  Information Services,"  which formulated a series of regulations and requirements for deep synthesis fraud. For deep-synthesized information  such as intelligent dialogue, synthetic voices, facial generation, facial replacement, facial manipulation,  virtual characters, and virtual scenes,  it is required to be significantly labeled to avoid public confusion or misunderstanding. At the same time,  the content of deep-synthesized information should be true and accurate,  and deep-synthesized information should not be used to create fake news, distort the truth,  mislead the public's perception, or damage the legitimate rights and interests of others.
On March 1, 2024,  the National Cybersecurity Standardization Technical Committee released the "Basic Requirements for the Security of  Generated AI Services,"  which clarified the basic requirements for the security of generated AI services, including language material security,  model security, and security measures. In terms of language material content security,  service providers need to focus on three aspects:  language material content filtering, intellectual property rights,  and personal information. In terms of personal information,  it emphasizes that before using language materials containing sensitive personal information,  the corresponding individual's separate consent or compliance with other circumstances stipulated by laws and  administrative regulations should be obtained.
On February 8, 2024,  the US Federal Communications Commission announced that AI-generated voices in robocalls were illegal,  and legislators in various states had already introduced legislation to combat AI- generating false and incorrect information.


# Technology: Preventing "DEEPFAKE" fraud

To combat "DEEPFAKE" fraud, in addition to legal regulations, technical identification and defense are also required:  [on the one hand, it is necessary to identify and detect forged videos, pictures,  and information (effectively identifying out false and forged content); on the other hand,  it is necessary to identify and detect the channels and platforms used for "DEEPFAKE" fraud (improve the security of  digital accounts in multiple ways).](https://www.aisecurius.com/blog/article/55)

## Enhance facial information security

The "EU AI Act" requires providers of AI tools to design these tools in such a way as to allow the detection of  synthetic/false content, add digital watermarks,  and promptly restrict the use of digital IDs for verifying personal identities. The working principle of AI watermarking  is to embed a unique signal into the output of the artificial intelligence model, which can be an image or text,  aiming to identify the content as AI-generated content to help others identify it effectively.
In addition, digital signatures and blockchain ledgers have uniqueness,  which can be used to track the source of behavior and mark it for review. Their immutability means that they can be used  to detect any tampering with the original file by using hash functions to ensure the authenticity of digital content.  Key functions such as timestamps and traceability can be used to determine the source and time of content creation. Of  course, these data are operated in a secure environment called Trusted Executive Environment (TEE).

## Identify forged false information

Based on deep learning,  generative adversarial networks (GANs) can train a neural network model called "discriminator". Through training,  the "discriminator" can more accurately identify the target of false images to distinguish between real and false images  and videos, and identify any differences between the real version and the created version.
In addition,  big data models can quickly analyze large amounts of audio data to identify "DEEPFAKE" audio content. For example,  Prisa Media, a Spanish media giant, has launched a tool called VerificAudio,  which can identify real and forged voices in Spanish for the detection of "DEEPFAKE" audio. The tool is currently  available to newsrooms in Mexico, Chile, Colombia, and Spain,  and is working to roll it out to journalists around the world.

## Ensure the security of face application

Based on AI technology and artificial review,  face anti-fraud systems can prevent the use of false videos and false images from "DEEPFAKE". Dingxiang's full-link  panoramic face security threat perception solution can effectively detect and identify false videos and false images  from "DEEPFAKE". It can monitor real-time risks in face recognition scenarios and key operations (such as camera  hijacking, device forgery, screen sharing, etc.),  and then verify and identify through multi-dimensional information such as face environment monitoring, live detection,  image authentication, and intelligent verification,  and automatically block abnormal or fraudulent operations after discovering forged videos or abnormal face information.
![2024031903.png](https://cdn.aisecurius.com/images/e06/e06b5696-4cde-4099-bdfe-b33389a1def8.png)
By comparing and identifying device information, geographical location, and behavior operations,  it is possible to detect and prevent abnormal operations from "DEEPFAKE" fraud. Dingxiang's device fingerprint  technology can record and compare device fingerprints to identify virtual machines, proxy servers, simulators, and other devices that are maliciously manipulated,  and analyze whether there are abnormal or inconsistent behaviors that do not conform to user habits,  such as multi-account logins, frequent IP address changes, frequent device attribute changes, etc.,  to help track and identify the activities of fraudsters.

## Ensure the security of content distribution channels

Social media platforms usually position themselves as merely channels for content,  and the Australian Competition and Consumer Commission (ACCC) is suing Facebook. The ACCC also believes that Facebook  should be held responsible as an accessory to fraud - because it failed to remove misleading advertisements in a timely  manner after receiving a notice of a problem. At least,  platforms should be responsible for promptly deleting deep-fake content for fraudulent purposes. Many platforms have  claimed to be doing so, for example, on Facebook,  any AI-generated content may display an icon clearly indicating that the content was generated by artificial  intelligence.
Social media companies have the greatest influence in restricting the spread of false content and can detect and remove  it from their platforms. However, the policies of major platforms, including Facebook, YouTube, and TikTok,  stipulate that they will only delete fraudulent content if it causes "serious harm" to people.

13-Year-Old Middle School Student Arrested on Charges of Creating Fake Pornographic Photos of Classmates Using AI Technology

Dingxiang Defense Cloud Business Security Intelligence Center has recently released an intelligence monograph entitled  "Research on "Deepfake" Threats and Security Strategies", 

Dingxiang Defense Cloud Business Security Intelligence Center has recently released an intelligence monograph entitled  ["Research on "Deepfake" Threats and Security Strategies", ](https://cdn.aisecurius.com/files/764/%E3%80%8AResearch%20on%20Deepfake%20Threats%20and%20Security%20Strategies%E3%80%8B%E8%8B%B1%E6%96%870313.pdf) 

![2024031204.jpg](https://cdn.aisecurius.com/images/3d5/3d57849d-f5df-4638-9af1-548f1434b1f7.jpg)

![2024031205.jpg](https://cdn.aisecurius.com/images/a7d/a7df2f97-bcaf-4a48-aba2-9a4e9a4342fb.jpg)

which conducts an in-depth study of the currently highly concerned "Deepfake" risks. The monograph consists of 8 chapters and 32 sections, systematically introducing the composition of "Deepfake" threats, the harms of "Deepfake",  the process of "Deepfake" fraud, typical "Deepfake" threat patterns, the industry chain behind "Deepfake",  the current mainstream "Deepfake" identification and detection strategies,  regulatory regulations of various countries on "Deepfake",  and the responsibilities that each party needs to bear in "Deepfake" fraud cases.

[Click to Read the Full Intelligence Report of "Research on "Deepfake" Threats and Security Strategies"](https://cdn.aisecurius.com/files/764/%E3%80%8AResearch%20on%20Deepfake%20Threats%20and%20Security%20Strategies%E3%80%8B%E8%8B%B1%E6%96%870313.pdf)。

# Fraud: 6 Common Types of "Deepfake" Fraud


##  1、Fake accounts to spread rumors


 With "Deepfake" technology, fraudsters are able to create and distribute highly customized and convincing content that can target individuals based on their online behavior, preferences, and social networks and can be seamlessly integrated into user feeds, facilitating rapid and widespread dissemination. This makes cybercrime more efficient and challenging for users and platforms.

Identifying fake accounts for "Deepfake" is a challenging task because they often involve combinations of real elements (such as real addresses) and fabricated information. This makes detection and prevention extremely difficult. The detection effort is further complicated by using legitimate components and false details. Moreover, because these fraud sexual identities lack a previous credit record or associated suspicious activity, it is difficult to identify them through a conventional fraud detection system.

![2024030802.png](https://cdn.aisecurius.com/images/c46/c46e4bd4-35a7-4774-a59e-d10fbfa069ef.png)

## 2、posing as acquaintances to commit fraud


 In January 2024, employees of Hong Kong multinational companies were cheated of HK $200 million, and the fraud cases reported by police in Baotou, Inner Mongolia in November 2023 were both fake acquaintances.

The "Deepfake" technology allows fraudsters to easily imitate the video and voice of the target person. These fake video and audio can imitate not only sound, but also intonation, accent and speaking style. In order to further increase the credibility of the fraud, the fraudsters will also obtain the sensitive information of the victims (such as work style, living habits, travel trends, etc., etc.) on social media and public channels, to prove the authenticity of their imposter identity, making it difficult for the victims to distinguish the truth from the false. After obtaining the trust of the victim, and then to the victim of financial funds, trade secrets and other fraud.


## 3、Fake your identity to apply for bank loans



 According to the McKinsey Institute, synthetic identity fraud has become the fastest growing type of financial crime in the United States and is on the rise globally. Indeed, synthetic identity fraud, accounts for 85% of all current fraud behaviors. Moreover, the UK GDG study shows that more than 8.6 million people in the UK use false or other identities to obtain goods, services or credit.

 For financial institutions, the "Deepfake" fraud is even more worrying.scammers use "Deepfake" technology to fake information, sounds, videos, pictures, and then combine real and false identity information to create new false identities to open bank accounts or make fraud purchases. What's more, fraudsters can use the "Deepfake" technology to learn different banking businesses and processes, and then quickly launch a fraud on different banks at the same time.

In the future, the bank for the user's credit application, perhaps not only to evaluate " he / she is suitable for credit 100,000? Or $200,000?", also need to tell" this loan applicant is human? Or Artificial intelligence?", 92% of banks are expected to face a" Deepfake " fraud threat.


## 4、Make "phishing" even more difficult to identify


 Phishing has long been a prominent topic in security, and despite such forms of fraud for decades, it is still one of the most effective ways for fraud to attack or penetrate organizations. Fraudsters based on social engineering principle, by email, website, and phone calls, SMS and social media, using human nature (such as impulse, discontent, curiosity), as trusted entities, induce the victims click on false links, download malicious software, induced transfer funds, provide sensitive data such as account password behavior.

With the development of technology, Internet telephers are also changing their strategies, especially with the help of AI, where fraudsters use "Deepfake" technology to deceive victims, making phishing attacks more complex and extremely difficult to detect. In 2023, the deep forgery phishing fraud incidents surged by 3,000%.


## 5、pretend to be others remote interview entry


In July 2022, the FEDERAL Bureau of Investigation (FBI) warned that a growing number of fraudsters are using "Deepfake" technology to pose as job seekers in remote job interviews to defraud salaries and steal business secrets.


 The FBI did not specify the ultimate goal. But the agency noted that the fake interviewers, if successful and hired, will be allowed to successfully access sensitive data such as "customer PII (personal identity information), financial data, corporate IT database and / or proprietary information."

![2024031001.jpg](https://cdn.aisecurius.com/images/2e3/2e32efb4-ea58-4ad5-803d-854724efa040.jpg)

The FBI also said stolen personal information from some of the victims of the scam had been used to conduct remote interviews and had been used to conduct pre-employment background checks with other applicants' profiles.


## 6、fake login to steal the bank balance


On February 15,2024, the foreign security company Group-IB announced that it had found a malware named "GoldPickaxe". The iOS version of the malware lured users into face recognition, submitting identity documents, and then making deep forgery based on the user's face information.

Through the deeply forged fake face video, fraud molecules can log in the user's bank account, intercept the mobile phone verification message sent by the bank, and can transfer money, consume, change the account password, etc.


# Process: The "Deepfake" Deception Process Consists of Four Main Steps


 With Dingxiang defense cloud business security intelligence center to intercept a "Deepfake" financial fraud cases, for example, fraud molecules fraud process mainly has four stages, "Deepfake" technology is just a key factor in the process of fraud link, other links, the victim if unable to identify and judgment, it is easy to fraud molecular instructions step by step into the trap.

![2024030805.jpg](https://cdn.aisecurius.com/images/5fd/5fd2b3ee-9bd5-49db-8a42-9813f8d2767e.jpg)


##  The first stage, to cheat the victim's trust. 
Fraudsters contact the victim through text messages, social tools, social media, phone calls, etc., etc. (for example, can directly tell the name of the victim's name, home, phone, unit, address, ID number, colleagues or partners, or even some experiences) and gain trust.

 
## The second stage, steal the victim's face. 
Fraudsters make video calls with victims through social networking tools, video conferences, video calls, and other methods. During the video call, they obtain the victim's face information (face, bow, turning, mouth, blink and other key information) to make "Deepfake" fake videos and portrait production. During this period, victims will also be induced to set up phone call transfer or induce victims to download malicious App software, which can call transfer or intercept the bank's customer service phone or phone.


##  The third stage, log in to the victim's bank account. 
The fraudsters log in the victim's bank account through the bank App, submit the fake videos and fake portraits made by "Deepfake", pass the bank's face recognition authentication, and intercept the mobile phone SMS verification code and risk tips sent by the bank on the victim's mobile phone.


## The fourth stage, transfer the victim's bank balance.
 The bank calls the manual access phone call to the virtual number set by the fraudsters, posing as the victim, through the manual verification of the bank customer service staff, and finally successfully transferred the balance of the victim's bank card.


"Deepfake" danger is not only to generate false video and images, but also contributed to the whole fraud ecosystem: a robot, false accounts and anonymous service of intricate network operation, all of these are designed to make, enlarge and distribute fabricated information and content, with difficult identification, detection and traceability problems.

 Identification is difficult. Has developed to the point of convincingly generating realistic personal simulations, making it increasingly difficult to distinguish between true and false content to identify unless specifically trained, realizing that this threat is the first step in defending against it.

 Detection is difficult. To improve the quality of "Deepfake", detection is a big problem. Not only the naked eye can not effectively identify, some conventional detection tools can not be found in time.

 Traceability is difficult. There is no digital fingerprint, simulator forged address, false IP address, false device information, no clear digital clues to follow, or even no direct malicious software signature that can be detected, and traditional network security measures cannot effectively protect them.

It is a form of threat in the digital age, in which attackers are invisible and elusive, who not only produce information but also manipulate the realistic structure perceived by each participant. Therefore, to combat "Deepfake" fraud, on the one hand, it needs to identify and detect fake videos, pictures and information (effectively identify fake content); on the other hand, it is necessary to identify and detect "Deepfake" mode channels and platform networks (to improve the security of digital accounts in many aspects). This requires not only technical countermeasures, but also complex psychological warfare and the promotion of public safety awareness.
sychological warfare and the promotion of public safety awareness.

[Click to Read the Full Intelligence Report of "Research on "Deepfake" Threats and Security Strategies"](https://cdn.aisecurius.com/files/764/%E3%80%8AResearch%20on%20Deepfake%20Threats%20and%20Security%20Strategies%E3%80%8B%E8%8B%B1%E6%96%870313.pdf)。

First! Dingxiang Releases "Research on "Deepfake" Threats and Security Strategies"

Furthermore, foreign darknet marketplaces offer dedicated channels and groups for deepfakes, ranging from self-creation tools to personalized services. 

*This excerpt is from DingXiang Defense Cloud Business Security Intelligence Center's white paper on "deepfakes," which are highly realistic synthetic videos or audios used for malicious purposes. If you're interested in learning more, you can contact marketing@dingxiang-inc.com to receive a free electronic copy upon completion.*


Deepfakes pose a significant threat, enabling criminals to steal identities, spread misinformation, and create fake content for malicious purposes. Real-world examples include:

January 2024:[ Employees of a multinational company in Hong Kong lost HK$200 million due to phishing fraud involving deepfakes](https://www.aisecurius.com/blog/article/50).
December 2023: [An overseas student's parents were tricked into paying a 5 million yuan ransom after receiving a deepfake video depicting their child's "kidnapping."](https://www.aisecurius.com/blog/article/47)


A KPMG report reveals a staggering 900% year-on-year increase in the number of deepfakes available online. Notably, bandeepfakes reports that nearly all deepfakes (98%) are pornographic and target women.


# Investigating the Deepfake Industry Chain


According to a new report by iProov, a biometrics company, fraudsters commonly use tools like SwapFace, DeepFaceLive, and Swapstream to create deepfakes. These tools often work in conjunction with:

**Simulators:** Changing location data for virtual "presence" anywhere.

**Device modification software**: Forging device attributes to bypass security systems.

**IP changers**: Rapidly switching IP addresses across regions.

By combining these tools, criminals can distribute deepfakes through various channels, including video conferencing platforms, work networks, and social media, ultimately enabling further fraudulent activities.


The emergence of "Cybercrime as a Service" (CaaS) has unfortunately made acquiring deepfake services and technologies easier than ever. This raises concerns as platforms like GitHub, a software development platform, host over 3,000 repositories related to deepfakes, highlighting their widespread development and potential for misuse.

Furthermore, foreign darknet marketplaces offer dedicated channels and groups for deepfakes, ranging from self-creation tools to personalized services. The pricing structure, with options ranging from $2 for basic deepfakes to $100 for more complex ones, further facilitates deepfake-related crimes due to their affordability and ease of use.
![2024030502.png](https://cdn.aisecurius.com/images/1a6/1a645258-f162-46ea-9a39-517e171eda3c.png)

By improving the language clarity, conciseness, and flow of the original text, this revised version aims to be more appealing and informative for American audiences.


# Process: Steps involved in creating a "deepfake" video


"Deepfakes" involve AI algorithms and deep learning. Overall, the process of creating a "deepfake" video involves the following steps:

**1. Data Collection**

Collect a large amount of data on the target subject, including multi-angle facial photos, work information, life information, etc., of which a large amount of images, information, and videos are collected from public social media.

**2. Feature Extraction
**
Using deep learning algorithms, accurately identify and extract key facial features such as eyes, nose, and mouth.
![2024030501.png](https://cdn.aisecurius.com/images/418/418958be-c87d-40ba-bc6e-c42b42f8e4d1.png)
**3. Image Synthesis**

Overlay and blend the target's face onto the face of the subject in the video to be faked, aligning facial features to ensure they match and replace between the source and target.

**4. Voice Processing**

Use machine learning and artificial intelligence to replicate a person's voice with startling accuracy, such as pitch, tone, and speaking style, and match the lip movements in the video with the synthesized speech.

**5. Environment Rendering**

Use lighting and color tools to further refine the coordination and matching of the characters, voice, movement and environment, costumes in the video.

**6. Video Synthesis and Export**

The video can then be used for online playback, live streaming, video conferencing, and other fraudulent activities.


# Difficulties: Challenges in Identifying and Detecting "Deepfakes"


**Difficult to Identify**

"Deepfakes" have evolved to the point where they can generate convincingly realistic personal simulations, making it increasingly difficult to distinguish between real and fake content. It is difficult for people to identify them without specialized training, and recognizing this threat is the first step in defending against it.

**
Difficult to Detect**

The increasing quality of "deepfakes" makes detection a major challenge. It is not only difficult for the naked eye to identify effectively, but some conventional detection tools cannot detect them in time.

**Difficult to Track**

Traditional cybersecurity measures cannot effectively protect against "deepfakes" when there is no digital fingerprint,  There are no clear digital clues to follow, no IP address to blacklist, and even no direct malware signature to detect.
![2024021801.png](https://cdn.aisecurius.com/images/66a/66a1d8dd-4927-4743-8970-4348957ed771.png)
**The "Deepfake" Fraud Ecosystem**

What's even more frightening is that the danger of "deepfakes" lies not only in the technology, but also in the entire fraud ecosystem it facilitates.** The "deepfake" fraud ecosystem operates through a complex network of bots, fake accounts, and anonymous services, all designed to create, amplify, and distribute fabricated information and content. **This is a form of guerrilla warfare in the digital age, in which attackers are invisible and elusive. They not only create information, but also manipulate the reality structure perceived by each participant. **Therefore, combating "deepfake" fraud requires not only technical countermeasures, but also complex psychological warfare and public safety awareness.**


# Security: Four Defense Measures Against "Deepfake" Fraud

As technology advances,  methods for detecting and identifying "deepfake" scams are also evolving. Businesses and individuals need to verify  identities through multiple channels and adopt multiple strategies to identify and defend against "deepfake" fraud.
## 1. Behavioral and Biometric Recognition
ion

(1) [During video calls,  you can ask the other person to press their nose or face to observe the changes in their face.](https://www.aisecurius.com/blog/article/52) If it is a real person's  nose,  it will deform when pressed. You can also ask the other person to eat food or drink water to observe the changes in  their face. Alternatively, you can ask them to do some strange actions or expressions,  such as waving or making a difficult gesture, to distinguish between real and fake. In the process of waving,  the data of the face will be interfered, and there will be some shaking or flickering,  or some abnormal situations. In one-on-one communication,  you can ask some questions that only the other person knows to verify their authenticity. At the same time,  when someone makes a remittance request in a video or recording,  it is necessary to call or repeatedly verify from other channels.

(2) In point-to-point communication,[  you can ask some questions that only the other person knows to verify their authenticity.](https://www.aisecurius.com/blog/article/47)

(3) "Deepfakes" can replicate voices, but they may also contain unnatural tones, rhythms,  or subtle distortions that will be particularly noticeable after careful listening. At the same time,  voice analysis software can help identify voice anomalies.

(4) In the case of files, an automatic document verification system can analyze the document for inconsistencies,  such as font changes or layout diff
## 2. Device and Account Recognition
 Recognition

(1) Digital signatures and blockchain ledgers are unique and can be used to track the source of behavior and mark it for  review.

(2) Comparing and identifying device information, geographic location,  and behavioral operations can identify and prevent abnormal operations. DingXiang Device Fingerprint can distinguish  between legitimate users and potential fraudulent behavior by recording and comparing device fingerprints. Its  technology uniquely identifies and identifies each device, identifies virtual machines, proxy servers,  simulators and other maliciously controlled devices, analyzes whether the device has multiple accounts logged in,  whether the IP address is frequently changed, whether the device attributes are frequently changed,  etc. Abnormal or unusual behavior, helping to track and identify the activities of fraudsters.

(3) [Account login from different places, changing devices, changing phone numbers, ](https://www.aisecurius.com/blog/article/52) dormant accounts suddenly becoming active, etc., need to be strengthened frequent verification;  in addition, continuous identity verification during the session is crucial,  maintaining persistent checks to ensure that the user's identity remains consistent during use. DingXiang's frictionless  verification can quickly and accurately distinguish whether the operator is a human or a machine,  accurately identify fraudulent behavior, and monitor and intercept abnormal behavior in real time. In addition,  based on the principle of least privilege,  restrict access to sensitive systems and accounts to ensure access to the resources required for their roles,  thereby reducing the potential impact of account theft.
![2024030504.jpg](https://cdn.aisecurius.com/images/818/8181db92-6201-4fcf-8627-f84e238c47a6.jpg)

(4) Face anti-fraud system that combines artificial review and AI technology to prevent fake videos of "deepfakes".  DingXiang's full-link panoramic face security threat perception scheme can effectively detect and discover fake videos. It conducts intelligent risk assessment and risk rating on user face images through multi-dimensional information such as face  environment monitoring information, liveness detection, image forgery, intelligent verification, etc.,  and quickly identifies fake authentication risks. DingXiang's full-link panoramic face security  threat awareness scheme, real-time risk monitoring for face recognition scenarios and key operations, targeted monitoring such as camera hijacking, device forgery, screen sharing and other behaviors, and trigger the active defense mechanism to deal with. After discovering forged videos or abnormal face information, the system supports automatic execution of defense policies, and the corresponding defense disposal after the device hits the defense policies can effectively block risky operations.


## 3. AI Recognition and Evidence Collection

(1) Generative Adversarial Networks (GANs) based on deep learning can train a neural network model called a  "discriminator" to identify any differences between the real and created versions. Big data models can quickly analyze  large amounts of video and audio data to identify anomalies faster than humans can. Additionally,  machine learning models can identify characteristic patterns of "deepfake" production algorithms,  thereby identifying "deepfake" content. Machine learning models can also be retrained and adjusted to keep up with  real-time iterative evolution.

(2) AI forensics tools play a critical role in investigating and attributing "deepfake" content. These tools analyze  digital footprints, metadata,  and other traces left behind during the creation process to help identify the source of the attack and assist in legal  investigations.


## 4. Social Prevention and Public Education

(1) [Reduce or eliminate the sharing of sensitive information such as accounts](https://www.aisecurius.com/blog/article/47), family members, travel,  and work positions on social media to prevent fraudsters from stealing and downloading them, and then "deep forge" pictures and sounds, Then they forged their identities.

(2) Continuously educating the public about "deepfake" technology and its associated risks is critical. Encouraging the  public to be vigilant and quickly report suspicious situations can also significantly improve organizations' ability to  detect and respond to "deepfake" threats.

Technology is constantly evolving,  and new frauds are constantly emerging. Stay up-to-date on the latest developments in AI and "deepfake" technologies  whenever possible to adjust your security measures accordingly. Continuous research, development,  and updating of AI models are critical to staying ahead of the increasingly sophisticated "deepfake" technology.

"White Paper: Deepfake Industry Chain Investigation and Four Defense Measures"

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