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.

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.