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This webinar is the second part of a series focusing on how anti-fraud systems detect users. The speaker, Dimma Jan, provides updates on their anti-detect software, discusses a new canvas detection method implemented in Chromium browsers, and explains several techniques for detecting canvas and WebGL fingerprint substitutions. The webinar also touches upon audio fingerprinting and network protocols used for user detection.
New Canvas Detection Method: Chromium browsers (version 137 and higher) now use a "floating-point canvas" with increased uniqueness and multiple "bodies," making older canvas fingerprint detection techniques less effective. This requires updated anti-detect software.
WebGL and Audio Fingerprinting: The speaker details various methods for detecting WebGL and audio fingerprint substitutions, highlighting the limitations of current anti-detect software and the increasing sophistication of anti-fraud systems. Audio fingerprinting is largely dependent on the browser's audio engine version, not hardware settings.
Web Transport Protocol: The web transport protocol (supported by Chromium 135+) bypasses traditional proxying methods (TCP, UDP, QUIC), allowing websites to obtain users' real IP addresses. This poses a significant challenge for anti-detect software.
Collaboration with Geobox: The speaker announces a partnership with Geobox, offering a combined solution to address geolocation challenges faced by anti-detect users. This solution is expected to be released soon.
OpenAI's Impact: The speaker discusses OpenAI's upcoming ChatGPT release (version 6), which aims to revolutionize anti-fraud systems and potentially render existing anti-detect software obsolete.