Anti-Fingerprinting

Anti-fingerprinting detection identifies browser privacy features, extensions, and tools designed to prevent or mitigate fingerprinting. This meta-fingerprinting technique detects the very defenses users employ to avoid fingerprinting, ironically making them more identifiable. The technique checks for indicators of privacy browsers (Tor Browser, Brave), anti-fingerprinting extensions (Canvas Defender, Privacy Badger, uBlock Origin), VPNs, and browser modifications. Detection methods include: checking for injected canvas noise (drawing a canvas and checking if pixel values fluctuate between renders), detecting blocked or spoofed APIs (checking if certain properties return null or generic values), measuring timing inconsistencies (privacy tools introduce delays or noise in performance.now()), detecting user agent spoofing (checking for inconsistencies between navigator.userAgent and actual API availability), and identifying generic or rounded fingerprint values. For example, Tor Browser returns 'America/New_York' timezone for all users regardless of actual location, makes all screens appear 1000x900, limits canvas to 8-bit color depth, and disables WebGL entirely - these uniform values paradoxically make Tor users highly identifiable as a group. Brave's fingerprinting randomization adds subtle noise to canvas and audio, but the presence of this noise pattern itself is detectable. Extensions that block navigator.getBattery() or navigator.mediaDevices create API unavailability that's suspicious in modern browsers. Headless browsers (used for automation) often have missing window.chrome object, webdriver flags set to true, or unusual console properties. The technique compiles a 'lies' score indicating detected inconsistencies or anti-fingerprinting measures, ironically providing additional entropy that makes privacy-conscious users more trackable within their privacy-tool cohort.

• •Privacy detection
• •Bot detection
• •Headless browsers