Real device fingerprints
Profiles pull complete fingerprints from an upstream catalogue of actual macOS / Windows machines, not noise generators. Properties stay internally consistent — what fingerprint checkers expect.
Fingerprint management for the Afina antidetect browser
Every profile gets the fingerprint of a real device — UA, platform, WebGL, Canvas, Audio, ClientRects, hardware concurrency, deviceMemory and more. Picked from an upstream catalogue of actual devices, not generated noise. Built into every antidetect profile.
Real device • Real fingerprint
Look like a real device, every time
Modern fingerprint checkers don't just read one property — they cross-check WebGL renderer against UA, ClientRects against screen size, AudioContext against CPU cores. Afina pulls the whole consistent set from a real device, then applies optional noise on top, so each profile reads as a unique but believable machine.
Per-OS, per-chip selection
Choose macOS M1/M2/M3/M4, Intel Mac, Windows 10 or Windows 11. WebGL renderer, AudioContext signature, language defaults and OS-specific quirks come pre-aligned with the chip. Available via API and the proxy-aware UI.
Canvas / WebGL / Audio noise
Optional anti-correlation noise on Canvas, WebGL, ClientRects and AudioContext — disable per profile or globally. Configurable inside RPA scripts via account settings.
Anti-detect against modern checkers
Tested against widely-used multi-accounting fingerprint checkers. New checker shows up? The catalogue updates with the next desktop release, no extra config on your side.
Look real to every fingerprint checker
Real device fingerprints, configurable noise and anti-detection updates ship in every release.
Fingerprint management in Afina gives every profile a complete fingerprint of an actual macOS or Windows machine — UA, platform, WebGL, Canvas, AudioContext, ClientRects, hardwareConcurrency, deviceMemory and dozens more. Picked from an upstream catalogue of real devices, not generated noise, so anti-fraud systems read each profile as unique but believable.
Why generated noise fails fingerprint checkers
Modern fingerprint checkers don't read one property — they cross-check WebGL renderer against UA, ClientRects against screen size, AudioContext samples against CPU cores. Random per-property noise creates inconsistencies the checker flags instantly. Pulling a coherent fingerprint from one real device avoids that whole class of detection.
What's inside the fingerprint engine
1. Real-device chip catalogue
Each profile picks a device chip — macOS M1/M2/M3, Windows Intel/AMD generation — and inherits the full consistent fingerprint of that machine. WebGL strings match the GPU, fonts match the OS, hardware concurrency matches the CPU.
2. Anti-detection at every layer
Canvas, Audio, ClientRects, WebRTC, Battery, Speech Synthesis voices, MediaDevices, screen.colorDepth, plugin enumeration — all controllable, all aligned. Headless / Puppeteer / Playwright giveaways (navigator.webdriver, missing chrome.runtime, etc.) are scrubbed at the Chromium build level.
3. Optional noise on top
Per-profile Canvas / WebGL / Audio noise so two profiles on the same device chip still differentiate to fingerprint trackers — without crossing into inconsistency territory. Tunable strength per profile.
4. Real Chromium, not a wrapped lib
Afina is a real Chromium build with the antidetect surface baked into the engine, not a JS-injection layer. TLS / JA3 handshake matches mainstream Chrome byte-for-byte — corporate WAFs and CDN bot-walls don't flag.
5. Verified against public checkers
Default Afina profiles pass Pixelscan, CreepJS, BrowserLeaks, AmIUnique, IPHey, FingerprintJS, Bot.sannysoft, Browserscan, Whoer and EFF Cover Your Tracks. Numbers reproducible — install, create a profile, run the checker, see "consistent / anonymous". The fingerprint-check page tracks the pass rate on each.
Where fingerprint quality matters
Ad platforms, e-commerce marketplaces, ticketing, dating and crypto exchanges all run anti-fraud stacks that lean on fingerprints. Multi-accounting only works when every profile reads as a different real user. Web scraping and SERP collection survive bot-walls only when JA3, HTTP/2 SETTINGS and ClientHints all align. Afina is the substrate for all of those.