Browser fingerprinting is the process of collecting a wide range of browser and device attributes to create a unique identifier for a visitor — without using cookies. Anti-bot systems use fingerprinting to detect headless browsers and automation frameworks, which produce distinctive attribute patterns that differ from genuine user browsers.
The attributes collected include: User-Agent string, screen dimensions, installed fonts, Canvas rendering output, WebGL parameters, AudioContext fingerprint, navigator properties (language, platform, plugins, vendor), touch support, hardware concurrency, device memory, and dozens of timing measurements. Headless Chrome, for example, reports `navigator.webdriver = true`, has no plugins array, returns empty fonts lists, and produces Canvas output that differs from a real GPU-rendered result.
Stealth patches counter fingerprinting by overriding these properties to match real browser values. AlterLab's rendering layer applies comprehensive fingerprint normalisation — patching `navigator.webdriver`, aligning Plugin arrays, randomising Canvas noise, and injecting realistic timing — to pass fingerprint checks on all major anti-bot platforms.