Noise injection is a technique that adds subtle random variations to fingerprint values, changing the resulting hash while maintaining functionality.
🔬 How It Works
For canvas fingerprinting:
// Original pixel data
[255, 128, 64, 255, ...]
// After noise injection (±1-2 values)
[254, 129, 64, 255, ...]
// Result: Different hash, visually identical📊 Where Noise Is Applied
- Canvas: Pixel color values
- Audio: Audio sample values
- WebGL: Rendering output
- ClientRects: Position values
✅ Advantages
- Changes fingerprint hash effectively
- Imperceptible to users
- Doesn't break functionality
- Easy to implement
❌ Limitations
- Can be detected through multiple samples
- Random noise patterns may be identifiable
- Doesn't provide consistent identity
💡 Best Practice: Quality antidetect browsers use consistent noise per profile (seeded randomness) rather than pure random noise.