Your IP: Unknown · Your Status: ProtectedUnprotectedUnknown
Biometric spoofing

Biometric spoofing

(also biometric spoof attack, presentation attack)

Biometric spoofing definition

Biometric spoofing is the deliberate manipulation or imitation of biometric traits to deceive systems reliant on biometric authentication for security. By using fake or altered biometric samples, attackers can pass off as someone else and trick authentication measures into giving them access to protected systems.

See also: cyberattack, biometric authentication, biometric data, biometric device, biometric security, two-factor authentication

Examples of biometric spoofing

  • Fingerprint spoofing: Using fake fingerprints to mimic a legitimate user’s fingerprint characteristics. The attackers may copy the victim’s fingerprints from a surface they touched (such as a door handle), then generate artificial fingerprints with materials like gelatin or silicone.
  • Face spoofing: Using printed photos, masks, or 3D models to impersonate a legitimate user’s face. Advanced face spoofing techniques can create realistic facial mimics using video or deepfake technology.
  • Iris spoofing: Making artificial iris patterns or manipulating iris images to deceive iris recognition systems. Digital image manipulation tools can create synthetic images or alter real iris pictures, with the result then being printed using a high-resolution printer.
  • Voice spoofing: Imitating a legitimate user’s voice to get past voice-recognition systems. This may involve pre-recorded samples, voice synthesis, or voice conversion.

Preventing biometric spoofing

  • Include additional multi-factor authentication methods to deny attackers access to your device or network even when they have spoofed biometric credentials.
  • Use multiple biometric traits for identification (a process called “biometric fusion”) to make it harder for attackers to gain access with only one set of spoofed credentials.
  • Implement liveness detection to differentiate between live and spoofed samples (for example, by analyzing blood flow, capturing eye movement, or assessing voice characteristics).

Further reading

Ultimate digital security

We value your privacy

This website uses cookies to provide you with a safer and more personalized experience. By accepting, you agree to the use of cookies for ads and analytics, in line with our Cookie Policy.