Liveness Detection
Techniques used to verify that an input (e.g., biometric) originates from a live subject rather than a spoof or replay, enhancing system security and integrity.
Anti-spoofing measures - challenge-response protocols, motion analysis, texture analysis, or multi-modal checks - that distinguish genuine biometric inputs (face, voice) from photos, videos, or recordings. Governance requires regular liveness-test updates to counter evolving spoofing attacks, integration into authentication pipelines, and logging of failed attempts for security monitoring and compliance reporting.
A banking app’s face-login adds a liveness check: it prompts users to blink or turn their head, verifying real-time motion before unlocking. All liveness-check failures are logged and reviewed weekly by security analysts to detect new spoofing tactics and refine detection algorithms.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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