How is PAD different from liveness?

Liveness confirms a live subject; PAD focuses on detecting spoofs/presentations crafted to fool sensors. They’re often implemented together.

What methods are most effective?

Multi-modal signals (texture, depth, motion), randomized prompts, and attack-aware AI models improve resilience.

How do we measure quality?

With FAR/FRR and attack presentation classification error rates; independent evaluations build trust.

Against novel, high-quality spoofs — continuous retraining and attack intel are vital.

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Presentation Attack Detection (PAD)

Overview

PAD detects attempts to spoof biometric systems using photos, videos, masks, molds, or synthetic signals. It complements liveness detection and is referenced by ISO/IEC 30107-3 testing protocols. Techniques include texture/reflectance analysis, challenge-response, depth sensing, and AI models trained on known attacks.

Strong PAD lowers false accepts (FAR) without harming genuine users (FRR) and is essential for remote onboarding and step-up authentication. Regulators increasingly expect PAD for biometric KYC to counter deepfakes and injection attacks. Vendors should publish evaluated performance and undergo third-party testing.

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