How Does Liveness Detection Work? Active vs Passive

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Liveness detection, formally known as Presentation Attack Detection (PAD), verifies that a face presented to a camera belongs to a live, physically present person rather than a photograph, screen replay, mask, or prerecorded video, addressing a major weakness of face matching alone. Didit provides passive liveness, which analyzes a capture without requiring user action, and active liveness, which uses randomized prompts such as blinking or head movements to provide stronger protection against replay attacks. Its passive system is reported to have achieved iBeta Level 1 PAD certification with a 0% attack success rate across 360 attempts, although this level does not cover 3D masks or prosthetics. Liveness is positioned as one component of secure KYC alongside document verification and face matching, with uses in fintech onboarding, crypto compliance, age-gated services, and account re-authentication. Didit integrates liveness into hosted verification workflows, returns results in under two seconds, supports automated outcome routing, charges $0.10 for passive checks and $0.15 for active checks, and notes that additional defenses are needed for synthetic-video injection attacks that bypass the camera pipeline.

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