Active Liveness Detection: Inside the Mechanics of Deepfake Prevention

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Active liveness detection is a biometric security approach designed to verify that an online user is physically present rather than using a photo, video, mask, or deepfake. It combines three-dimensional facial mapping through structured light or time-of-flight sensors with analysis of skin texture, reflections, material properties, and dynamic physiological cues such as blinking, expressions, movement, blood flow, and pulse. Users may be asked to complete randomized actions, making prerecorded or scripted presentation attacks more difficult because the system can assess real-time facial geometry, natural movement, and response patterns. Compared with passive liveness checks that assess a static image or brief video, active methods provide added protection against sophisticated spoofing, though they require more user interaction. Didit presents its iBeta Level 1-certified active liveness product as a multimodal system using 3D, flash-based anti-spoofing, randomized prompts, and biometric-signal analysis, claiming 99.9% spoof-detection accuracy to support fraud prevention and identity-verification compliance.

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