Biometric Anti-Spoofing: Benchmarking for a Secure Digital World
Blog post from Didit
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Biometric anti-spoofing, or liveness detection, is increasingly important for protecting digital identity verification from presentation attacks such as photos, video replays, masks, deepfakes, morphs, and injected synthetic data. Its performance is commonly assessed through False Rejection Rate, which measures legitimate users incorrectly denied access, False Acceptance Rate, which measures successful spoof attempts, and the ISO/IEC 30107-3 Presentation Attack Detection Error Rate, including attack success and bona fide user classification errors. The discussion emphasizes that detailed attack taxonomies can reveal system-specific weaknesses and guide algorithm improvements, while balancing fraud prevention with a low-friction user experience. Didit presents its passive liveness detection as an iBeta Level 1-certified solution that reportedly achieves 99.9% accuracy against presentation attacks and integrates with document verification and face matching tools.
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