What Is Deepfake Detection Technology?

The main detection approaches are: (1) artifact analysis — identifying pixel-level inconsistencies left by generative models; (2) biometric inconsistency detection — finding unnatural facial geometry, blinking patterns, or eye movements; (3) GAN fingerprinting — detecting statistical signatures in the frequency domain; (4) face embedding comparison — identifying whether a face in the content matches a known individual's biometric profile. For NCII victim protection, face embedding comparison is the most victim-relevant approach because it answers the victim's question: 'Is my face in this content?' rather than the forensic question: 'Is this content AI-generated?'

Key facts about this term

1. Artifact analysis detects generation model signatures Each generative AI model leaves subtle statistical signatures in the images it produces. Forensic tools can detect these signatures even in visually convincing deepfakes.
2. Biometric face comparison is victim-focused detection For NCII, the question is 'does this depict me?' — not 'is this fake?' Biometric face embedding comparison directly answers the victim's question and is the foundation of ScanErase's detection system.
3. Detection accuracy lags behind generation capability As generative AI improves, deepfakes become harder to detect through artifact analysis. Biometric matching is more robust because it relies on the perpetually stable target identity, not the evolving artifacts of generators.