From Deepfake Detection to Proactive Authenticity: A Conceptual Biometric Digital Twin Architecture

Short summary

Argues that reactive deepfake detection is a losing battle and proposes a shift to proactive authenticity verification using multi-modal biometric 'digital twins.' Presents pseudocode for enrollment (capturing face mesh, voice print, micro-expressions) and real-time verification against a decentralized ledger. The approach stores cryptographic hashes of biometric traits rather than raw data.

• •Reactive deepfake detection is fundamentally flawed as synthetic media improves
• •Proposes proactive authenticity via multi-modal biometric digital twins on a decentralized ledger
• •Enrollment captures face mesh, voice print, micro-expressions, and physiological signals as cryptographic signatures

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