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Deepfake

Overview

Deepfakes are AI-generated or manipulated media that convincingly imitate real people in face, voice, or video. In KYC and authentication they enable high-grade impersonation: synthetic selfies, lip-synced videos, cloned voices, or face swaps intended to defeat biometric checks. Risks include account opening fraud, account takeover, and social engineering of support staff.
Countermeasures combine presentation attack detection, active video liveness, secure capture with device attestation and cryptographic nonces, and anomaly detection on temporal jitter and compression artifacts. Operationally, raise thresholds for high-risk events, diversify signals with MRZ or NFC checks and registry hits, and run adversarial tests. Education and agent scripts reduce help-desk vulnerability. Governance documents threat models, testing coverage, metrics like attack error rate, and rapid update paths as tools evolve.

FAQ

Why are deepfakes difficult to stop?

Realistic motion and voice synthesis can bypass naive liveness. Defense needs multiple signals, secure capture, and frequent adversarial updates rather than a single static control.

What controls are most effective today?

PAD plus active liveness and device attestation, backed by document or NFC checks and anomaly analytics. No single control suffices for high assurance.

Where are deepfakes appearing in fraud?

Remote onboarding, step-up authentication, and call centers using voice cloning. Train agents, require callbacks or in-app approvals, and monitor for unusual phrasing.

How do we measure readiness and improve?

Run red-team tests, track attack error rates, monitor field anomalies, and publish procedures for rapid threshold and model updates.