Deepfake Detection
Methods that detect synthetic or manipulated media (audio, images, video) used to impersonate people in identity verification and biometric systems.
Overview
Deepfake detection aims to identify AI-generated or heavily manipulated media intended to defeat identity verification (e.g., selfie checks) or to enable fraud via impersonation.
How it’s used in identity systems
- As a signal alongside PAD/liveness, device checks, and document verification.
- To flag suspicious submissions for step-up or manual review.
- To harden enrollment and re-verification, where synthetic media can create or take over accounts.
Common challenges
- Rapidly evolving generation methods and attack techniques.
- Domain shifts (lighting, cameras, compression) that can affect detector performance.
References
Vendors using Deepfake Detection
Latest Data Cards
Data Card Mitek enhances video verification to counter injection attacks in Spanish market
2026-02-16CC-BY-4.0video-injection-detectiondeepfake-detectionMitek enhanced its SEPBLAC-compliant digital onboarding platform for Spain with four defense layers against injection attacks—deepfake detection, digital manipulation analysis, injection attack protection, and face gallery analysis—achieving 99.9% accuracy on known deepfake engines and over 99% detection rates for face swaps.
- 89% of Spanish companies reported increased fraud attempts annually, with identity fraud the leading cause of corporate fraud loss in Spain.
- The update aligns with February 2025's new European injection attack standard, CEN/TS 18099:2025.
- Mitek's approach combines AI-trained detection with traditional software checks targeting coordinated fraud attempts during onboarding.