Mitigation

AML.M0034Deepfake Detection

What it is

Apply deepfake detection algorithms against any untrusted or user-provided data, especially in impactful applications such as biometric verification, to block generated content. Detectors may use a combination of approaches, including: - AI models trained to differentiate between real and deepfake content. - Identifying common inconsistencies in deepfake content, such as unnatural facial movements, audio mismatches, or pixel-level artifacts. - Biometrics analysis, such blinking, eye movements, and microexpressions.

References

  1. https://atlas.mitre.org/mitigations/AML.M0034

Related by meaning· 6

Nearest entities by semantic similarity across the cs-graph corpus.

ATLAS mitigation
Adversarial Input Detection
ATLAS mitigation
User Training
ATLAS
Generate Deepfakes
ATLAS mitigation
Model Hardening
ATLAS mitigation
Validate AI Model
ATLAS
Evade AI Model
Sourced from MITRE ATLAS — Adversarial Threat Landscape for AI Systems. Curated by Adam Lundqvist, SQUR.