Deepfake Detection - AI Mitigation
AI MitigationApply 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 mismatche...
Overview
A source-backed snapshot of this defense.
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.
Safeguard details
Where this defense applies and how the source classifies it.
- ATLAS ID
- AML.M0034
- Priority score
- 25
Covered techniques
Attacks this defense is designed to help with.
AML.T0052.001 - Deepfake-Assisted Phishing
Deploy technical controls to detect and block synthetic audio and video. This includes AI-based analysis tools that examine media for artifacts indicative deepfakes.
AML.T0015 - Evade AI Model
Deepfake detection can be used to identify and block generated content.
AML.T0088 - Generate Deepfakes
Deepfake detection can be used to identify and block generated content.
AML.T0052 - Phishing
Deepfake detection can be used to identify and block phishing attempts that use generated content.
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Source evidence
Original public records and references for this page.
Original source
Original source links
Open the public records and source datasets used for this page.