Live Deepfake Injection to Evade Mobile KYC Verification
A red team defeated a mobile facial-authentication / KYC service by injecting a live, face-swapped deepfake video into the camera feed — impersonating a target and passing liveness checks to open fraudulent accounts.
What happened
Catalogued by MITRE ATLAS as AML.CS0033, the iProov Red Team defeated a mobile facial biometric / KYC service by injecting a live, face-swapped deepfake into the camera feed. Rather than presenting a photo to the lens (a presentation attack), the technique injects synthesized video directly into the capture pipeline, letting the attacker impersonate a target identity and satisfy the system’s liveness checks — enough to pass identity verification and open fraudulent accounts.
It targets exactly the AI meant to stop fraud: the face-matching and liveness models. As generative face tools have gotten cheap and real-time, injection attacks against onboarding and KYC have become a practical threat to any app relying on selfie verification.
Why it maps to ATLAS
- AML.T0088 — Generate Deepfakes: a synthetic face video of the target is produced.
- AML.T0016.002 — Obtain Capabilities: Generative AI: off-the-shelf generative tools create the fake.
- AML.T0073 — Impersonation: the deepfake impersonates a specific identity.
- AML.T0015 — Evade AI Model: the liveness/face-matching model is fooled.
- AML.T0048.000 — External Harms: Financial Harm: the outcome is account fraud.
Detection notes
Presentation-attack detection alone doesn’t stop injection attacks. Harden the capture path: device attestation, secure/verified camera pipelines, and virtual-camera detection, plus liveness challenges tuned to surface face-swap artifacts. For high-stakes KYC, use risk-based step-up — secondary factors or human review — rather than trusting a single selfie.
Detection
Log sources
- Camera pipeline integrity / virtual-camera detection
- Presentation-attack-detection (PAD) + liveness telemetry
- Device attestation + injection-detection signals
Signals
- Virtual-camera or injected video sources feeding the verification app
- Face-swap / synthesis artifacts under liveness challenges
- Onboarding attempts inconsistent with device/environment attestation
Mitigations
- Inject-resistant capture: device attestation, secure camera pipeline
- Advanced liveness + deepfake-artifact detection challenges
- Risk-based step-up (human review, secondary factors) for KYC