MIG-COW: Transferable Adversarial Attacks on Deepfake Detectors via Gradient Decomposition
MIG-COW demonstrates that gradient-decomposition-based adversarial attacks can defeat diverse deepfake detectors, undermining trust in automated media-verification pipelines.
Summary written by editorial AI · Source link below
arXiv:2610.30914v1 Announce Type: new Abstract: Despite recent advances, deepfake detectors remain vulnerable to adversarial examples, particularly in diverse, real-world settings. We propose MIG-COW, a novel adversarial attack framework that generates highly generalizable and visually imperceptible adversarial examples. By combining momentum-integrated gradients with a consensus-orthogonal decomposition, MIG-COW captures both shared and model-specific vulnerabilities across heterogeneous CNN a
Editorial Analysis
Enterprises relying on automated deepfake detection for media verification or KYC processes should note that transferable adversarial attacks are becoming more practical.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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