MIG-COW: Transferable Adversarial Attacks on Deepfake Detectors via Gradient Decomposition

Reported by arXiv cs.CR (Wonjune Seo, Joonhyuk Baek, Yeseong Jung, Saerom Park)

Summary by Cyber Security News23 Oct 2026, 06:00 CET

About this summary. This is a short excerpt from the description that arXiv cs.CR published with its article. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2610.30914

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.

Read the full article at arXiv cs.CR →

Source attribution: headline and facts are from arXiv cs.CR (arxiv.org). Summary method: excerpt of the source description. See our source attribution policy.

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