English(EN) MIG-COW: Transferable Adversarial Attacks on Deepfake Detectors via Gradient Decomposition

新的MIG-COW框架可生成可迁移的对抗性深度伪造检测攻击

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员开发了MIG-COW，一个用于针对深度伪造检测器创建对抗性攻击的新框架。该方法使用动量积分梯度和共轭正交分解来生成可迁移到不同类型的卷积神经网络（CNN）和视觉Transformer（ViT）的攻击。虽然MIG-COW在AADD-2025挑战基准的白盒场景中实现了近乎完美的攻击成功率，但在黑盒场景中的有效性却显著降低，表明在实际可迁移性方面存在挑战。研究还指出，在攻击集成中包含多样化的、性能较低的模型有时会降低其整体有效性。 AI

影响凸显了深度伪造检测持续存在的漏洞以及创建鲁棒、可迁移的对抗性攻击所面临的挑战。

排序理由研究论文，详细介绍了一种针对深度伪造检测器的对抗性攻击新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Wonjune Seo, Joonhyuk Baek, Yeseong Jung, Saerom Park · 2026-10-23 04:00

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

arXiv:2610.30914v1 Announce Type: cross 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 …

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

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