提出可逃避检测的深度伪造伪装技术，揭示真实视频也可能被误判为伪造。
Active Fake: DeepFake Camouflage
- 通过对抗学习生成难以察觉的融合不一致
- 在真实视频中制造伪造特征，有效误导检测器
- 适合研究伪造检测漏洞或防御机制的人阅读
深度伪造技术因其高保真度操控面部特征而引发广泛关注，其中人脸替换型伪造最具危害性，通过替换原脸生成虚假行为。现有基于深度神经网络的取证方法主要依赖捕捉伪造人脸中的融合不一致性，但这也催生了新型安全威胁——主动伪造（Active Fake）：个体故意在真实视频中制造融合不一致以逃避责任，这种行为称为深度伪造伪装。为此，本文提出一种新框架，可在保证不可感知性、有效性与迁移性的前提下，生成融合不一致。该框架通过对抗学习优化，构建出难以察觉却能有效误导取证检测器的伪造特征。大量实验验证了该方法的有效性与鲁棒性，凸显了主动伪造检测研究的紧迫性。
原文摘要 · Abstract (English)
DeepFake technology has gained significant attention due to its ability to manipulate facial attributes with high realism, raising serious societal concerns. Face-Swap DeepFake is the most harmful among these techniques, which fabricates behaviors by swapping original faces with synthesized ones. Existing forensic methods, primarily based on Deep Neural Networks (DNNs), effectively expose these manipulations and have become important authenticity indicators. However, these methods mainly concentrate on capturing the blending inconsistency in DeepFake faces, raising a new security issue, termed Active Fake, emerges when individuals intentionally create blending inconsistency in their authentic videos to evade responsibility. This tactic is called DeepFake Camouflage. To achieve this, we introduce a new framework for creating DeepFake camouflage that generates blending inconsistencies while ensuring imperceptibility, effectiveness, and transferability. This framework, optimized via an adversarial learning strategy, crafts imperceptible yet effective inconsistencies to mislead forensic detectors. Extensive experiments demonstrate the effectiveness and robustness of our method, highlighting the need for further research in active fake detection.
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