DeepFake, an AI technology for creating facial forgeries, has garnered global attention. Amid such circumstances, forensics researchers focus on developing defensive algorithms to counter these threats. In contrast, there are techniques developed for enhancing the aggressiveness of DeepFake, e.g., through anti-forensics attacks, to disrupt forensic detectors. However, such attacks often sacrifice image visual quality for improved undetectability. To address this issue, we propose a method to generate novel adversarial sharpening masks for launching black-box anti-forensics attacks. Unlike many existing arts, with such perturbations injected, DeepFakes could achieve high anti-forensics performance while exhibiting pleasant sharpening visual effects. After experimental evaluations, we prove that the proposed method could successfully disrupt the state-of-the-art DeepFake detectors. Besides, compared with the images processed by existing DeepFake anti-forensics methods, the visual qualities of anti-forensics DeepFakes rendered by the proposed method are significantly refined.

翻译：DeepFake作为一种用于生成面部伪造的人工智能技术，已引起全球关注。在此背景下，取证研究人员专注于开发防御算法以应对此类威胁。与此同时，也存在旨在增强DeepFake攻击性的技术，例如通过反取证攻击来干扰取证检测器。然而，此类攻击往往以牺牲图像视觉质量为代价来提升不可检测性。为解决此问题，我们提出一种生成新型对抗性锐化掩模的方法，用于发起黑盒反取证攻击。与现有诸多方法不同，通过注入此类扰动，DeepFake既能实现高反取证性能，又能呈现愉悦的锐化视觉效果。实验评估证明，所提方法可成功干扰当前最先进的DeepFake检测器。此外，与现有DeepFake反取证方法处理的图像相比，本方法生成的反取证DeepFake视觉质量得到显著提升。