MagDR：用于防御Deepfakes的掩模引导检测和重建
MagDR: Mask-guided Detection and Reconstruction for Defending Deepfakes
摘要：
Deepfakes引发了人们对视觉内容真实性的严重担忧。之前的工作揭示了通过向源数据添加对抗性扰动来破坏deepfakes的可能性，但我们认为这种威胁尚未消除。本文介绍了MagDR，这是一种用于防御deepfakes免受对抗攻击的掩码引导检测和重建管道。MagDR从一个检测模块开始，定义了一些判断deepfakes输出异常的标准，然后用它来指导可学习的重建过程。提取自适应掩模以捕获局部面部区域的变化。在实验中，MagDR防御了deepfakes的三个主要任务，学习的重建管道在输入数据之间传输，在防御黑盒和白盒攻击方面表现出良好的性能。
英文摘要：
Deepfakes raised serious concerns on the authenticity of visual contents. Prior works revealed the possibility to disrupt deepfakes by adding adversarial perturbations to the source data, but we argue that the threat has not been eliminated yet. This paper presents MagDR, a mask-guided detection and reconstruction pipeline for defending deepfakes from adversarial attacks. MagDR starts with a detection module that defines a few criteria to judge the abnormality of the output of deepfakes, and then uses it to guide a learnable reconstruction procedure. Adaptive masks are extracted to capture the change in local facial regions. In experiments, MagDR defends three main tasks of deepfakes, and the learned reconstruction pipeline transfers across input data, showing promising performance in defending both black-box and white-box attacks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
