Face-swap DeepFake is an emerging AI-based face forgery technique that can replace the original face in a video with a generated face of the target identity while retaining consistent facial attributes such as expression and orientation. Due to the high privacy of faces, the misuse of this technique can raise severe social concerns, drawing tremendous attention to defend against DeepFakes recently. In this paper, we describe a new proactive defense method called FakeTracer to expose face-swap DeepFakes via implanting traces in training. Compared to general face-synthesis DeepFake, the face-swap DeepFake is more complex as it involves identity change, is subjected to the encoding-decoding process, and is trained unsupervised, increasing the difficulty of implanting traces into the training phase. To effectively defend against face-swap DeepFake, we design two types of traces, sustainable trace (STrace) and erasable trace (ETrace), to be added to training faces. During the training, these manipulated faces affect the learning of the face-swap DeepFake model, enabling it to generate faces that only contain sustainable traces. In light of these two traces, our method can effectively expose DeepFakes by identifying them. Extensive experiments are conducted on the Celeb-DF dataset, compared with recent passive and proactive defense methods, and are studied thoroughly regarding various factors, corroborating the efficacy of our method on defending against face-swap DeepFake.

翻译：换脸深度伪造是一种新兴的基于AI的面部伪造技术，可在视频中利用目标身份生成的面部替换原始人脸，同时保留表情、朝向等一致的面部属性。由于人脸的高度隐私性，该技术的滥用可能引发严重的社会问题，近期促使学术界高度关注深度伪造防御研究。本文提出一种名为FakeTracer的新型主动防御方法，通过在训练阶段植入痕迹来揭露换脸深度伪造。与通用人脸合成深度伪造相比，换脸深度伪造因涉及身份变换、受编解码过程支配且采用无监督训练而更为复杂，增加了向训练阶段植入痕迹的难度。为有效防御换脸深度伪造，我们设计了两类痕迹：可持续痕迹（STrace）和可擦除痕迹（ETrace），并将其添加至训练人脸中。在训练过程中，这些经处理的人脸会干扰换脸深度伪造模型的学习，使其仅能生成包含可持续痕迹的面部。基于这两类痕迹，我们的方法可通过识别痕迹有效揭露深度伪造。我们在Celeb-DF数据集上开展了广泛实验，与近期被动与主动防御方法进行对比，并深入研究了多种影响因素，充分验证了本方法在防御换脸深度伪造方面的有效性。