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

FakeTracer: Proactively Defending Against Face-swap DeepFakes via Implanting Traces in Training