FakeTracer: Proactively Defending Against Face-swap DeepFakes via
  Implanting Traces in Training
Pu Sun Honggang Qi Yuezun Li Siwei Lyu
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.