  Malicious Deepfakes have led to a sharp conflict over distinguishing between
genuine and forged faces. Although many countermeasures have been developed to
detect Deepfakes ex-post, undoubtedly, passive forensics has not considered any
preventive measures for the pristine face before foreseeable manipulations. To
complete this forensics ecosystem, we thus put forward the proactive solution
dubbed SepMark, which provides a unified framework for source tracing and
Deepfake detection. SepMark originates from encoder-decoder-based deep
watermarking but with two separable decoders. For the first time the deep
separable watermarking, SepMark brings a new paradigm to the established study
of deep watermarking, where a single encoder embeds one watermark elegantly,
while two decoders can extract the watermark separately at different levels of
robustness. The robust decoder termed Tracer that resists various distortions
may have an overly high level of robustness, allowing the watermark to survive
both before and after Deepfake. The semi-robust one termed Detector is
selectively sensitive to malicious distortions, making the watermark disappear
after Deepfake. Only SepMark comprising of Tracer and Detector can reliably
trace the trusted source of the marked face and detect whether it has been
altered since being marked; neither of the two alone can achieve this.
Extensive experiments demonstrate the effectiveness of the proposed SepMark on
typical Deepfakes, including face swapping, expression reenactment, and
attribute editing.
