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.

翻译：摘要：恶意深度伪造技术引发了真假人脸辨别的激烈冲突。尽管已开发出众多事后检测深度伪造的方法，但被动取证显然未考虑原始人脸在可预见的篡改发生前的任何预防措施。为完善这一取证生态体系，我们提出名为SepMark的主动防御方案，该方案为源头追溯与深度伪造检测提供统一框架。SepMark源自基于编码器-解码器的深度水印技术，但创新性地采用双可分离解码器架构。作为首个深度可分离水印方案，SepMark为既有的深度水印研究领域带来了全新范式：单个编码器优雅嵌入单一水印，两个解码器则能分别在不同鲁棒性层级独立提取水印。其中名为追踪器（Tracer）的鲁棒解码器能抵抗各类失真，其鲁棒性程度过高，可使水印在深度伪造前后均得以存续；而名为检测器（Detector）的半鲁棒解码器则对恶意失真具有选择性敏感，导致水印在深度伪造后消失。唯有兼具追踪器与检测器的SepMark才能可靠追溯被标记人脸的可信来源，并检测其自标记后是否被篡改——两者单独均无法实现此功能。大量实验证明，所提出的SepMark在典型深度伪造场景（包括换脸、表情复现及属性编辑）中均具有效性。