Recent DeepFake detection methods have shown excellent performance on public datasets but are significantly degraded on new forgeries. Solving this problem is important, as new forgeries emerge daily with the continuously evolving generative techniques. Many efforts have been made for this issue by seeking the commonly existing traces empirically on data level. In this paper, we rethink this problem and propose a new solution from the unsupervised domain adaptation perspective. Our solution, called DomainForensics, aims to transfer the forgery knowledge from known forgeries to new forgeries. Unlike recent efforts, our solution does not focus on data view but on learning strategies of DeepFake detectors to capture the knowledge of new forgeries through the alignment of domain discrepancies. In particular, unlike the general domain adaptation methods which consider the knowledge transfer in the semantic class category, thus having limited application, our approach captures the subtle forgery traces. We describe a new bi-directional adaptation strategy dedicated to capturing the forgery knowledge across domains. Specifically, our strategy considers both forward and backward adaptation, to transfer the forgery knowledge from the source domain to the target domain in forward adaptation and then reverse the adaptation from the target domain to the source domain in backward adaptation. In forward adaptation, we perform supervised training for the DeepFake detector in the source domain and jointly employ adversarial feature adaptation to transfer the ability to detect manipulated faces from known forgeries to new forgeries. In backward adaptation, we further improve the knowledge transfer by coupling adversarial adaptation with self-distillation on new forgeries. This enables the detector to expose new forgery features from unlabeled data and avoid forgetting the known knowledge of known...

翻译：近期深度伪造检测方法在公开数据集上表现出色，但在面对新型伪造时性能显著下降。解决这一问题至关重要，因为随着生成技术的持续演进，新型伪造手段层出不穷。现有研究多从数据层面出发，通过经验性地寻找普遍存在的伪造痕迹来应对该问题。本文重新审视该问题，并从无监督域适应的角度提出新方案。我们提出的DomainForensics方法旨在将已知伪造的知识迁移至新型伪造领域。与现有研究不同，本方案不聚焦于数据视角，而是通过对齐域差异，优化深度伪造检测器的学习策略以捕获新型伪造特征。具体而言，与仅考虑语义类别知识迁移、应用范围受限的传统域适应方法不同，我们的方法能够捕获微妙的伪造痕迹。我们提出一种专用于跨域捕获伪造知识的双向适应策略：该策略同时考虑前向适应和反向适应，通过前向适应将源域的伪造知识迁移至目标域，再通过反向适应将目标域知识逆向迁移回源域。在前向适应阶段，我们对源域的深度伪造检测器进行监督训练，并联合对抗特征适应技术，将检测操纵人脸的能力从已知伪造迁移至新型伪造。在反向适应阶段，我们将对抗适应与新型伪造的自蒸馏技术相结合，进一步强化知识迁移。这种设计使检测器能够从无标签数据中发掘新型伪造特征，同时避免遗忘已知伪造的相关知识...