The rapid progress of Deepfake technology has made face swapping highly realistic, raising concerns about the malicious use of fabricated facial content. Existing methods often struggle to generalize to unseen domains due to the diverse nature of facial manipulations. In this paper, we revisit the generation process and identify a universal principle: Deepfake images inherently contain information from both source and target identities, while genuine faces maintain a consistent identity. Building upon this insight, we introduce DiffusionFake, a novel plug-and-play framework that reverses the generative process of face forgeries to enhance the generalization of detection models. DiffusionFake achieves this by injecting the features extracted by the detection model into a frozen pre-trained Stable Diffusion model, compelling it to reconstruct the corresponding target and source images. This guided reconstruction process constrains the detection network to capture the source and target related features to facilitate the reconstruction, thereby learning rich and disentangled representations that are more resilient to unseen forgeries. Extensive experiments demonstrate that DiffusionFake significantly improves cross-domain generalization of various detector architectures without introducing additional parameters during inference. Our Codes are available in https://github.com/skJack/DiffusionFake.git.

本研究针对现有深伪检测方法在未知领域泛化能力不足的问题，提出了一种新颖的解决方案DiffusionFake。通过逆向生成伪造面孔的过程，该框架通过引导重构提取出的特征，提升了检测模型的生成特征的表征能力，从而在面对未见伪造面孔时表现出更强的鲁棒性。实验结果表明，DiffusionFake显著提高了多种检测架构的跨域泛化能力。

DiffusionFake：通过引导稳定扩散增强深伪检测的泛化能力