FaceReclaim: Deep Traceability of Face-Swapped Images Through Feature Decoupling

Face-swapping Deepfake technology enables the replacement of one person’s face with another while preserving the facial attributes (e.g., expressions and postures) of the original person being replaced. Although it has various applications, its misuse raises significant security concerns. In recent years, numerous Deepfake forensic techniques have been proposed, relying on binary classification models to detect the authenticity of facial images. However, another important issue, the feasibility of tracing the original face replaced in the deepfake process, has received little attention, as it intuitively seems impossible. In this work, we consider the problem known as the Traceability of Face-swapping Deepfake (TFD). Unlike existing deepfake detection tasks, TFD focuses on restoring the original faces lost in the face-swapping process. Specifically, we reformulate TFD as a specialized image-to-image editing problem and propose a novel diffusion-based framework named FaceReclaim for TFD. To ensure effective traceability, we develop a Multi-Scale Face Attribute Decoupling (FAD) and a Multi-Modal Face Identity Prompting (FIP) for dual-conditioning guidance. FAD extracts multi-scale attribute information decoupled from the synthetic face-swapped images, providing positive guidance to preserve essential attribute features during restoration. FIP integrates both textual and visual instructions, serving as negative guidance to explicitly direct the model in erasing identity traces of the face-swapped image. Our research shows that the synthetic face still retains subtle, traceable features of the original face, enabling its approximation and restoration. We evaluate our model from both visual restoration performance and the face verification perspective. Extensive experiments demonstrate the qualitative and quantitative effectiveness of our scheme.