Towards High Fidelity Face Swapping: A Comprehensive Survey and New Benchmark
For researchers in face swapping and deepfake generation, this work offers a unified taxonomy and a principled evaluation framework to address the fragmentation and inconsistent evaluation in the field.
This paper provides a comprehensive survey of face swapping methods, organizing them into five paradigms, and introduces a new benchmark (CASIA FaceSwapping) with standardized protocols to enable fair evaluation. Extensive experiments on representative methods yield new insights into current performance characteristics and limitations.
Face swapping has witnessed significant progress in recent years, largely driven by advances in deep generative models such as GANs and diffusion models.Despite these advances, existing methods remain fragmented across different paradigms, and their evaluation is highly inconsistent due to the lack of standardized datasets and protocols. Moreover, prior surveys primarily focus on broader deepfake generation or detection, leaving face swapping insufficiently studied as a standalone problem. In this paper, we present a comprehensive survey and benchmark for face swapping. We provide a structured review of existing methods, organizing them into five major paradigms and systematically analyzing their design principles, strengths, and limitations. To enable fair and controlled evaluation, we introduce CASIA FaceSwapping, a high-quality benchmark with balanced demographic distributions and explicit attribute variations, and establish standardized protocols to assess the robustness of different face swapping methods. Extensive experiments on representative approaches yield new insights into the performance characteristics and limitations of current techniques. Overall, our work provides a unified perspective and a principled evaluation framework to facilitate the development of more robust and controllable face swapping methods. More results can be found at https://github.com/CASIA-NLPRAI/face-swapping-survey.