Generalizable Face Forgery Detection via Separable Prompt Learning

Detecting face forgeries using CLIP has recently emerged as a promising direction. However, most existing methods focus on adapting its visual encoder, leaving the potential of the textual encoder largely underexplored. In this paper, we propose Separable Prompt Learning (SePL) to better exploit the text modality, which further enhances the detection capacity. Specifically, SePL distills the forgery knowledge from CLIP via two separate learnable prompts, supported by a cross-modality alignment strategy and dedicated objectives. Extensive experiments demonstrate that our method achieves superior performance under both cross-dataset and cross-method evaluation. The code has been released at https://github.com/OUC-YER/SePL-DeepfakeDetection.