FCL: Frequency-based Contrastive Learning for Generalizable Face Forgery Detection

With the proliferation of deepfake techniques, face forgery detection has emerged as a critical area of research to mitigate the risks associated with facial manipulation. Recent studies have highlighted the superiority of frequency information over color-space input, especially in high-compression scenarios. However, most frequency-enhanced methods employ a two-stream network architecture, assigning separate encoders for RGB and frequency domains, and rely solely on vanilla binary cross-entropy loss, limiting their generalization ability.To address these limitations, we propose a novel framework, Frequency-enhanced Contrastive Learning (FCL), which trains the model in a supervised manner using contrastive loss. FCL treats two views of different modalities generated from the same image as positive pairs and samples with opposite labels as negative pairs. This approach pulls features from the RGB and high-frequency domains closer while pushing features of pristine and forgery faces apart. Additionally, we introduce a Shallow Feature Supplement (SFS) module to complement local information from low-level shallow feature embeddings into high-level feature maps, and a Dual Modal Fusion (DMF) module to adaptively aggregate information from both domains.Extensive experiments on seven datasets demonstrate the superior generalization of our method compared to state-of-the-art competitors. Notably, FCL achieves a remarkable accuracy of 92.29% and an AUC of 94.25% on the heavily compressed FaceForensics++ dataset, showcasing its robustness against compression. These results underscore the potential of FCL as a powerful tool for face forgery detection in real-world scenarios.The source code ofour proposed algorithm are available at https://github.com/Y30230924Wang/Frequency-Enhanced-Contrastive-Learning.