Federated Learning-Driven Face Forgery Detection via Rich Feature Complementary Fusion

The rapid expansion of cyber–physical–social systems (CPSSs) highlights the critical need for authentic digital interactions. However, this progress is threatened by deepfake technology, which can undermine system security and trust. Existing deepfake detection models, while effective, typically require centralized data and thus create significant privacy and security vulnerabilities. Furthermore, their reliance on single-modal RGB data limits their ability to detect sophisticated forgeries. This work introduces a novel framework that tackles both challenges simultaneously. We leverage federated learning (FL) to enable collaborative model training without sharing sensitive data and preserving privacy. To enhance detection accuracy, we move beyond single-modal inputs by incorporating a spatial-rich model (SRM) to extract high-frequency features indicative of manipulation. These features are then synergistically fused with RGB data via a specialized module to improve the model’s discriminative power. Experimental results on the FF++ dataset demonstrate that the proposed method achieves an accuracy of 92.00% and an AUC of 97.69%, which outperforms other competitors. These results validate the power of the multimodal fusion strategy and FL paradigm.