Deepfake Detection Method Combining Multi-stage Feature Disentanglement and Frequency-Domain Information
LIN Liwei1,2, LI Yang1,2, ZHU Hengliang1,2, WANG Mengqiang1,2, HUANG Chuan3, CHEN Jianwei3, ZHANG Jing1,2, CHEN Bixia1,2
1. Fujian Provincial Key Laboratory of Big Data Mining and App-lications, Fujian University of Technology, Fuzhou 350118; 2. School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118; 3. College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117
Abstract：Deepfake detection is faced with significant challenges due to its limited generalization capability and poor adaptability to unseen forgery techniques. To address these issues, a deepfake detection method combining multi-stage feature disentanglement and frequency-domain information(MFD-FD) is proposed. First, a hierarchical feature disentanglement strategy is designed. By introducing a forgery suppression loss and a reconstruction loss, content features are progressively separated from artifact features from shallow to deep layers. Thus, the coupling between the two features is effectively reduced with critical information preserved and the model can focus on more purified artifact representations. Next, frequency domain information is introduced to compensate for the deficiency of spatial features in spectral information, thereby enhancing the detection stability of the model against perturbations such as image compression. Finally, a frequency-domain fusion data augmentation method based on a cosine transition mask is presented to enhance the model robustness by synthesizing diverse forged samples. Extensive experiments demonstrate that MFD-FD outperforms the state-of-the-art methods in both generalization and robustness.
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2026, Vol. 39 
