Toward Robust Deepfake Detection: A Proactive Method Based on Watermarking and Knowledge Distillation

Face deepfake detection is a critical technology for verifying the authenticity of facial media content and has long been a focal point in multimedia forensics. However, existing methods face significant challenges, primarily due to their limited ability to generalize across domains. Consequently, the growing variety of forgery techniques, combined with the degradation of visual quality in forged images, makes reliable detection even more difficult. To address these challenges, we propose WKD, a proactive deepfake detection framework based on Watermarking and Knowledge Distillation. The key insights of WKD are twofold: First, we embed watermark information into the Fractional-order Quaternion Radial Harmonic Fourier Moments (FrQRHFMs) space of the host image, achieving a robust balance between imperceptibility and robustness. Second, we design a dual-task learning framework consisting of a watermark extractor and a forgery discriminator, where learnable Low-Rank Adaptation (LoRA) layers are used to transfer knowledge from the extractor to the discriminator, thereby providing additional clues for deepfake detection. Specifically, the integrity of the watermark is compromised only when the host image undergoes a deepfake forgery, while it remains unaffected by conventional attacks. Experimental results on benchmark datasets demonstrate that WKD achieves state-of-the-art performance in both intra-domain and cross-domain deepfake detection, particularly when images are subjected to various conventional attacks.