Dual-Task Mutual Learning With QPHFM Watermarking for Deepfake Detection

Deepfake technology has rapidly evolved and emerged in recent years, posing significant threats to individuals' reputations and security. Although passive detection methods can achieve reasonable accuracy, they still lack proactive defense mechanisms. To address this issue, this letter proposes a proactive detection framework that combines Quaternion Polar Harmonic Fourier Moments (QPHFMs) with Dual-Task Mutual Learning (DTML) framework. Firstly, watermark information is embedded into QPHFMs, ensuring high imperceptibility while enhancing robustness against common attacks. Secondly, DTML is introduced, where the knowledge distilled from watermark detection can facilitate more accurate deepfake detection. Experimental results on benchmark datasets demonstrate that our method surpasses state-of-the-art techniques, delivering exceptional performance in watermark robustness and imperceptibility while simultaneously accomplishing accurate deepfake detection.