Toward Robust Proactive Deepfake Detection via Orthogonal Moment Watermarking

Summary

Researchers propose a proactive Deepfake detection framework that embeds a watermark into the Quaternion Radial Harmonic Fourier Moments (QRHFMs) domain of face images. A Local-Enhanced State Space (LSS) block extracts features, and a dual-branch architecture pairs a watermark extractor with a forgery discriminator via knowledge distillation. On benchmark datasets the method reports 92.58% average accuracy and 95.96% AUC, with robustness to image quality degradation and to Deepfake techniques unseen during training.

Topics

• Deepfakes and impersonation