WaveGuard: Robust Deepfake Detection and Source Tracing via Dual-Tree Complex Wavelet and Graph Neural Networks

Deepfake technology has great potential in the field of media and entertainment, but it also brings serious risks, including privacy disclosure and identity fraud. To counter these threats, proactive forensic methods have become a research hotspot by embedding invisible watermark signals to build active protection schemes. However, existing methods are vulnerable to watermark destruction under malicious distortions, which leads to insufficient robustness. Moreover, embedding strong signals may degrade image quality, making it challenging to balance robustness and imperceptibility. Although watermarked images look natural, their underlying structures are often different from the original images, which is ignored by traditional watermarking methods. To address these issues, this paper proposes a proactive watermarking framework called WaveGuard, which explores frequency domain embedding and graph-based structural consistency optimization. In this framework, the watermark is embedded into the high-frequency sub-bands by dual-tree complex wavelet transform (DT-CWT) to enhance the robustness against distortions and deepfake forgeries. By leveraging joint sub-band correlations and selected sub-band combinations, the framework enables robust source tracing and semi-robust deepfake detection. To enhance imperceptibility, we propose a Structural Consistency Graph Neural Network (SC-GNN) that constructs graph representations of the original and watermarked images to ensure structural consistency and reduce perceptual artifacts. Experimental results show that the proposed method performs exceptionally well in face swap and face replay tasks. The code has been published at https://github.com/vpsg-research/WaveGuard.