One-Identity-One-Key: Region-Aware Watermark for Proactive Deepfake Detection and Robust Source Tracing

Proactive forensic methods based on watermarking have attracted increasing attention as an effective paradigm for deepfake detection and source tracing. However, the existing methods still have shortcomings in encryption security, robustness and detection performance when dealing with complex forgery. In this paper, we propose a region-aware watermarking based proactive deepfake detection framework named Segfacemark. Building on global watermark embedding, this method explicitly models the sensitivity of various semantic facial regions to tampering. By employing region-wise partitioning and redundant embedding, the framework strengthens the protection of key facial areas, thereby comprehensively enhancing watermark robustness. In addition, the proposed framework employs a dynamic encryption mechanism that derives personalized keys from identity features to encrypt structural features, thereby realizing a “one-identity–one-key” watermark generation strategy and significantly enhancing system security and robustness against forgery. In the detection stage, the dual verification mechanism of identity decryption consistency and global region-aware watermark consistency not only improves detection accuracy but also enables forgery type identification, and further enhances the interpretability of deepfake detection results through region-aware watermarks. Experimental results show that the proposed method achieves high accuracy and robustness in deepfake detection, while preserving visual fidelity. The code will be released at: https://github.com/vpsg-research/Segfacemark.