HashShield: A Robust DeepFake Forensic Framework With Separable Perceptual Hashing
Meihong Yang, Baolin Qi, Ruihe Ma, Yongjin Xian, Bin Ma · IEEE Signal Processing Letters · 2025
The proliferation of DeepFakes has heightened the necessity to distinguish between authentic and counterfeit faces. While numerous methods concentrate on detecting DeepFakes, only a few address safeguarding genuine faces from manipulation. This letter proposes a novel active forensics system for DeepFake forensics utilizing separable perceptual hash enhancement algorithm. A separable perceptual hash code specifically designed for face deep forgery is introduced, achieving robustness while maintaining sensitivity and imperceptibility when embedded within the original image. Additionally, a multi-scale perceptual smoothing loss function is employed to optimize perceptual similarity, structural smoothness, and embedding stability. As a result, this system ensures the consistence of confidential information both before and after manipulation, thereby enhancing the capability of face source detection and DeepFake identification. Experimental results demonstrate that the proposed scheme can effectively counter traditional deep forgery techniques while exhibiting significant potential in preserving personal privacy.