Proactive Deepfake Defence via Identity Watermarking

Yuan Zhao, Bo Liu, Ming Ding, Baoping Liu, Tianqing Zhu, Xin Yu; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, pp. 4602-4611

The explosive progress of Deepfake techniques poses unprecedented privacy and security risks toward our society by creating real-looking but fake visual content. However, the current Deepfake detection studies are still in their infancy, because they mainly rely on capturing artifacts left by a Deepfake synthesis process as detection clues. These artifacts could be easily obscured due to various distortions (e.g. blurring) and could also be removed with the development of advanced Deepfake techniques, rendering the artifacts-based detection methods less effective in achieving reliable forgery forensics. In this paper, we propose a novel Deepfake detection method that does not depend on identifying the synthesized artifacts, but resorts to a mechanism of anti-counterfeit labels. Specifically, we design a neural network with an encoder-decoder structure to embed messages as anti-Deepfake labels into the facial identity features. Since the injected label is entangled with the facial identity feature, it will be sensitive to face swap translations (i.e., Deepfake), but robust to conventional image modifications (e.g., resize and compress). Therefore, we can check whether the watermarked image has been tampered with by Deepfake methods according to the existence of the label. Experimental results demonstrate that our method can achieve an average detection accuracy of more than 80%, which validates the effectiveness of the proposed method to implement Deepfake detection.