The rapid development of generative AI techniques enables the synthesis of highly realistic facial images, posing significant challenges for the accurate detection of face forgeries. In contrast to solely elevating detector awareness, proactively reducing the intrinsic difficulty of forgery detection can streamline detector complexity while improving both generalization and robustness. This insight motivates our defense strategy to make face forgery clues more evident. Specifically, a novel proactive approach dubbed Self-Steganographic Detection (SSD) is proposed to imperceptibly embed facial images into themselves as a form of detection evidence. The recovery process is designed to remain robust under normal manipulations while exhibiting deliberate degradation under malicious manipulations, thereby clearly revealing potential forgeries. Unlike embedding bit-level vectors, pixel-level images are informative to ensure the generalization of our approach. Due to the similarity between the protected and embedded images, SSD performs detection without storing any embedded information in advance. To support practical deployment, our approach incorporates a dual detection scheme that aims to identify unprotected images and determine the authenticity of protected images. Extensive experiments using 8 face forgery techniques demonstrate the effectiveness of our approach compared to state-of-the-art methods.
Xia et al. (Thu,) studied this question.