Learning what to protect: role-aware representations for image self-recovery against tampering

With the rapid advancement of generative AI and image manipulation techniques, distinguishing authentic content from manipulated images has become increasingly challenging, raising growing forensic and security concerns. Existing image forensics methods can detect and localize manipulation but rarely recover the original content. Image self-recovery attempted to address this limitation by embedding recovery information into the image as a watermark. However, existing methods still face a longstanding trade-off between watermark imperceptibility and recovery fidelity. They typically protect information from the entire image without considering that different information contributes differently to recovery, resulting in redundant recovery information and a higher embedding burden. Hence, we propose ProCast, a novel self-recovery framework to tackle this fundamental challenge by jointly addressing three interdependent questions: what to protect, where to embed, and how to recover. ProCast learns to organize image information into components with different recovery roles and applies different protection strategies accordingly, reducing unnecessary recovery information and the embedding burden. It further adopts content-adaptive embedding, and reliability-based recovery to achieve high-fidelity recovery with improved watermark imperceptibility. Notably, although trained only on splicing, ProCast generalizes to unseen AIGC and conventional manipulations. Extensive experiments show that ProCast achieves higher watermark imperceptibility, more accurate tampering localization, and higher-fidelity recovery, improving the quality (PSNR) of watermarked image by 3.8 dB and the quality of recovered image by around 2-8 dB over the strongest existing self-recovery baselines.