Robust Image Self-Recovery against Tampering using Watermark Generation with Pixel Shuffling
This addresses the need for robust image authenticity verification in the context of AI-generated content, offering a practical solution for restoring trustworthy data, though it appears incremental as it builds on existing watermarking methods.
The paper tackles the problem of inaccurate recovery of tampered regions in image self-recovery by proposing ReImage, a neural watermarking framework that embeds a shuffled version of the image as a watermark, achieving state-of-the-art performance across diverse tampering scenarios.
The rapid growth of Artificial Intelligence-Generated Content (AIGC) raises concerns about the authenticity of digital media. In this context, image self-recovery, reconstructing original content from its manipulated version, offers a practical solution for understanding the attacker's intent and restoring trustworthy data. However, existing methods often fail to accurately recover tampered regions, falling short of the primary goal of self-recovery. To address this challenge, we propose ReImage, a neural watermarking-based self-recovery framework that embeds a shuffled version of the target image into itself as a watermark. We design a generator that produces watermarks optimized for neural watermarking and introduce an image enhancement module to refine the recovered image. We further analyze and resolve key limitations of shuffled watermarking, enabling its effective use in self-recovery. We demonstrate that ReImage achieves state-of-the-art performance across diverse tampering scenarios, consistently producing high-quality recovered images. The code and pretrained models will be released upon publication.