Robust Image Self-Recovery against Tampering using Watermark Generation with Pixel Shuffling

Minyoung Kim, Paul Hongsuck Seo

Keywords: Self-recovery, Watermarking

TL;DR: We embed a shuffled version of the image as a watermark and recover tampered regions via image enhancement module. Our method restores fine details better than existing approaches.

Abstract: 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.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 11719