用像素乱序水印实现图像篡改后自动恢复，效果优于现有方法。
Robust Image Self-Recovery against Tampering using Watermark Generation with Pixel Shuffling
- 将原图像素打乱后嵌入自身作为水印，辅助恢复篡改内容。
- 在多种篡改场景下均实现高质量恢复，显著提升准确率。
- 适合数字媒体真实性验证与内容可信修复的研究者使用。
人工智能生成内容（AIGC）的迅猛发展引发了对数字媒体真实性的担忧。在此背景下，图像自恢复——从被篡改版本中重建原始内容——为理解攻击意图和恢复可信数据提供了实用方案。然而，现有方法常难以准确恢复篡改区域，未能达成自恢复的核心目标。为此，我们提出ReImage，一种基于神经水印的自恢复框架，将目标图像的像素乱序版本嵌入自身作为水印。设计了针对神经水印优化的生成器，并引入图像增强模块以优化恢复结果。进一步分析并解决了乱序水印的关键局限，使其有效应用于自恢复任务。实验表明，ReImage在多种篡改场景下均达到领先性能，持续生成高质量恢复图像。代码与预训练模型将在论文发表后公开。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。