标题： TAG-WM：通过扩散反演敏感性感知篡改的生成图像水印 显示英文标题

标题： TAG-WM: Tamper-Aware Generative Image Watermarking via Diffusion Inversion Sensitivity

摘要： 人工智能生成内容（AIGC）实现了高效的视觉创作，但引发了版权和真实性风险。作为一种常见的完整性验证和来源追踪技术，数字图像水印被视为上述问题的潜在解决方案。其中，能够保持生成质量的水印方法正受到越来越多的关注。然而，生成图像编辑应用的普及和高性能提高了恶意篡改的风险，带来了新的需求。1）当前无损视觉质量水印的篡改鲁棒性仍受制于修改敏感的扩散逆过程，需要增强鲁棒性。2）改进的篡改质量和快速迭代周期使得被动篡改检测方法不足，使主动篡改定位能力成为水印所需的功能。为解决这些需求，本文提出了一种名为TAG-WM的感知篡改的生成图像水印方法。所提出的方法包括四个关键模块：一种双标记联合采样（DMJS）算法，用于在保持生成质量的同时将版权和定位水印嵌入潜在空间，利用反向DMJS的水印潜在重建（WLR），一种利用扩散逆过程敏感性的密集变化区域检测器（DVRD），通过统计偏差分析识别被篡改区域，以及由定位结果引导的感知篡改解码（TAD）。实验结果表明，TAG-WM在保持无损生成质量和256位相当容量的同时，实现了最先进的篡改鲁棒性和篡改定位能力。 显示英文摘要

摘要： AI-generated content (AIGC) enables efficient visual creation but raises copyright and authenticity risks. As a common technique for integrity verification and source tracing, digital image watermarking is regarded as a potential solution to above issues. Among these, watermarking methods capable of preserving the generation quality are receiving increased attention. However, the proliferation and high performance of generative image editing applications have elevated the risks of malicious tampering, creating new demands. 1) The tamper robustness of current lossless visual quality watermarks remains constrained by the modification-sensitive diffusion inversion process, necessitating enhanced robustness. 2) The improved tampering quality and rapid iteration cycles render passive tampering detection methods inadequate, making proactive tampering localization capability a desired feature for watermarks. To address these requirements, this paper proposes a Tamper-Aware Generative image WaterMarking method named TAG-WM. The proposed method comprises four key modules: a dual-mark joint sampling (DMJS) algorithm for embedding copyright and localization watermarks into the latent space while preserving generative quality, the watermark latent reconstruction (WLR) utilizing reversed DMJS, a dense variation region detector (DVRD) leveraging diffusion inversion sensitivity to identify tampered areas via statistical deviation analysis, and the tamper-aware decoding (TAD) guided by localization results. The experimental results indicate that TAG-WM achieves SOTA tampering robustness and tampering localization capability with distortions while maintaining lossless generation quality and a considerable capacity of 256 bits.