TAG-WM：基于扩散反转敏感性的篡改感知生成式图像水印方法 (TAG-WM: Tamper-Aware Generative Image Watermarking via Diffusion Inversion Sensitivity)

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. However, the widespread adoption and advancing capabilities of generative image editing tools have amplified malicious tampering risks, while simultaneously posing new challenges to passive tampering detection and watermark robustness. To address these challenges, 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 demonstrate that TAG-WM achieves state-of-the-art performance in both tampering robustness and localization capability even under distortion, while preserving lossless generation quality and maintaining a watermark capacity of 256 bits. The code is available at: https://github.com/Suchenl/TAG-WM.

翻译：人工智能生成内容（AIGC）虽然实现了高效的视觉创作，但也带来了版权与真实性风险。作为完整性验证和溯源追踪的常用技术，数字图像水印被视为解决上述问题的潜在方案。然而，生成式图像编辑工具的广泛普及与能力提升，既加剧了恶意篡改风险，也对被动篡改检测与水印鲁棒性提出了新挑战。为应对这些挑战，本文提出一种名为TAG-WM的篡改感知生成式图像水印方法。该方法包含四个核心模块：采用双标记联合采样（DMJS）算法在潜在空间中嵌入版权水印与定位水印，同时保持生成质量；利用逆向DMJS实现水印潜在重构（WLR）；基于扩散反转敏感性构建稠密变异区域检测器（DVRD），通过统计偏差分析识别篡改区域；以及由定位结果引导的篡改感知解码（TAD）模块。实验结果表明，TAG-WM在失真条件下仍能实现篡改鲁棒性与定位能力的最优性能，同时保持无损生成质量并维持256比特的水印容量。代码已开源：https://github.com/Suchenl/TAG-WM。