Recent advancements in diffusion-based image editing pose a significant threat to the authenticity of digital visual content. Traditional embedding-based watermarking methods often introduce perceptible perturbations to maintain robustness, inevitably compromising visual fidelity. Meanwhile, existing zero-watermarking approaches, typically relying on global image features, struggle to withstand sophisticated manipulations. In this work, we uncover a key observation: while individual image patches undergo substantial alterations during AI-based editing, the relational distance between patch pairs remains relatively invariant. Leveraging this property, we propose Relational Zero-Watermarking (Rel-Zero), a novel framework that requires no modification to the original image but derives a unique zero-watermark from these editing-invariant patch relations. By grounding the watermark in intrinsic structural consistency rather than absolute appearance, Rel-Zero provides a non-invasive yet resilient mechanism for content authentication. Extensive experiments demonstrate that Rel-Zero achieves substantially improved robustness across diverse editing models and manipulations compared to prior zero-watermarking approaches.

翻译：近期基于扩散模型的图像编辑技术的进步对数字视觉内容的真实性构成了严重威胁。传统的嵌入型水印方法为保持鲁棒性常引入可感知的扰动，不可避免地损害视觉保真度。与此同时，现有零水印方法通常依赖全局图像特征，难以抵御复杂篡改操作。本研究揭示了一个关键现象：虽然单个图像块在基于AI的编辑过程中会发生显著改变，但图像块对之间的关联距离却保持相对不变。利用这一特性，我们提出关联零水印（Rel-Zero）这一新型框架，该方法无需修改原始图像，而是从这些编辑不变的图像块关系中提取唯一的零水印。通过将水印根植于内在结构一致性而非绝对外观特征，Rel-Zero为非侵入式内容认证提供了兼具鲁棒性的保护机制。大量实验表明，与现有零水印方法相比，Rel-Zero在应对多种编辑模型和篡改操作时均展现出显著提升的鲁棒性。