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：近年来，基于扩散模型的图像编辑技术对数字视觉内容的真实性构成了重大威胁。传统基于嵌入的水印方法为维持鲁棒性常引入可感知的扰动，不可避免地损害了视觉保真度。与此同时，现有的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。本工作中，我们揭示了一项关键发现：尽管在基于AI的编辑过程中单个图像块发生显著改变，但图像块对之间的相对距离保持相对不变。利用这一特性，我们提出关系型零水印（Rel-Zero）新框架，该框架无需修改原始图像，而是从这些编辑不变的图像块关系中推导出独特的零水印。通过将水印锚定于内在结构一致性而非绝对外观，Rel-Zero为内容认证提供了非侵入式但具有韧性的机制。大量实验表明，相较于先前的零水印方法，Rel-Zero在多种编辑模型及操作场景下均实现了显著提升的鲁棒性。