Rel-Zero: Harnessing Patch-Pair Invariance for Robust Zero-Watermarking Against AI Editing

TL;DR

Rel-Zero introduces a novel zero-watermarking framework that leverages invariant patch relations to authenticate images, providing robustness against AI-based edits without altering the original content.

Contribution

It uncovers the invariance of patch-pair relations during AI editing and utilizes this property to develop a resilient, non-invasive zero-watermarking method.

Findings

Significantly improved robustness against AI editing manipulations.

No modification needed to the original image for watermarking.

Outperforms prior zero-watermarking methods in experiments.

Abstract

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.