ONRW: Optimizing inversion noise for high-quality and robust watermark

2026-01-24

AI Summary

This work proposes a high-fidelity robust image watermarking framework based on diffusion models to address the challenge of simultaneously achieving robustness against image distortions and preserving visual quality. The method leverages null-text guidance to invert images into latent noise representations, where watermark embedding and noise optimization are jointly performed. To prevent semantic distortion, it incorporates self-attention constraints and a pseudo-masking strategy during the embedding process, thereby enhancing both watermark robustness and image fidelity. Experimental results on the COCO dataset demonstrate that the proposed approach achieves an average 10% improvement in robustness across twelve common image transformations compared to Stable Signature, significantly outperforming existing state-of-the-art methods.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Watermarking methods have always been effective means of protecting intellectual property, yet they face significant challenges. Although existing deep learning-based watermarking systems can hide watermarks in images with minimal impact on image quality, they often lack robustness when encountering image corruptions during transmission, which undermines their practical application value. To this end, we propose a high-quality and robust watermark framework based on the diffusion model. Our method first converts the clean image into inversion noise through a null-text optimization process, and after optimizing the inversion noise in the latent space, it produces a high-quality watermarked image through an iterative denoising process of the diffusion model. The iterative denoising process serves as a powerful purification mechanism, ensuring both the visual quality of the watermarked image and enhancing the robustness of the watermark against various corruptions. To prevent the optimizing of inversion noise from distorting the original semantics of the image, we specifically introduced self-attention constraints and pseudo-mask strategies. Extensive experimental results demonstrate the superior performance of our method against various image corruptions. In particular, our method outperforms the stable signature method by an average of 10\% across 12 different image transformations on COCO datasets. Our codes are available at https://github.com/920927/ONRW.

Problem

• watermarking
• robustness
• image corruption
• diffusion model
• intellectual property protection

Innovation

• diffusion model
• inversion noise optimization
• robust watermarking
• null-text optimization
• self-attention constraints