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

TL;DR

This paper introduces ONRW, a diffusion model-based watermarking framework that enhances image quality and robustness against corruptions by optimizing inversion noise with self-attention and pseudo-mask strategies.

Contribution

The paper proposes a novel diffusion model-based watermarking method that improves robustness and image quality through inversion noise optimization and semantic preservation techniques.

Findings

Outperforms stable signature method by 10% on COCO datasets

Achieves high robustness against various image corruptions

Maintains high visual quality of watermarked images

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.