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

• URL: http://arxiv.org/abs/2601.17388v1
• Date: Sat, 24 Jan 2026 09:22:29 GMT
• Title: ONRW: Optimizing inversion noise for high-quality and robust watermark
• Authors: Xuan Ding, Xiu Yan, Chuanlong Xie, Yao Zhu,
• Abstract summary: We propose a high-quality and robust watermark framework based on the diffusion model.<n>We show that our method outperforms the stable signature method by an average of 10% across 12 different image transformations on COCO datasets.
• Score: 12.520973746455693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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