GaussMarker: Robust Dual-Domain Watermark for Diffusion Models

• URL: http://arxiv.org/abs/2506.11444v1
• Date: Fri, 13 Jun 2025 03:45:15 GMT
• Title: GaussMarker: Robust Dual-Domain Watermark for Diffusion Models
• Authors: Kecen Li, Zhicong Huang, Xinwen Hou, Cheng Hong,
• Abstract summary: GaussMarker efficiently achieves state-of-the-art performance under eight image distortions and four advanced attacks across three versions of Stable Diffusion.<n>This paper presents the first dual-domain DM watermarking approach using a pipelined injector to consistently embed watermarks in both the spatial and frequency domains.
• Score: 9.403937469402871
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As Diffusion Models (DM) generate increasingly realistic images, related issues such as copyright and misuse have become a growing concern. Watermarking is one of the promising solutions. Existing methods inject the watermark into the single-domain of initial Gaussian noise for generation, which suffers from unsatisfactory robustness. This paper presents the first dual-domain DM watermarking approach using a pipelined injector to consistently embed watermarks in both the spatial and frequency domains. To further boost robustness against certain image manipulations and advanced attacks, we introduce a model-independent learnable Gaussian Noise Restorer (GNR) to refine Gaussian noise extracted from manipulated images and enhance detection robustness by integrating the detection scores of both watermarks. GaussMarker efficiently achieves state-of-the-art performance under eight image distortions and four advanced attacks across three versions of Stable Diffusion with better recall and lower false positive rates, as preferred in real applications.

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