GaussMarker: Robust Dual-Domain Watermark for Diffusion Models

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.

@article{li2025_2506.11444,
  title={ GaussMarker: Robust Dual-Domain Watermark for Diffusion Models },
  author={ Kecen Li and Zhicong Huang and Xinwen Hou and Cheng Hong },
  journal={arXiv preprint arXiv:2506.11444},
  year={ 2025 }
}