GaussMarker: Robust Dual-Domain Watermark for Diffusion Models

TL;DR

GaussMarker introduces a dual-domain watermarking method for diffusion models, embedding watermarks in both spatial and frequency domains, significantly improving robustness against manipulations and attacks.

Contribution

The paper proposes the first dual-domain watermarking approach with a pipelined injector and a learnable Gaussian Noise Restorer, enhancing robustness and detection accuracy in diffusion models.

Findings

Achieves state-of-the-art robustness under various distortions and attacks.

Improves recall and reduces false positives in watermark detection.

Demonstrates effectiveness across multiple Stable Diffusion versions.

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.