为扩散模型设计双域水印，提升抗攻击鲁棒性。
GaussMarker: Robust Dual-Domain Watermark for Diffusion Models
- 在空间与频率双域嵌入水印，提升稳定性。
- 在八种图像失真和四种高级攻击下表现最优。
- 适合需要高可靠水印的图像生成应用。
随着扩散模型生成的图像日益逼真，版权与滥用问题日益突出。水印技术成为有前景的解决方案。现有方法仅在生成初始高斯噪声单一域嵌入水印，鲁棒性不足。本文首次提出基于流水线注入器的双域水印方法，将水印同时嵌入空间域与频率域。为进一步增强对图像操作和高级攻击的鲁棒性，引入与模型无关的可学习高斯噪声恢复器（GNR），用于修复被篡改图像中提取的高斯噪声，并融合双域水印检测得分以提升识别能力。GaussMarker 在三种 Stable Diffusion 版本上，于八种图像失真和四种高级攻击下均达到当前最佳性能，具有更高的召回率与更低的误报率，更符合实际应用需求。
原文摘要 · Abstract (English)
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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