Diffusion models have made substantial advances in recent years, enabling high-quality image synthesis; however, the widespread dissemination and reuse of their outputs have introduced new challenges in intellectual property protection and content provenance. Image watermarking offers a solution to these challenges, and recent work has increasingly explored Noise-as-Watermark (NaW) approaches that integrate watermarking directly into the diffusion process. However, existing NaW methods fail to balance robustness and diversity. We attribute this weakness to value encoding, which encodes watermark bits into individual sampled values. It is extremely fragile in practical application scenarios. To address this, we encode watermark bits into the structured noise pattern, so that the watermark is preserved even when individual values are perturbed. To further ensure generation diversity, we introduce a dedicated randomization design that reshuffles the positions of noise elements without changing their values, preventing the watermark from inducing fixed noise patterns or spatial locations. Extensive experiments demonstrate that our method achieves state-of-the-art robustness while maintaining high generation quality across a wide range of lossy scenarios.

翻译：近年来，扩散模型取得了显著进展，实现了高质量的图像合成；然而，其生成内容的广泛传播与再利用为知识产权保护和内容溯源带来了新的挑战。图像水印技术为这些挑战提供了解决方案，近期研究日益关注将水印直接集成到扩散过程中的噪声即水印（NaW）方法。然而，现有的NaW方法难以在鲁棒性与多样性之间取得平衡。我们将此不足归因于值编码机制——该机制将水印比特编码至独立采样值中，在实际应用场景中极为脆弱。为解决这一问题，我们将水印比特编码至结构化噪声模式中，使得即使单个数值受到扰动，水印信息仍能得以保留。为进一步确保生成多样性，我们引入了专用的随机化设计，通过对噪声元素的位置进行重排（保持数值不变），防止水印导致固定的噪声模式或空间定位。大量实验表明，我们的方法在多种有损场景下均能保持优异的生成质量，同时实现了当前最优的鲁棒性。