Watermarking is an important mechanism for provenance and copyright protection of diffusion-generated images. Training-free methods, exemplified by Gaussian Shading, embed watermarks into the initial noise of diffusion models with negligible impact on the quality of generated images. However, extracting this type of watermark typically requires multi-step diffusion inversion to obtain precise initial noise, which is computationally expensive and time-consuming. To address this issue, we propose One-step Inversion (OSI), a significantly faster and more accurate method for extracting Gaussian Shading style watermarks. OSI reformulates watermark extraction as a learnable sign classification problem, which eliminates the need for precise regression of the initial noise. Then, we initialize the OSI model from the diffusion backbone and finetune it on synthesized noise-image pairs with a sign classification objective. In this manner, the OSI model is able to accomplish the watermark extraction efficiently in only one step. Our OSI substantially outperforms the multi-step diffusion inversion method: it is 20x faster, achieves higher extraction accuracy, and doubles the watermark payload capacity. Extensive experiments across diverse schedulers, diffusion backbones, and cryptographic schemes consistently show improvements, demonstrating the generality of our OSI framework.

翻译：水印技术是扩散生成图像溯源与版权保护的重要机制。以高斯着色为代表的免训练方法将水印嵌入扩散模型的初始噪声中，对生成图像质量的影响可忽略不计。然而，提取此类水印通常需要多步扩散逆变换以获得精确的初始噪声，计算成本高且耗时。为解决该问题，我们提出单步逆变换（OSI）——一种显著更快且更精确的高斯着色风格水印提取方法。OSI将水印提取重新定义为可学习的符号分类问题，从而避免了对初始噪声的精确回归需求。随后，我们从扩散主干网络初始化OSI模型，并在合成的噪声-图像对上进行符号分类目标的微调。通过这种方式，OSI模型能够仅通过单步计算高效完成水印提取。我们的OSI方法显著优于多步扩散逆变换方法：提取速度提升20倍，提取准确率更高，并将水印有效载荷容量提升一倍。在不同调度器、扩散主干网络和密码学方案上的大量实验均显示出稳定改进，证明了OSI框架的普适性。