Watermarking the outputs of generative models is a crucial technique for tracing copyright and preventing potential harm from AI-generated content. In this paper, we introduce a novel technique called Tree-Ring Watermarking that robustly fingerprints diffusion model outputs. Unlike existing methods that perform post-hoc modifications to images after sampling, Tree-Ring Watermarking subtly influences the entire sampling process, resulting in a model fingerprint that is invisible to humans. The watermark embeds a pattern into the initial noise vector used for sampling. These patterns are structured in Fourier space so that they are invariant to convolutions, crops, dilations, flips, and rotations. After image generation, the watermark signal is detected by inverting the diffusion process to retrieve the noise vector, which is then checked for the embedded signal. We demonstrate that this technique can be easily applied to arbitrary diffusion models, including text-conditioned Stable Diffusion, as a plug-in with negligible loss in FID. Our watermark is semantically hidden in the image space and is far more robust than watermarking alternatives that are currently deployed. Code is available at https://github.com/YuxinWenRick/tree-ring-watermark.

翻译：生成模型输出的水印技术对于追溯版权和防止AI生成内容潜在危害至关重要。本文提出一种称为树轮水印（Tree-Ring Watermarking）的新技术，可鲁棒地标记扩散模型输出。与现有方法在采样后对图像进行事后修改不同，树轮水印巧妙影响整个采样过程，产生对人类不可见的模型指纹。该水印将特定模式嵌入用于采样的初始噪声向量中。这些模式在傅里叶空间中经过结构化设计，使其对卷积、裁剪、缩放、翻转和旋转具有不变性。图像生成后，通过逆扩散过程恢复噪声向量来检测水印信号，进而验证嵌入模式。我们证明该技术可轻松作为插件应用于任意扩散模型（包括文本条件控制的Stable Diffusion），且FID损失可忽略不计。该水印在图像空间中具有语义隐蔽性，且比当前部署的替代水印方案鲁棒性强得多。代码已开源在https://github.com/YuxinWenRick/tree-ring-watermark。