  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.
