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Watermarking the outputs of generative models is a crucial technique fortracing copyright and preventing potential harm from AI-generated content. Inthis paper, we introduce a novel technique called Tree-Ring Watermarking thatrobustly fingerprints diffusion model outputs. Unlike existing methods thatperform post-hoc modifications to images after sampling, Tree-Ring Watermarkingsubtly influences the entire sampling process, resulting in a model fingerprintthat is invisible to humans. The watermark embeds a pattern into the initialnoise vector used for sampling. These patterns are structured in Fourier spaceso that they are invariant to convolutions, crops, dilations, flips, androtations. After image generation, the watermark signal is detected byinverting the diffusion process to retrieve the noise vector, which is thenchecked for the embedded signal. We demonstrate that this technique can beeasily applied to arbitrary diffusion models, including text-conditioned StableDiffusion, as a plug-in with negligible loss in FID. Our watermark issemantically hidden in the image space and is far more robust than watermarkingalternatives that are currently deployed. Code is available athttps://github.com/YuxinWenRick/tree-ring-watermark.

Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust