The Stable Signature: Rooting Watermarks in Latent Diffusion Models

Generative image modeling enables a wide range of applications but raises ethical concerns about responsible deployment. This paper introduces an active strategy combining image watermarking and Latent Diffusion Models.

The goal is for all generated images to conceal an invisible watermark allowing for future detection and/or identification. The method quickly fine-tunes the latent decoder of the image generator, conditioned on a binary signature.

A pre-trained watermark extractor recovers the hidden signature from any generated image and a statistical test then determines whether it comes from the generative model. We evaluate the invisibility and robustness of the watermarks on a variety of generation tasks, showing that Stable Signature works even after the images are modified.

For instance, it detects the origin of an image generated from a text prompt, then cropped to keep $10%$ of the content, with $90$+$%$ accuracy at a false positive rate below 10$^{-6}$.