  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$^{-6}$.
