The Stable Signature: Rooting Watermarks in Latent Diffusion Models

September 30, 2023

Abstract

Generative image modeling enables a wide range of applications but raises ethical concerns about responsible deployment. We introduce an active content tracing method 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 the Stable Signature is robust to image modifications. For instance, it detects the origin of an image generated from a text prompt, then cropped to keep 10% of the content, with more than 90% accuracy at a false positive rate below 1E-6. Code is available at: https://github.com/facebookresearch/stable_signature

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AUTHORS

Written by

Pierre Fernandez

Guillaume Couairon

Herve Jegou

Matthijs Douze

Teddy Furon

Publisher

ICCV

Research Topics

Computer Vision

Integrity

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