DiffusionShield: A Watermark for Data Copyright Protection against Generative Diffusion Models
Yingqian Cui, Jie Ren, Xu Han, Pengfei He, Hui Liu, Lichao Sun, Yue Xing, Jiliang Tang
Abstract
Recently, Generative Diffusion Models (GDMs) have shown remarkable abilities in learning and generating images, fostering a large community of GDMs. However, the unrestricted proliferation has raised serious concerns on copyright issues. For example, artists become concerned that GDMs could effortlessly replicate their unique artworks without permission. In response to these challenges, we introduce a novel watermark scheme, Diffusion Shield, against GDMs. It protects images from infringement by encoding the ownership message into an imperceptible watermark and injecting it into images. This watermark can be easily learned by GDMs and will be reproduced in generated images. By detecting the watermark in generated images, the infringement can be exposed with evidence. Benefiting from the uniformity of the watermarks and the joint optimization method, Diffusion Shield ensures low distortion of the original image, high watermark detection performance, and lengthy encoded messages. We conduct rigorous and comprehensive experiments to show its effectiveness in defending against infringement by GDMs and its superiority over traditional watermark methods.