  Recently, Generative Diffusion Models (GDMs) have showcased their remarkable
capabilities in learning and generating images. A large community of GDMs has
naturally emerged, further promoting the diversified applications of GDMs in
various fields. However, this unrestricted proliferation has raised serious
concerns about copyright protection. For example, artists including painters
and photographers are becoming increasingly concerned that GDMs could
effortlessly replicate their unique creative works without authorization. In
response to these challenges, we introduce a novel watermarking scheme,
DiffusionShield, tailored for GDMs. DiffusionShield protects images from
copyright infringement by GDMs through encoding the ownership information into
an imperceptible watermark and injecting it into the images. Its watermark can
be easily learned by GDMs and will be reproduced in their generated images. By
detecting the watermark from generated images, copyright infringement can be
exposed with evidence. Benefiting from the uniformity of the watermarks and the
joint optimization method, DiffusionShield ensures low distortion of the
original image, high watermark detection performance, and the ability to embed
lengthy messages. We conduct rigorous and comprehensive experiments to show the
effectiveness of DiffusionShield in defending against infringement by GDMs and
its superiority over traditional watermarking methods. The code for
DiffusionShield is accessible in
https://github.com/Yingqiancui/DiffusionShield.
