Generative Diffusion Models (GDMs) have showcased their remarkable capabilities in image learning and generation. Yet, their unrestrained use raised concerns about copyright protection, especially among artists, as it can replicate unique creative works without authorization. To address the challenges, we propose a watermark scheme, DiffusionShield, against GDMs. It protects images against infringement by encoding ownership information into an imperceptible watermark injected to the images. The watermark can be easily learned by GDMs and reproduced in their generated images. By detecting the watermark in generated data, infringement can be exposed with evidence. Benefiting from the uniformity of the watermarks and the joint optimization method, DiffusionShield ensures low distortion of the image, high detection accuracy, and the ability to embed lengthy messages. Experiments has validated DiffusionShield’s efficacy in defending against GDMs infringements and its superiority over conventional watermarking techniques.