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.

翻译：近年来，生成扩散模型（GDMs）在学习与生成图像方面展现出卓越能力。大量GDMs应用群落自然涌现，进一步推动了其在各领域的多元化应用。然而，这种无限制的扩散引发了严重的版权保护问题。例如，包括画家和摄影师在内的艺术家日益担忧，GDMs可能未经授权轻易复制其独特创意作品。为应对这些挑战，我们提出一种适用于GDMs的新型水印方案——DiffusionShield。该方案通过将所有权信息编码为不可感知的水印并注入图像，从而保护图像免受GDMs的版权侵害。其水印可被GDMs轻松学习并复现于生成图像中。通过检测生成图像中的水印，可凭证据揭露侵权行为。得益于水印的均匀性及联合优化方法，DiffusionShield确保了原始图像的低失真、高水印检测性能及长消息嵌入能力。我们通过严谨全面的实验证明，DiffusionShield在防御GDMs侵权方面效能显著，并优于传统水印方法。