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.

翻译：近期，生成扩散模型在图像学习与生成方面展现出卓越能力，催生了大量衍生应用，进一步推动了其多领域多元化发展。然而，这种无约束的扩散也引发了严重的版权保护问题。例如，画师、摄影师等艺术创作者日益担忧其原创作品可能被生成扩散模型未经授权轻易复制。针对这一挑战，我们提出了一种面向生成扩散模型的新型水印方案——扩散盾。该方案通过将所有权信息编码为不可见水印并注入图像，从而保护图像免遭生成扩散模型的侵权复制。这种水印能被生成扩散模型轻松学习，并在其生成图像中复现。通过从生成图像中检测水印，可基于证据揭露侵权行为。得益于水印的均匀性及联合优化方法，扩散盾在保证原图像低失真、高水印检测性能的同时，还能嵌入长消息。我们开展了严格全面的实验，验证了扩散盾抵御生成扩散模型侵权的有效性，以及其相较于传统水印方法的优越性。扩散盾代码已开源至https://github.com/Yingqiancui/DiffusionShield。