Generative image modeling enables a wide range of applications but raises ethical concerns about responsible deployment. This paper introduces an active strategy combining image watermarking and Latent Diffusion Models. The goal is for all generated images to conceal an invisible watermark allowing for future detection and/or identification. The method quickly fine-tunes the latent decoder of the image generator, conditioned on a binary signature. A pre-trained watermark extractor recovers the hidden signature from any generated image and a statistical test then determines whether it comes from the generative model. We evaluate the invisibility and robustness of the watermarks on a variety of generation tasks, showing that Stable Signature works even after the images are modified. For instance, it detects the origin of an image generated from a text prompt, then cropped to keep $10\%$ of the content, with $90$+$\%$ accuracy at a false positive rate below 10$^{-6}$.

翻译：生成式图像建模支持广泛的应用，但也引发了关于负责任部署的伦理担忧。本文提出了一种结合图像水印与潜在扩散模型的主动策略。其目标是使所有生成的图像都隐藏一个不可见水印，以便未来进行检测和/或身份识别。该方法基于二进制签名对图像生成器的潜在解码器进行快速微调。预训练的水印提取器从任意生成的图像中恢复隐藏签名，随后通过统计检验确定该图像是否源自生成模型。我们在多种生成任务上评估了水印的不可见性和鲁棒性，表明即使图像经过修改，稳定签名仍能有效工作。例如，针对文本提示生成后裁剪至仅保留10%内容的图像，该方法在假阳性率低于10⁻⁶时，能以90%以上的准确率检测其来源。