Integrating watermarks into generative images is a critical strategy for protecting intellectual property and enhancing artificial intelligence security. This paper proposes Plug-in Generative Watermarking (PiGW) as a general framework for integrating watermarks into generative images. More specifically, PiGW embeds watermark information into the initial noise using a learnable watermark embedding network and an adaptive frequency spectrum mask. Furthermore, it optimizes training costs by gradually increasing timesteps. Extensive experiments demonstrate that PiGW enables embedding watermarks into the generated image with negligible quality loss while achieving true invisibility and high resistance to noise attacks. Moreover, PiGW can serve as a plugin for various commonly used generative structures and multimodal generative content types. Finally, we demonstrate how PiGW can also be utilized for detecting generated images, contributing to the promotion of secure AI development. The project code will be made available on GitHub.

翻译：将水印嵌入生成图像是保护知识产权和增强人工智能安全的关键策略。本文提出即插即用生成式水印（PiGW）作为一种通用框架，用于将水印信息嵌入生成图像。具体而言，PiGW通过可学习的水印嵌入网络和自适应频谱掩码将水印信息嵌入初始噪声中，并采用逐步增加时间步长的方式优化训练成本。大量实验表明，PiGW能在生成图像中嵌入水印，且质量损失可忽略不计，同时实现真正不可见性及对噪声攻击的高抗性。此外，PiGW可作为插件适配多种常见生成结构及多模态生成内容类型。最后，我们展示了PiGW还可用于检测生成图像，从而推动安全人工智能发展。项目代码将发布于GitHub。