Safeguarding intellectual property and preventing potential misuse of
AI-generated images are of paramount importance. This paper introduces a robust
and agile plug-and-play watermark detection framework, dubbed as RAW. As a
departure from traditional encoder-decoder methods, which incorporate fixed
binary codes as watermarks within latent representations, our approach
introduces learnable watermarks directly into the original image data.
Subsequently, we employ a classifier that is jointly trained with the watermark
to detect the presence of the watermark. The proposed framework is compatible
with various generative architectures and supports on-the-fly watermark
injection after training. By incorporating state-of-the-art smoothing
techniques, we show that the framework provides provable guarantees regarding
the false positive rate for misclassifying a watermarked image, even in the
presence of certain adversarial attacks targeting watermark removal.
Experiments on a diverse range of images generated by state-of-the-art
diffusion models reveal substantial performance enhancements compared to
existing approaches. For instance, our method demonstrates a notable increase
in AUROC, from 0.48 to 0.82, when compared to state-of-the-art approaches in
detecting watermarked images under adversarial attacks, while maintaining image
quality, as indicated by closely aligned FID and CLIP scores.

本文介绍了一个名为 RAW 的稳健而灵活的即插即用水印检测框架，它将可学习的水印直接引入原始图像数据中，并使用与水印共同训练的分类器来检测水印的存在。此框架在各种生成架构上可兼容，并支持训练后即时进行水印注入，通过整合最先进的平滑技术，不仅在水印图像误分类的假阳性率方面提供可证明的保证，而且在存在针对水印去除的某些对抗性攻击时也表现出显著的性能增强。在由最先进的扩散模型生成的各种图像上的实验证明与现有方法相比，我们的方法在检测带有对抗性攻击的水印图像时，AUROC 从 0.48 增加到 0.82，同时保持图像质量，表现为接近的 FID 和 CLIP 得分。