RAW: A Robust and Agile Plug-and-Play Watermark Framework for
  AI-Generated Images with Provable Guarantees
Xun Xian Ganghua Wang Xuan Bi Jayanth Srinivasa Ashish Kundu Mingyi Hong Jie Ding
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.