RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees
<|reference_start|>RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees: 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.<|reference_end|>
@article{xian2024raw:,
    title={RAW: A Robust and Agile Plug-and-Play Watermark Framework for
  AI-Generated Images with Provable Guarantees},
    author={Xun Xian, Ganghua Wang, Xuan Bi, Jayanth Srinivasa, Ashish Kundu,
  Mingyi Hong, and Jie Ding},
    journal={arXiv preprint arXiv:2403.18774},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.18774},
    primaryClass={cs.CV cs.CR cs.LG}
}