@inproceedings{
guo2024freqmark,
title={FreqMark: Invisible Image Watermarking via Frequency Based Optimization in Latent Space},
author={YiYang Guo and Ruizhe Li and Mude Hui and Hanzhong Allan Guo and Chen Zhang and Chuangjian Cai and Le Wan and Shangfei Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=01s5ODIHKd}
}
Invisible watermarking is essential for safeguarding digital content, enabling copyright protection and content authentication. 
However, existing watermarking methods fall short in robustness against regeneration attacks.
In this paper, we propose a novel method called FreqMark that involves unconstrained optimization of the image latent frequency space obtained after VAE encoding. Specifically, FreqMark embeds the watermark by optimizing the latent frequency space of the images and then extracts the watermark through a pre-trained image encoder. This optimization allows a flexible trade-off between image quality with watermark robustness and effectively resists regeneration attacks.
Experimental results demonstrate that FreqMark offers significant advantages in image quality and robustness, permits flexible selection of the encoding bit number, and achieves a bit accuracy exceeding 90\% when encoding a 48-bit hidden message under various attack scenarios.
FreqMark: Invisible Image Watermarking via Frequency Based Optimization in Latent Space
YiYang Guo Ruizhe Li Mude Hui Hanzhong Allan Guo Chen Zhang Chuangjian Cai Le Wan Shangfei Wang