Can I read Generative Model Watermarking Suppressing High-Frequency Artifacts on EtoBox?

Generative Model Watermarking Suppressing High-Frequency Artifacts by Zhang, Li; Liu, Yong; Zhang, Xinpeng; Wu, Hanzhou is a scholarly article available to read on EtoBox.

What is Generative Model Watermarking Suppressing High-Frequency Artifacts about?

Protecting deep neural networks (DNNs) against intellectual property (IP) infringement has attracted an increasing attention in recent years. Recent advances focus on IP protection of generative models, which embed the watermark information into the image generated by the model to be protected. Although the generated marked image has good visual quality, it introduces noticeable artifacts to the marked image in high-frequency area, which severely impairs the imperceptibility of the watermark and thereby reduces the security of the watermarking system. To deal with this problem, in this paper, we propose a novel framework for generative model watermarking that can suppress those high-frequency artifacts. The main idea of the proposed framework is to design a new watermark embedding network that can suppress high-frequency artifacts by applying anti-aliasing. To realize anti-aliasing, we use low-pass filtering for the internal sampling layers of the new watermark embedding network. Meanwhile, joint loss optimization and adversarial training are applied to enhance the effectiveness and robustness. Experimental results indicate that the marked model not only maintains the performance v

Author

Zhang, Li; Liu, Yong; Zhang, Xinpeng; Wu, Hanzhou

Published

2023

Language

EN