PiGW: A Plug-in Generative Watermarking Framework
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910374625804288 |
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| author | Ma, Rui Guo, Mengxi Yuming, Li Zhang, Hengyuan Ma, Cong Li, Yuan Xie, Xiaodong Zhang, Shanghang |
| author_facet | Ma, Rui Guo, Mengxi Yuming, Li Zhang, Hengyuan Ma, Cong Li, Yuan Xie, Xiaodong Zhang, Shanghang |
| contents | Integrating watermarks into generative images is a critical strategy for protecting intellectual property and enhancing artificial intelligence security. This paper proposes Plug-in Generative Watermarking (PiGW) as a general framework for integrating watermarks into generative images. More specifically, PiGW embeds watermark information into the initial noise using a learnable watermark embedding network and an adaptive frequency spectrum mask. Furthermore, it optimizes training costs by gradually increasing timesteps. Extensive experiments demonstrate that PiGW enables embedding watermarks into the generated image with negligible quality loss while achieving true invisibility and high resistance to noise attacks. Moreover, PiGW can serve as a plugin for various commonly used generative structures and multimodal generative content types. Finally, we demonstrate how PiGW can also be utilized for detecting generated images, contributing to the promotion of secure AI development. The project code will be made available on GitHub. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12053 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PiGW: A Plug-in Generative Watermarking Framework Ma, Rui Guo, Mengxi Yuming, Li Zhang, Hengyuan Ma, Cong Li, Yuan Xie, Xiaodong Zhang, Shanghang Multimedia Integrating watermarks into generative images is a critical strategy for protecting intellectual property and enhancing artificial intelligence security. This paper proposes Plug-in Generative Watermarking (PiGW) as a general framework for integrating watermarks into generative images. More specifically, PiGW embeds watermark information into the initial noise using a learnable watermark embedding network and an adaptive frequency spectrum mask. Furthermore, it optimizes training costs by gradually increasing timesteps. Extensive experiments demonstrate that PiGW enables embedding watermarks into the generated image with negligible quality loss while achieving true invisibility and high resistance to noise attacks. Moreover, PiGW can serve as a plugin for various commonly used generative structures and multimodal generative content types. Finally, we demonstrate how PiGW can also be utilized for detecting generated images, contributing to the promotion of secure AI development. The project code will be made available on GitHub. |
| title | PiGW: A Plug-in Generative Watermarking Framework |
| topic | Multimedia |
| url | https://arxiv.org/abs/2403.12053 |