MaXsive: High-Capacity and Robust Training-Free Generative Image Watermarking in Diffusion Models
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| Format: | Preprint |
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2025
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| _version_ | 1866908470321610752 |
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| author | Mao, Po-Yuan Tsai, Cheng-Chang Lu, Chun-Shien |
| author_facet | Mao, Po-Yuan Tsai, Cheng-Chang Lu, Chun-Shien |
| contents | The great success of the diffusion model in image synthesis led to the release of gigantic commercial models, raising the issue of copyright protection and inappropriate content generation. Training-free diffusion watermarking provides a low-cost solution for these issues. However, the prior works remain vulnerable to rotation, scaling, and translation (RST) attacks. Although some methods employ meticulously designed patterns to mitigate this issue, they often reduce watermark capacity, which can result in identity (ID) collusion. To address these problems, we propose MaXsive, a training-free diffusion model generative watermarking technique that has high capacity and robustness. MaXsive best utilizes the initial noise to watermark the diffusion model. Moreover, instead of using a meticulously repetitive ring pattern, we propose injecting the X-shape template to recover the RST distortions. This design significantly increases robustness without losing any capacity, making ID collusion less likely to happen. The effectiveness of MaXsive has been verified on two well-known watermarking benchmarks under the scenarios of verification and identification. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_21195 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MaXsive: High-Capacity and Robust Training-Free Generative Image Watermarking in Diffusion Models Mao, Po-Yuan Tsai, Cheng-Chang Lu, Chun-Shien Cryptography and Security Artificial Intelligence Multimedia The great success of the diffusion model in image synthesis led to the release of gigantic commercial models, raising the issue of copyright protection and inappropriate content generation. Training-free diffusion watermarking provides a low-cost solution for these issues. However, the prior works remain vulnerable to rotation, scaling, and translation (RST) attacks. Although some methods employ meticulously designed patterns to mitigate this issue, they often reduce watermark capacity, which can result in identity (ID) collusion. To address these problems, we propose MaXsive, a training-free diffusion model generative watermarking technique that has high capacity and robustness. MaXsive best utilizes the initial noise to watermark the diffusion model. Moreover, instead of using a meticulously repetitive ring pattern, we propose injecting the X-shape template to recover the RST distortions. This design significantly increases robustness without losing any capacity, making ID collusion less likely to happen. The effectiveness of MaXsive has been verified on two well-known watermarking benchmarks under the scenarios of verification and identification. |
| title | MaXsive: High-Capacity and Robust Training-Free Generative Image Watermarking in Diffusion Models |
| topic | Cryptography and Security Artificial Intelligence Multimedia |
| url | https://arxiv.org/abs/2507.21195 |