Towards Effective User Attribution for Latent Diffusion Models via Watermark-Informed Blending
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929631954731008 |
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| author | Pan, Yongyang Liu, Xiaohong Luo, Siqi Xin, Yi Guo, Xiao Liu, Xiaoming Min, Xiongkuo Zhai, Guangtao |
| author_facet | Pan, Yongyang Liu, Xiaohong Luo, Siqi Xin, Yi Guo, Xiao Liu, Xiaoming Min, Xiongkuo Zhai, Guangtao |
| contents | Rapid advancements in multimodal large language models have enabled the creation of hyper-realistic images from textual descriptions. However, these advancements also raise significant concerns about unauthorized use, which hinders their broader distribution. Traditional watermarking methods often require complex integration or degrade image quality. To address these challenges, we introduce a novel framework Towards Effective user Attribution for latent diffusion models via Watermark-Informed Blending (TEAWIB). TEAWIB incorporates a unique ready-to-use configuration approach that allows seamless integration of user-specific watermarks into generative models. This approach ensures that each user can directly apply a pre-configured set of parameters to the model without altering the original model parameters or compromising image quality. Additionally, noise and augmentation operations are embedded at the pixel level to further secure and stabilize watermarked images. Extensive experiments validate the effectiveness of TEAWIB, showcasing the state-of-the-art performance in perceptual quality and attribution accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10958 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Effective User Attribution for Latent Diffusion Models via Watermark-Informed Blending Pan, Yongyang Liu, Xiaohong Luo, Siqi Xin, Yi Guo, Xiao Liu, Xiaoming Min, Xiongkuo Zhai, Guangtao Multimedia Cryptography and Security Computer Vision and Pattern Recognition Image and Video Processing Rapid advancements in multimodal large language models have enabled the creation of hyper-realistic images from textual descriptions. However, these advancements also raise significant concerns about unauthorized use, which hinders their broader distribution. Traditional watermarking methods often require complex integration or degrade image quality. To address these challenges, we introduce a novel framework Towards Effective user Attribution for latent diffusion models via Watermark-Informed Blending (TEAWIB). TEAWIB incorporates a unique ready-to-use configuration approach that allows seamless integration of user-specific watermarks into generative models. This approach ensures that each user can directly apply a pre-configured set of parameters to the model without altering the original model parameters or compromising image quality. Additionally, noise and augmentation operations are embedded at the pixel level to further secure and stabilize watermarked images. Extensive experiments validate the effectiveness of TEAWIB, showcasing the state-of-the-art performance in perceptual quality and attribution accuracy. |
| title | Towards Effective User Attribution for Latent Diffusion Models via Watermark-Informed Blending |
| topic | Multimedia Cryptography and Security Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2409.10958 |