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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| Online Access: | https://arxiv.org/abs/2405.17472 |
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| _version_ | 1866915036111306752 |
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| author | Huang, Kai Wang, Haoming Gao, Wei |
| author_facet | Huang, Kai Wang, Haoming Gao, Wei |
| contents | Text-to-image diffusion models can be fine-tuned in custom domains to adapt to specific user preferences, but such adaptability has also been utilized for illegal purposes, such as forging public figures' portraits, duplicating copyrighted artworks and generating explicit contents. Existing work focused on detecting the illegally generated contents, but cannot prevent or mitigate illegal adaptations of diffusion models. Other schemes of model unlearning and reinitialization, similarly, cannot prevent users from relearning the knowledge of illegal model adaptation with custom data. In this paper, we present FreezeAsGuard, a new technique that addresses these limitations and enables irreversible mitigation of illegal adaptations of diffusion models. Our approach is that the model publisher selectively freezes tensors in pre-trained diffusion models that are critical to illegal model adaptations, to mitigate the fine-tuned model's representation power in illegal adaptations, but minimize the impact on other legal adaptations. Experiment results in multiple text-to-image application domains show that FreezeAsGuard provides 37% stronger power in mitigating illegal model adaptations compared to competitive baselines, while incurring less than 5% impact on legal model adaptations. The source code is available at: https://github.com/pittisl/FreezeAsGuard. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17472 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FreezeAsGuard: Mitigating Illegal Adaptation of Diffusion Models via Selective Tensor Freezing Huang, Kai Wang, Haoming Gao, Wei Machine Learning Artificial Intelligence Cryptography and Security Computer Vision and Pattern Recognition Text-to-image diffusion models can be fine-tuned in custom domains to adapt to specific user preferences, but such adaptability has also been utilized for illegal purposes, such as forging public figures' portraits, duplicating copyrighted artworks and generating explicit contents. Existing work focused on detecting the illegally generated contents, but cannot prevent or mitigate illegal adaptations of diffusion models. Other schemes of model unlearning and reinitialization, similarly, cannot prevent users from relearning the knowledge of illegal model adaptation with custom data. In this paper, we present FreezeAsGuard, a new technique that addresses these limitations and enables irreversible mitigation of illegal adaptations of diffusion models. Our approach is that the model publisher selectively freezes tensors in pre-trained diffusion models that are critical to illegal model adaptations, to mitigate the fine-tuned model's representation power in illegal adaptations, but minimize the impact on other legal adaptations. Experiment results in multiple text-to-image application domains show that FreezeAsGuard provides 37% stronger power in mitigating illegal model adaptations compared to competitive baselines, while incurring less than 5% impact on legal model adaptations. The source code is available at: https://github.com/pittisl/FreezeAsGuard. |
| title | FreezeAsGuard: Mitigating Illegal Adaptation of Diffusion Models via Selective Tensor Freezing |
| topic | Machine Learning Artificial Intelligence Cryptography and Security Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.17472 |