Comprehensive Evaluation and Analysis for NSFW Concept Erasure in Text-to-Image Diffusion Models
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915570877726720 |
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| author | Chen, Die Li, Zhiwen Chen, Cen Xie, Yuexiang Li, Xiaodan Ye, Jinyan Chen, Yingda Li, Yaliang |
| author_facet | Chen, Die Li, Zhiwen Chen, Cen Xie, Yuexiang Li, Xiaodan Ye, Jinyan Chen, Yingda Li, Yaliang |
| contents | Text-to-image diffusion models have gained widespread application across various domains, demonstrating remarkable creative potential. However, the strong generalization capabilities of diffusion models can inadvertently lead to the generation of not-safe-for-work (NSFW) content, posing significant risks to their safe deployment. While several concept erasure methods have been proposed to mitigate the issue associated with NSFW content, a comprehensive evaluation of their effectiveness across various scenarios remains absent. To bridge this gap, we introduce a full-pipeline toolkit specifically designed for concept erasure and conduct the first systematic study of NSFW concept erasure methods. By examining the interplay between the underlying mechanisms and empirical observations, we provide in-depth insights and practical guidance for the effective application of concept erasure methods in various real-world scenarios, with the aim of advancing the understanding of content safety in diffusion models and establishing a solid foundation for future research and development in this critical area. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_15450 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comprehensive Evaluation and Analysis for NSFW Concept Erasure in Text-to-Image Diffusion Models Chen, Die Li, Zhiwen Chen, Cen Xie, Yuexiang Li, Xiaodan Ye, Jinyan Chen, Yingda Li, Yaliang Computer Vision and Pattern Recognition Text-to-image diffusion models have gained widespread application across various domains, demonstrating remarkable creative potential. However, the strong generalization capabilities of diffusion models can inadvertently lead to the generation of not-safe-for-work (NSFW) content, posing significant risks to their safe deployment. While several concept erasure methods have been proposed to mitigate the issue associated with NSFW content, a comprehensive evaluation of their effectiveness across various scenarios remains absent. To bridge this gap, we introduce a full-pipeline toolkit specifically designed for concept erasure and conduct the first systematic study of NSFW concept erasure methods. By examining the interplay between the underlying mechanisms and empirical observations, we provide in-depth insights and practical guidance for the effective application of concept erasure methods in various real-world scenarios, with the aim of advancing the understanding of content safety in diffusion models and establishing a solid foundation for future research and development in this critical area. |
| title | Comprehensive Evaluation and Analysis for NSFW Concept Erasure in Text-to-Image Diffusion Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.15450 |