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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.00064 |
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| _version_ | 1866912237177798656 |
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| author | Khader, Massine El Bouzidi, Elias Al Oumida, Abdellah Sbaihi, Mohammed Binard, Eliott Poli, Jean-Philippe Ouerdane, Wassila Addad, Boussad Kapusta, Katarzyna |
| author_facet | Khader, Massine El Bouzidi, Elias Al Oumida, Abdellah Sbaihi, Mohammed Binard, Eliott Poli, Jean-Philippe Ouerdane, Wassila Addad, Boussad Kapusta, Katarzyna |
| contents | Recent advances in Diffusion Models have enabled the generation of images from text, with powerful closed-source models like DALL-E and Midjourney leading the way. However, open-source alternatives, such as StabilityAI's Stable Diffusion, offer comparable capabilities. These open-source models, hosted on Hugging Face, come equipped with ethical filter protections designed to prevent the generation of explicit images. This paper reveals first their limitations and then presents a novel text-based safety filter that outperforms existing solutions. Our research is driven by the critical need to address the misuse of AI-generated content, especially in the context of information warfare. DiffGuard enhances filtering efficacy, achieving a performance that surpasses the best existing filters by over 14%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00064 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DiffGuard: Text-Based Safety Checker for Diffusion Models Khader, Massine El Bouzidi, Elias Al Oumida, Abdellah Sbaihi, Mohammed Binard, Eliott Poli, Jean-Philippe Ouerdane, Wassila Addad, Boussad Kapusta, Katarzyna Computer Vision and Pattern Recognition Artificial Intelligence Recent advances in Diffusion Models have enabled the generation of images from text, with powerful closed-source models like DALL-E and Midjourney leading the way. However, open-source alternatives, such as StabilityAI's Stable Diffusion, offer comparable capabilities. These open-source models, hosted on Hugging Face, come equipped with ethical filter protections designed to prevent the generation of explicit images. This paper reveals first their limitations and then presents a novel text-based safety filter that outperforms existing solutions. Our research is driven by the critical need to address the misuse of AI-generated content, especially in the context of information warfare. DiffGuard enhances filtering efficacy, achieving a performance that surpasses the best existing filters by over 14%. |
| title | DiffGuard: Text-Based Safety Checker for Diffusion Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2412.00064 |