| Autor: |
Khader, Massine El, Bouzidi, Elias Al, Oumida, Abdellah, Sbaihi, Mohammed, Binard, Eliott, Poli, Jean-Philippe, Ouerdane, Wassila, Addad, Boussad, Kapusta, Katarzyna |
| Rok vydání: |
2024 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
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%. |
| Databáze: |
arXiv |
| Externí odkaz: |
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