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
–arXiv.org 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%.
arXiv.org Artificial Intelligence
Nov-25-2024
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- Research Report (1.00)
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- Government > Military (0.48)
- Information Technology > Security & Privacy (0.46)
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