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%.
@article{arxiv.2412.00064,
title = {DiffGuard: Text-Based Safety Checker for Diffusion Models},
author = {Massine El Khader and Elias Al Bouzidi and Abdellah Oumida and Mohammed Sbaihi and Eliott Binard and Jean-Philippe Poli and Wassila Ouerdane and Boussad Addad and Katarzyna Kapusta},
journal= {arXiv preprint arXiv:2412.00064},
year = {2025}
}