defense arXiv Aug 5, 2025 · Aug 2025
Fan Yang, Yihao Huang, Jiayi Zhu et al. · Huazhong University of Science and Technology · National University of Singapore +2 more
Defends diffusion T2I models against NSFW generation by classifying predicted noise mid-generation, robust to adversarial prompts
Output Integrity Attack visiongenerative
Diffusion-based text-to-image (T2I) models enable high-quality image generation but also pose significant risks of misuse, particularly in producing not-safe-for-work (NSFW) content. While prior detection methods have focused on filtering prompts before generation or moderating images afterward, the in-generation phase of diffusion models remains largely unexplored for NSFW detection. In this paper, we introduce In-Generation Detection (IGD), a simple yet effective approach that leverages the predicted noise during the diffusion process as an internal signal to identify NSFW content. This approach is motivated by preliminary findings suggesting that the predicted noise may capture semantic cues that differentiate NSFW from benign prompts, even when the prompts are adversarially crafted. Experiments conducted on seven NSFW categories show that IGD achieves an average detection accuracy of 91.32% over naive and adversarial NSFW prompts, outperforming seven baseline methods.
diffusion Huazhong University of Science and Technology · National University of Singapore · East China Normal University +1 more
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