USD: NSFW Content Detection for Text-to-Image Models via Scene Graph
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Presented at USENIX Security 2025 by
In recent years, Text-to-Image (T2I) techniques have achieved remarkable success in synthesizing high-quality visual content. However, this advancement has raised significant societal concerns regarding the potential security risks, particularly the generation of unsafe images, such as those containing sexual or violent content. Previous research has primarily focused on classifying unsafe concepts based on overall image features. However, extracting abstract harmful concepts directly from concrete image content has proven to be challenging, limiting the effectiveness of existing methods. Our observations reveal that harmful concepts are often embedded in entities and their relationships, particularly in the actions involving these entities. In this work, we propose \Name, a novel approach for identifying unsafe scenes. For the first time, we leverage scene graph generation and classification to detect harmful attributes and relationships within images. Our method focuses on defining and detecting unsafe scenes, providing insight into how unsafe images are generated by Text-to-Image models. In three meta-scenarios, our method achieved F1 scores that were, on average, 95.52% higher than baseline approaches. Additionally, Name effectively localized unsafe portions of the image, removing 95% of harmful content while preserving 76.34% of image consistency. This pioneering study highlights the importance of investigating the intent and purpose of unsafe images to enhance the security of T2I models and ensure safer applications of this technology.