Groot: Adversarial Testing for Generative Text-to-Image Models with
  Tree-based Semantic Transformation
Yi Liu Guowei Yang Gelei Deng Feiyue Chen Yuqi Chen Ling Shi Tianwei Zhang Yang Liu
With the prevalence of text-to-image generative models, their safety becomes
a critical concern. adversarial testing techniques have been developed to probe
whether such models can be prompted to produce Not-Safe-For-Work (NSFW)
content. However, existing solutions face several challenges, including low
success rate and inefficiency. We introduce Groot, the first automated
framework leveraging tree-based semantic transformation for adversarial testing
of text-to-image models. Groot employs semantic decomposition and sensitive
element drowning strategies in conjunction with LLMs to systematically refine
adversarial prompts. Our comprehensive evaluation confirms the efficacy of
Groot, which not only exceeds the performance of current state-of-the-art
approaches but also achieves a remarkable success rate (93.66%) on leading
text-to-image models such as DALL-E 3 and Midjourney.