Groot: Adversarial Testing for Generative Text-to-Image Models with Tree-based Semantic Transformation
<|reference_start|>Groot: Adversarial Testing for Generative Text-to-Image Models with Tree-based Semantic Transformation: 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.<|reference_end|>
@article{liu2024groot:,
    title={Groot: Adversarial Testing for Generative Text-to-Image Models with
  Tree-based Semantic Transformation},
    author={Yi Liu, Guowei Yang, Gelei Deng, Feiyue Chen, Yuqi Chen, Ling Shi,
  Tianwei Zhang, and Yang Liu},
    journal={arXiv preprint arXiv:2402.12100},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12100},
    primaryClass={cs.CL cs.AI cs.CR cs.SE}
}