DREAM: Scalable Red Teaming for Text-to-Image Generative Systems via Distribution Modeling

• URL: http://arxiv.org/abs/2507.16329v1
• Date: Tue, 22 Jul 2025 08:10:22 GMT
• Title: DREAM: Scalable Red Teaming for Text-to-Image Generative Systems via Distribution Modeling
• Authors: Boheng Li, Junjie Wang, Yiming Li, Zhiyang Hu, Leyi Qi, Jianshuo Dong, Run Wang, Han Qiu, Zhan Qin, Tianwei Zhang,
• Abstract summary: Text-to-image (T2I) generative models are still susceptible to producing harmful content, such as sexual or violent imagery.<n>Red teaming aims to proactively identify diverse prompts that can elicit unsafe outputs from the T2I system.<n>We propose DREAM, a scalable red teaming framework to automatically uncover diverse problematic prompts from a given T2I system.
• Score: 23.856811182352992
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Despite the integration of safety alignment and external filters, text-to-image (T2I) generative models are still susceptible to producing harmful content, such as sexual or violent imagery. This raises serious concerns about unintended exposure and potential misuse. Red teaming, which aims to proactively identify diverse prompts that can elicit unsafe outputs from the T2I system (including the core generative model as well as potential external safety filters and other processing components), is increasingly recognized as an essential method for assessing and improving safety before real-world deployment. Yet, existing automated red teaming approaches often treat prompt discovery as an isolated, prompt-level optimization task, which limits their scalability, diversity, and overall effectiveness. To bridge this gap, in this paper, we propose DREAM, a scalable red teaming framework to automatically uncover diverse problematic prompts from a given T2I system. Unlike most prior works that optimize prompts individually, DREAM directly models the probabilistic distribution of the target system's problematic prompts, which enables explicit optimization over both effectiveness and diversity, and allows efficient large-scale sampling after training. To achieve this without direct access to representative training samples, we draw inspiration from energy-based models and reformulate the objective into simple and tractable objectives. We further introduce GC-SPSA, an efficient optimization algorithm that provide stable gradient estimates through the long and potentially non-differentiable T2I pipeline. The effectiveness of DREAM is validated through extensive experiments, demonstrating that it surpasses 9 state-of-the-art baselines by a notable margin across a broad range of T2I models and safety filters in terms of prompt success rate and diversity.

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