DiffZOO: A Purely Query-Based Black-Box Attack for Red-teaming Text-to-Image Generative Model via Zeroth Order Optimization

TL;DR

DiffZOO introduces a purely query-based black-box attack method using Zeroth Order Optimization to effectively test and expose safety vulnerabilities in text-to-image diffusion models without prior model knowledge.

Contribution

The paper presents DiffZOO, a novel black-box attack framework that leverages Zeroth Order Optimization and discrete prompt enhancement techniques for safer T2I models.

Findings

Achieves 8.5% higher attack success rate than previous methods.

Effective against multiple safety mechanisms and online servers.

Demonstrates practical utility for red teaming T2I models.

Abstract

Current text-to-image (T2I) synthesis diffusion models raise misuse concerns, particularly in creating prohibited or not-safe-for-work (NSFW) images. To address this, various safety mechanisms and red teaming attack methods are proposed to enhance or expose the T2I model's capability to generate unsuitable content. However, many red teaming attack methods assume knowledge of the text encoders, limiting their practical usage. In this work, we rethink the case of \textit{purely black-box} attacks without prior knowledge of the T2l model. To overcome the unavailability of gradients and the inability to optimize attacks within a discrete prompt space, we propose DiffZOO which applies Zeroth Order Optimization to procure gradient approximations and harnesses both C-PRV and D-PRV to enhance attack prompts within the discrete prompt domain. We evaluated our method across multiple safety mechanisms of the T2I diffusion model and online servers. Experiments on multiple state-of-the-art safety mechanisms show that DiffZOO attains an 8.5% higher average attack success rate than previous works, hence its promise as a practical red teaming tool for T2l models.