Culture-based Adversarial Attack on Text-to-Image Models

Le résumé fourni par la source

Text-to-image (T2I) models aim to output high-quality images based on the input prompt. It commonly incorporates safety filters to prevent the generation of sensitive images. In this study, we aim to reveal the safety vulnerabilities of black-box T2I models in a real-world scenario. To address the challenges of bypassing filters and mitigating the trade-off between stealthiness and effectiveness, we investigate the concepts from Semiotics and apply cultural elements in the design of adversarial prompts. Specifically, we propose CAAM, a Culture-based Adversarial Attack on T2I Models with a generation module, which collaborates with multiple LLM-based agents to generate fluent adversarial prompts, and a feedback module, which provides feedback to compromise the stealthiness and effectiveness. Extensive experiments with open-source and commercial T2I models are conducted to demonstrate the effectiveness of our method. This paper includes model-generated content that may contain offensive or distressing material.

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