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MMA-Diffusion: MultiModal Attack on Diffusion Models

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arxiv 2311.17516 v4 pith:RAOC35HP submitted 2023-11-29 cs.CR cs.CV

Yijun Yang , Ruiyuan Gao , Xiaosen Wang , Tsung-Yi Ho , Nan Xu , Qiang Xu This is my paper · ORCID
classification cs.CRcs.CV
keywords modelsmma-diffusionadoptionadvancementsapproachesattackavenuesbypass
verification ladder T0 review T1 audit T2 compute T3 formal
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In recent years, Text-to-Image (T2I) models have seen remarkable advancements, gaining widespread adoption. However, this progress has inadvertently opened avenues for potential misuse, particularly in generating inappropriate or Not-Safe-For-Work (NSFW) content. Our work introduces MMA-Diffusion, a framework that presents a significant and realistic threat to the security of T2I models by effectively circumventing current defensive measures in both open-source models and commercial online services. Unlike previous approaches, MMA-Diffusion leverages both textual and visual modalities to bypass safeguards like prompt filters and post-hoc safety checkers, thus exposing and highlighting the vulnerabilities in existing defense mechanisms.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PromptSafe: Gated Prompt Tuning for Safe Text-to-Image Generation

    cs.CV 2025-08 conditional novelty 6.0 of 10

    PromptSafe uses LLM-rewritten safe prompts to train a gated soft prompt that suppresses NSFW content in text-to-image generation without image supervision or inference overhead.

  2. From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models

    cs.LG 2025-07 conditional novelty 6.0 of 10

    Seed2Harvest expands 1,000 human adversarial prompts into 27,650 LLM-generated variants that keep roughly comparable unsafe-image trigger rates and add hundreds of new geographic contexts.

  3. PRJ: Perception-Retrieval-Judgement for Generated Images

    cs.CV 2025-06 reject novelty 4.0 of 10

    A new safety checker for AI-generated images, built from a vision-language model, retrieval-augmented knowledge lookup, and an LLM judge, reports higher detection rates and category-level toxicity scores than three ex...

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