paperAugust 2026Unreviewed

DiSCO: Defending text-to-image generation through distribution-guided contrastive prompt optimization

Tong Zhang, M. Alfarra, Carlos Hinojosa, Christos Louizos, Bernard Ghanem

Abstract

As text-to-image generative models advance, they raise critical safety concerns, particularly the generation of Not-Safe-For-Work (NSFW) content such as violence and nudity, further exacerbated by red-teaming adversarial attacks. Existing defenses predominantly operate under white-box assumptions, relying on text encoder optimization, weight editing, or inference-time intervention, and fundamentally cannot scale to proprietary models. Black-box alternatives based on LLM prompt rewriting offer br

Framework mappings

MITRE ATLAS

AML.T0043Craft Adversarial Data

NIST AI Risk Management Framework

MEASUREMeasure

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Cite

@misc{zhang2026disco,
  title = {{DiSCO: Defending text-to-image generation through distribution-guided contrastive prompt optimization}},
  author = {Tong Zhang and M. Alfarra and Carlos Hinojosa and Christos Louizos and Bernard Ghanem},
  year = {2026},
  month = aug,
  eprint = {2608.17067},
  archivePrefix = {arXiv},
  url = {https://www.semanticscholar.org/paper/74f0b8c1be197c2e6a6d54d29c20352d640cb979}
}