paperSeptember 2026Unreviewed

A Survey on Adversarial Attacks and Defenses for Diffusion Models Across Multiple Modalities

Ozgur Kara, Tarik Can Ozden, Furkan Horoz, Zeqian Long, Haotian Xue, Yipu Chen, Oguzhan Akcin, Yongxin Chen, James Matthew Rehg

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Abstract

Diffusion models have become the dominant family of generative models in the visual domain. However, their widespread public availability enables misuse at scale, motivating a rapidly growing body of research on adversarial attacks and defenses. This survey provides, to our knowledge, the first unified review of this literature across three visual modalities: image, video, and 3D. We introduce a comprehensive, task-centric taxonomy: we first divide the literature by modality; within each modalit

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AML.T0043Craft Adversarial Data

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Cite

@misc{kara2026survey,
  title = {{A Survey on Adversarial Attacks and Defenses for Diffusion Models Across Multiple Modalities}},
  author = {Ozgur Kara and Tarik Can Ozden and Furkan Horoz and Zeqian Long and Haotian Xue and Yipu Chen and Oguzhan Akcin and Yongxin Chen and James Matthew Rehg},
  year = {2026},
  month = sep,
  eprint = {2609.05503},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.05503}
}