A Survey on Universal Adversarial Attack

Zhang, Chaoning; Benz, Philipp; Lin, Chenguo; Karjauv, Adil; Wu, Jing; Kweon, In So

doi:10.24963/ijcai.2021/635

A Survey on Universal Adversarial Attack

Chaoning Zhang, Philipp Benz, Chenguo Lin, Adil Karjauv, Jing Wu, In So Kweon

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Survey Track. Pages 4687-4694.

https://doi.org/10.24963/ijcai.2021/635

Keywords

Computer vision: General
Machine learning: General

Abstract

The intriguing phenomenon of adversarial examples has attracted significant attention in machine learning and what might be more surprising to the community is the existence of universal adversarial perturbations (UAPs), i.e. a single perturbation to fool the target DNN for most images. With the focus on UAP against deep classifiers, this survey summarizes the recent progress on universal adversarial attacks, discussing the challenges from both the attack and defense sides, as well as the reason for the existence of UAP. We aim to extend this work as a dynamic survey that will regularly update its content to follow new works regarding UAP or universal attack in a wide range of domains, such as image, audio, video, text, etc. Relevant updates will be discussed at: https://bit.ly/2SbQlLG. We welcome authors of future works in this field to contact us for including your new findings.

Chaoning Zhang, Philipp Benz, Chenguo Lin, Adil Karjauv, Jing Wu, and In So Kweon. 2021. A Survey on Universal Adversarial Attack. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 4687–4694. https://doi.org/10.24963/ijcai.2021/635

@inproceedings{ijcai2021p635,
  title     = {A Survey on Universal Adversarial Attack},
  author    = {Zhang, Chaoning and Benz, Philipp and Lin, Chenguo and Karjauv, Adil and Wu, Jing and Kweon, In So},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Prof. Zhi-Hua Zhou},
  pages     = {4687--4694},
  year      = {2021},
  month     = {8},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2021/635},
  url       = {https://doi.org/10.24963/ijcai.2021/635},
}

TY  - CPAPER
TI  - A Survey on Universal Adversarial Attack
AU  - Zhang, Chaoning
AU  - Benz, Philipp
AU  - Lin, Chenguo
AU  - Karjauv, Adil
AU  - Wu, Jing
AU  - Kweon, In So
T2  - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2021
SP  - 4687
EP  - 4694
DO  - 10.24963/ijcai.2021/635
UR  - https://doi.org/10.24963/ijcai.2021/635
ER  -