L-AutoDA: Large Language Models for Automatically Evolving Decision-based Adversarial Attacks-Reference-Cited by-同舟云学术

L-AutoDA: Large Language Models for Automatically Evolving Decision-based Adversarial Attacks

Published:2024-07-14 Issue: Volume: Page:1846-1854
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference Companion
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Author:

Guo Ping¹²^ORCID,Liu Fei¹²^ORCID,Lin Xi¹²^ORCID,Zhao Qingchuan¹^ORCID,Zhang Qingfu¹²^ORCID

Affiliation:

1. City University of Hong Kong, Hong Kong, Hong Kong

2. CityU Shenzhen Research Institute, Shenzhen, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3638530.3664121

Reference45 articles.

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2. Wieland Brendel, Jonas Rauber, and Matthias Bethge. 2018. Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models. In 6th International Conference on Learning Representations, (ICLR). OpenReview.net.

3. Yulong Cao, Chaowei Xiao, Benjamin Cyr, Yimeng Zhou, Won Park, Sara Rampazzi, Qi Alfred Chen, Kevin Fu, and Z. Morley Mao. 2019. Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, (CCS). ACM.

4. Anirban Chakraborty Manaar Alam Vishal Dey Anupam Chattopadhyay and Debdeep Mukhopadhyay. 2021. A survey on adversarial attacks and defences. CAAI Trans. Intell. Technol. (2021). 10.1049/CIT2.12028

5. Jianbo Chen, Michael I. Jordan, and Martin J. Wainwright. 2020. HopSkipJumpAttack: A Query-Efficient Decision-Based Attack. In 2020 IEEE Symposium on Security and Privacy, (SP). IEEE.