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title Chain Association-based Attacking and Shielding Natural Language Processing Systems
booktitle Proceedings of the 16th Asian Conference on Machine Learning
year 2025
volume 260
series Proceedings of Machine Learning Research
month 0
publisher PMLR
pdf https://raw.githubusercontent.com/mlresearch/v260/main/assets/huang25c/huang25c.pdf
url https://proceedings.mlr.press/v260/huang25c.html
openreview FZmDuinQgJ
abstract Association as a gift enables people do not have to mention something in completely straightforward words and allows others to understand what they intend to refer to. In this paper, we propose a chain association-based adversarial attack against natural language processing systems, utilizing the comprehension gap between humans and machines. We first generate a chain association graph for Chinese characters based on the association paradigm for building search space of potential adversarial examples. Then, we introduce an discrete particle swarm optimization algorithm to search for the optimal adversarial examples. We conduct comprehensive experiments and show that advanced natural language processing models and applications, including large language models, are vulnerable to our attack, while humans appear good at understanding the perturbed text. We also explore two methods, including adversarial training and associative graph-based recovery, to shield systems from chain association-based attack. Since a few examples that use some derogatory terms, this paper contains materials that may be offensive or upsetting to some people.
layout inproceedings
issn 2640-3498
id huang25c
tex_title Chain Association-based Attacking and Shielding Natural Language Processing Systems
firstpage 905
lastpage 920
page 905-920
order 905
cycles false
bibtex_editor Nguyen, Vu and Lin, Hsuan-Tien
editor
given family
Vu
Nguyen
given family
Hsuan-Tien
Lin
bibtex_author Huang, JiaCheng and Chen, Long
author
given family
JiaCheng
Huang
given family
Long
Chen
date 2025-01-14
address
container-title Proceedings of the 16th Asian Conference on Machine Learning
genre inproceedings
issued
date-parts
2025
1
14
extras