@inproceedings{yu-etal-2024-query,
title = "Query-Efficient Textual Adversarial Example Generation for Black-Box Attacks",
author = "Yu, Zhen and
Chen, Zhenhua and
He, Kun",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.31",
pages = "556--569",
abstract = "Deep neural networks for Natural Language Processing (NLP) have been demonstrated to be vulnerable to textual adversarial examples. Existing black-box attacks typically require thousands of queries on the target model, making them expensive in real-world applications. In this paper, we propose a new approach that guides the word substitutions using prior knowledge from the training set to improve the attack efficiency. Specifically, we introduce Adversarial Boosting Preference (ABP), a metric that quantifies the importance of words and guides adversarial word substitutions. We then propose two query-efficient attack strategies based on ABP: query-free attack ($ABP_{free}$) and guided search attack ($ABP_{guide}$). Extensive evaluations for text classification demonstrate that $ABP_{free}$ generates more natural adversarial examples than existing universal attacks, $ABP_{guide}$ significantly reduces the number of queries by a factor of 10 500 while achieving comparable or even better performance than black-box attack baselines. Furthermore, we introduce the first ensemble attack $ABP_{ens}$ in NLP, which gains further performance improvements and achieves better transferability and generalization by the ensemble of the ABP across different models and domains. Code is available at https://github.com/BaiDingHub/ABP.",
}
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<abstract>Deep neural networks for Natural Language Processing (NLP) have been demonstrated to be vulnerable to textual adversarial examples. Existing black-box attacks typically require thousands of queries on the target model, making them expensive in real-world applications. In this paper, we propose a new approach that guides the word substitutions using prior knowledge from the training set to improve the attack efficiency. Specifically, we introduce Adversarial Boosting Preference (ABP), a metric that quantifies the importance of words and guides adversarial word substitutions. We then propose two query-efficient attack strategies based on ABP: query-free attack (ABP_free) and guided search attack (ABP_guide). Extensive evaluations for text classification demonstrate that ABP_free generates more natural adversarial examples than existing universal attacks, ABP_guide significantly reduces the number of queries by a factor of 10 500 while achieving comparable or even better performance than black-box attack baselines. Furthermore, we introduce the first ensemble attack ABP_ens in NLP, which gains further performance improvements and achieves better transferability and generalization by the ensemble of the ABP across different models and domains. Code is available at https://github.com/BaiDingHub/ABP.</abstract>
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%0 Conference Proceedings
%T Query-Efficient Textual Adversarial Example Generation for Black-Box Attacks
%A Yu, Zhen
%A Chen, Zhenhua
%A He, Kun
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F yu-etal-2024-query
%X Deep neural networks for Natural Language Processing (NLP) have been demonstrated to be vulnerable to textual adversarial examples. Existing black-box attacks typically require thousands of queries on the target model, making them expensive in real-world applications. In this paper, we propose a new approach that guides the word substitutions using prior knowledge from the training set to improve the attack efficiency. Specifically, we introduce Adversarial Boosting Preference (ABP), a metric that quantifies the importance of words and guides adversarial word substitutions. We then propose two query-efficient attack strategies based on ABP: query-free attack (ABP_free) and guided search attack (ABP_guide). Extensive evaluations for text classification demonstrate that ABP_free generates more natural adversarial examples than existing universal attacks, ABP_guide significantly reduces the number of queries by a factor of 10 500 while achieving comparable or even better performance than black-box attack baselines. Furthermore, we introduce the first ensemble attack ABP_ens in NLP, which gains further performance improvements and achieves better transferability and generalization by the ensemble of the ABP across different models and domains. Code is available at https://github.com/BaiDingHub/ABP.
%U https://aclanthology.org/2024.naacl-long.31
%P 556-569
Markdown (Informal)
[Query-Efficient Textual Adversarial Example Generation for Black-Box Attacks](https://aclanthology.org/2024.naacl-long.31) (Yu et al., NAACL 2024)
ACL