Rethinking Word-level Adversarial Attack: The Trade-off between Efficiency, Effectiveness, and Imperceptibility

Pengwei Zhan, Jing Yang, He Wang, Chao Zheng, Liming Wang

Abstract
Neural language models have demonstrated impressive performance in various tasks but remain vulnerable to word-level adversarial attacks. Word-level adversarial attacks can be formulated as a combinatorial optimization problem, and thus, an attack method can be decomposed into search space and search method. Despite the significance of these two components, previous works inadequately distinguish them, which may lead to unfair comparisons and insufficient evaluations. In this paper, to address the inappropriate practices in previous works, we perform thorough ablation studies on the search space, illustrating the substantial influence of search space on attack efficiency, effectiveness, and imperceptibility. Based on the ablation study, we propose two standardized search spaces: the Search Space for ImPerceptibility (SSIP) and Search Space for EffecTiveness (SSET). The reevaluation of eight previous attack methods demonstrates the success of SSIP and SSET in achieving better trade-offs between efficiency, effectiveness, and imperceptibility in different scenarios, offering fair and comprehensive evaluations of previous attack methods and providing potential guidance for future works.
Anthology ID:
2024.lrec-main.1223
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
14037–14052
Language:
URL:
https://aclanthology.org/2024.lrec-main.1223
DOI:
Bibkey:
Cite (ACL):
Pengwei Zhan, Jing Yang, He Wang, Chao Zheng, and Liming Wang. 2024. Rethinking Word-level Adversarial Attack: The Trade-off between Efficiency, Effectiveness, and Imperceptibility. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 14037–14052, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Rethinking Word-level Adversarial Attack: The Trade-off between Efficiency, Effectiveness, and Imperceptibility (Zhan et al., LREC-COLING 2024)
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https://preview.aclanthology.org/dois-2013-emnlp/2024.lrec-main.1223.pdf
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