@inproceedings{hu-etal-2023-mask,
title = "[{MASK}] Insertion: a robust method for anti-adversarial attacks",
author = "Hu, Xinrong and
Xu, Ce and
Ma, Junlong and
Huang, Zijian and
Yang, Jie and
Guo, Yi and
Barthelemy, Johan",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.78",
doi = "10.18653/v1/2023.findings-eacl.78",
pages = "1058--1070",
abstract = "Adversarial attack aims to perturb input sequences and mislead a trained model for false predictions. To enhance the model robustness, defensing methods are accordingly employed by either data augmentation (involving adversarial samples) or model enhancement (modifying the training loss and/or model architecture). In contrast to previous work, this paper revisits the masked language modeling (MLM) and presents a simple yet efficient algorithm against adversarial attacks, termed [MASK] insertion for defensing (MI4D). Specifically, MI4D simply inserts [MASK] tokens to input sequences during training and inference, maximizing the intersection of the new convex hull (MI4D creates) with the original one (the clean input forms). As neither additional adversarial samples nor the model modification is required, MI4D is as computationally efficient as traditional fine-tuning. Comprehensive experiments have been conducted using three benchmark datasets and four attacking methods. MI4D yields a significant improvement (on average) of the accuracy between 3.2 and 11.1 absolute points when compared with six state-of-the-art defensing baselines.",
}
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%0 Conference Proceedings
%T [MASK] Insertion: a robust method for anti-adversarial attacks
%A Hu, Xinrong
%A Xu, Ce
%A Ma, Junlong
%A Huang, Zijian
%A Yang, Jie
%A Guo, Yi
%A Barthelemy, Johan
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F hu-etal-2023-mask
%X Adversarial attack aims to perturb input sequences and mislead a trained model for false predictions. To enhance the model robustness, defensing methods are accordingly employed by either data augmentation (involving adversarial samples) or model enhancement (modifying the training loss and/or model architecture). In contrast to previous work, this paper revisits the masked language modeling (MLM) and presents a simple yet efficient algorithm against adversarial attacks, termed [MASK] insertion for defensing (MI4D). Specifically, MI4D simply inserts [MASK] tokens to input sequences during training and inference, maximizing the intersection of the new convex hull (MI4D creates) with the original one (the clean input forms). As neither additional adversarial samples nor the model modification is required, MI4D is as computationally efficient as traditional fine-tuning. Comprehensive experiments have been conducted using three benchmark datasets and four attacking methods. MI4D yields a significant improvement (on average) of the accuracy between 3.2 and 11.1 absolute points when compared with six state-of-the-art defensing baselines.
%R 10.18653/v1/2023.findings-eacl.78
%U https://aclanthology.org/2023.findings-eacl.78
%U https://doi.org/10.18653/v1/2023.findings-eacl.78
%P 1058-1070
Markdown (Informal)
[[MASK] Insertion: a robust method for anti-adversarial attacks](https://aclanthology.org/2023.findings-eacl.78) (Hu et al., Findings 2023)
ACL