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        <title>Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation</title>
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        <namePart type="given">Xuanli</namePart>
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    <abstract>Modern NLP models are often trained over large untrusted datasets, raising the potential for a malicious adversary to compromise model behaviour. For instance, backdoors can be implanted through crafting training instances with a specific textual trigger and a target label. This paper posits that backdoor poisoning attacks exhibit a spurious correlation between simple text features and classification labels, and accordingly, proposes methods for mitigating spurious correlation as means of defence. Our empirical study reveals that the malicious triggers are highly correlated to their target labels; therefore such correlations are extremely distinguishable compared to those scores of benign features, and can be used to filter out potentially problematic instances. Compared with several existing defences, our defence method significantly reduces attack success rates across backdoor attacks, and in the case of insertion-based attacks, our method provides a near-perfect defence.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.60</identifier>
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            <start>953</start>
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%0 Conference Proceedings
%T Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation
%A He, Xuanli
%A Xu, Qiongkai
%A Wang, Jun
%A Rubinstein, Benjamin
%A Cohn, Trevor
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F he-etal-2023-mitigating
%X Modern NLP models are often trained over large untrusted datasets, raising the potential for a malicious adversary to compromise model behaviour. For instance, backdoors can be implanted through crafting training instances with a specific textual trigger and a target label. This paper posits that backdoor poisoning attacks exhibit a spurious correlation between simple text features and classification labels, and accordingly, proposes methods for mitigating spurious correlation as means of defence. Our empirical study reveals that the malicious triggers are highly correlated to their target labels; therefore such correlations are extremely distinguishable compared to those scores of benign features, and can be used to filter out potentially problematic instances. Compared with several existing defences, our defence method significantly reduces attack success rates across backdoor attacks, and in the case of insertion-based attacks, our method provides a near-perfect defence.
%R 10.18653/v1/2023.emnlp-main.60
%U https://aclanthology.org/2023.emnlp-main.60
%U https://doi.org/10.18653/v1/2023.emnlp-main.60
%P 953-967

Markdown (Informal)

[Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation](https://aclanthology.org/2023.emnlp-main.60) (He et al., EMNLP 2023)

• Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation (He et al., EMNLP 2023)

ACL

• Xuanli He, Qiongkai Xu, Jun Wang, Benjamin Rubinstein, and Trevor Cohn. 2023. Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 953–967, Singapore. Association for Computational Linguistics.