Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation

Xuanli He, Qiongkai Xu, Jun Wang, Benjamin I. P. Rubinstein, Trevor Cohn

Submission Type: Regular Long Paper

Submission Track: Machine Learning for NLP

Submission Track 2: NLP Applications

Keywords: backdoor attack, backdoor defence, spurious correlation

TL;DR: The paper presents an innovative methodology designed to provide protection from backdoor attacks, with a particular focus on addressing the issue from the perspective of spurious correlation.

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.

Submission Number: 1990