Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation

TL;DR

This paper identifies spurious correlations in backdoor poisoning attacks on NLP models and proposes mitigation methods that effectively filter malicious instances, significantly reducing attack success rates especially against insertion-based attacks.

Contribution

It introduces a novel perspective on backdoor attacks as spurious correlations and develops filtering techniques that outperform existing defenses in mitigating such attacks.

Findings

Malicious triggers are highly correlated with target labels.

Filtering based on correlation scores effectively detects malicious instances.

Near-perfect defense against insertion-based backdoor attacks.

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 \emph{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.