  At present, backdoor attacks attract attention as they do great harm to deep
learning models. The adversary poisons the training data making the model being
injected with a backdoor after being trained unconsciously by victims using the
poisoned dataset. In the field of text, however, existing works do not provide
sufficient defense against backdoor attacks. In this paper, we propose a
Noise-augmented Contrastive Learning (NCL) framework to defend against textual
backdoor attacks when training models with untrustworthy data. With the aim of
mitigating the mapping between triggers and the target label, we add
appropriate noise perturbing possible backdoor triggers, augment the training
dataset, and then pull homology samples in the feature space utilizing
contrastive learning objective. Experiments demonstrate the effectiveness of
our method in defending three types of textual backdoor attacks, outperforming
the prior works.
