NCL: Textual Backdoor Defense Using Noise-augmented Contrastive Learning

• URL: http://arxiv.org/abs/2303.01742v1
• Date: Fri, 3 Mar 2023 07:07:04 GMT
• Title: NCL: Textual Backdoor Defense Using Noise-augmented Contrastive Learning
• Authors: Shengfang Zhai, Qingni Shen, Xiaoyi Chen, Weilong Wang, Cong Li, Yuejian Fang and Zhonghai Wu
• Abstract summary: We propose a Noise-augmented Contrastive Learning framework to defend against textual backdoor attacks. Experiments demonstrate the effectiveness of our method in defending three types of textual backdoor attacks, outperforming the prior works.
• Score: 14.537250979495596
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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