DeHiB: Deep Hidden Backdoor Attack on Semi-supervised Learning via Adversarial Perturbation

The threat of data-poisoning backdoor attacks on learning algorithms typically comes from the labeled data used for learning. However, in deep semi-supervised learning (SSL), unknown threats mainly stem from unlabeled data. In this paper, we propose a novel deep hidden backdoor (DeHiB) attack for SSL-based systems. In contrast to the conventional attacking methods, the DeHiB can feed malicious unlabeled training data to the semi-supervised learner so as to enable the SSL model to output premeditated results. In particular, a robust adversarial perturbation generator regularized by a unified objective function is proposed to generate poisoned data. To alleviate the negative impact of trigger patterns on model accuracy and improve the attack success rate, a novel contrastive data poisoning strategy is designed. Using the proposed data poisoning scheme, one can implant the backdoor into the SSL model using the raw data without handcrafted labels. Extensive experiments based on CIFAR10 and CIFAR100 datasets demonstrates the effectiveness and crypticity of the proposed scheme.