  Due to the popularity of Artificial Intelligence (AI) techniques, we are
witnessing an increasing number of backdoor injection attacks that are designed
to maliciously threaten Deep Neural Networks (DNNs) causing misclassification.
Although there exist various defense methods that can effectively erase
backdoors from DNNs, they greatly suffer from both high Attack Success Rate
(ASR) and a non-negligible loss in Benign Accuracy (BA). Inspired by the
observation that a backdoored DNN tends to form a new cluster in its feature
spaces for poisoned data, in this paper we propose a novel two-stage backdoor
defense method, named MCLDef, based on Model-Contrastive Learning (MCL). In the
first stage, our approach performs trigger inversion based on trigger
synthesis, where the resultant trigger can be used to generate poisoned data.
In the second stage, under the guidance of MCL and our defined positive and
negative pairs, MCLDef can purify the backdoored model by pulling the feature
representations of poisoned data towards those of their clean data
counterparts. Due to the shrunken cluster of poisoned data, the backdoor formed
by end-to-end supervised learning is eliminated. Comprehensive experimental
results show that, with only 5% of clean data, MCLDef significantly outperforms
state-of-the-art defense methods by up to 95.79% reduction in ASR, while in
most cases the BA degradation can be controlled within less than 2%. Our code
is available at https://github.com/WeCanShow/MCL.
