CLB-Defense: based on contrastive learning defense for graph neural network against backdoor attack

Abstract

For the problem that the existing backdoor attack defense methods are difficult to deal with irregular and unstructured discrete graph data to alleviate the threat of backdoor attacks, a backdoor attack defense method for GNN based on contrastive learning was proposed, namely CLB-Defense.Specifically, a contrastive model was built by using contrastive learning in an unsupervised way, which searches suspicious backdoored samples.Then the suspicious backdoored samples were reshaped by using the graph importance indexes and the label smoothing strategy, and the defense against graph backdoor attack was realized.Finally, extensive experimental results show that CLB-Defense realizes the effect of defense performance on four public datasets and five popular graph backdoor attacks, e.g., CLB-Defense can reduce the attack success rate by an average of 75.66% (compared with the baselines, an improvement of 54.01%).

Keywords

• graph neural network;backdoor attack;robustness;defense;contrastive learning