Defending Graph Neural Networks Against Backdoor Attacks via Symmetry-Aware Graph Self-Distillation

Graph neural networks (GNNs) have exhibited remarkable performance in various applications. Still, research has revealed their vulnerability to backdoor attacks, where Adversaries inject malicious patterns during the training phase to establish a relationship between backdoor patterns and a specific target label, thereby manipulating the behavior of poisoned GNNs. The inherent symmetry present in the behavior of GNNs can be leveraged to strengthen the robustness of GNNs. This paper presents a quantitative metric, termed Logit Margin Rate (LMR), for analyzing the symmetric properties of the output landscapes across GNN layers. Additionally, a learning paradigm of graph self-distillation is combined with LMR to distill the symmetry knowledge from shallow layers, which can serve as the defensive supervision signals to preserve the benign symmetric relationships in deep layers, thus improving both model stability and adversarial robustness. Experiments were conducted on four benchmark datasets to evaluate the robustness of the proposed Graph Self-Distillation-based Backdoor Defense (GSD-BD) method against three widely used backdoor attack algorithms, demonstrating the robustness of GSD-BD even under severe infection scenarios.