Securing GNNs: Explanation-Based Identification of Backdoored Training Graphs

arXiv:2403.18136v1 Announce Type: new
Abstract: Graph Neural Networks (GNNs) have gained popularity in numerous domains, yet they are vulnerable to backdoor attacks that can compromise their performance and ethical application. The detection of these attacks is crucial for maintaining the reliability and security of GNN classification tasks, but effective detection techniques are lacking. Following an initial investigation, we observed that while graph-level explanations can offer limited insights, their effectiveness in detecting backdoor triggers is inconsistent and incomplete. To bridge this …

abstract application arxiv attacks backdoor classification cs.ai cs.lg detection domains ethical gnn gnns graph graph neural networks graphs identification networks neural networks performance reliability security tasks training type vulnerable