DShield: Defending against Backdoor Attacks on Graph Neural Networks via Discrepancy Learning

—Graph Neural Networks (GNNs) are vulnerable to backdoor attacks, where triggers inserted into original graphs cause adversary-determined predictions. Backdoor attacks on GNNs, typically focusing on node classification tasks, are categorized by dirty-and clean-label attacks and pose challenges due to the interconnected nature of normal and poisoned nodes. Current defenses are indeed circumvented by sophisticated triggers and often rely on strong assumptions borrowed from other domains (e.g., rapid loss drops on poisoned images). They lead to high attack risks, failing to effectively protect against both dirty-and clean-label attacks simultaneously. To tackle these challenges, we propose DShield, a comprehensive defense framework with a discrepancy learning mechanism to defend against various graph backdoor attacks. Specifically, we reveal two vital facts during the attacking process: semantic drift where dirty-label attacks modify the semantic information of poisoned nodes, and attribute over-emphasis where clean-label attacks exaggerate specific attributes to enforce adversary-determined predictions. Motivated by those, DShield employs a self-supervised learning framework to construct a model without relying on manipulated label information. Subsequently, it utilizes both the self-supervised and backdoored models to analyze discrepancies in semantic information and attribute importance, effectively filtering out poisoned nodes. Finally, DShield trains normal models using the preserved nodes, thereby minimizing the impact of poisoned nodes. Compared with 6 state-of-the-art defenses under 21 backdoor attacks, we conduct evaluations on 7 datasets with 2 victim models to demonstrate that DShield effectively mitigates backdoor threats with minimal degradation in performance on normal nodes. For instance