LoSplit: Loss-Guided Dynamic Split for Training-Time Defense Against Graph Backdoor Attacks
Di Jin, Yuxiang Zhang, Bingdao Feng, Xiaobao Wang, Dongxiao He, Zhen Wang
Graph Neural Networks (GNNs) are vulnerable to backdoor attacks. Existing defenses primarily rely on detecting structural anomalies, distributional outliers, or perturbation-induced prediction instability, which struggle to handle the more subtle, feature-based attacks that do not introduce obvious topological changes. Our empirical analysis reveals that both structure-based and feature-based attacks not only cause early loss convergence of target nodes but also induce a class-coherent loss drift, where this early convergence gradually spreads to nearby clean nodes, leading to significant distribution overlap. To address this issue, we propose \emph{LoSplit}, a training-time defense framework that leverages this early-stage loss drift to accurately separate target nodes. Our method dynamically selects epochs with maximal loss divergence, clusters target nodes via Gaussian Mixture Models (GMM), and applies a Decoupling-Unlearning strategy to break the association between target nodes and malicious labels. Extensive experiments on multiple real-world datasets demonstrate the effectiveness of this approach, significantly reducing attack success rates while maintaining high clean accuracy across diverse backdoor attack strategies.