Momentum gradient-based untargeted poisoning attack on hypergraph neural networks

Hypergraph Neural Networks (HGNNs) have been successfully applied in various hypergraph-related tasks due to their excellent higher-order representation capabilities. Unfortunately, recent works have shown deep learning models vulnerable to diverse attacks. Most studies of attacks on graphs have focused on Graph Neural Networks (GNNs), and the study of attacks on HGNNs remains largely unexplored. In this paper, we try to bridge this gap. We design a new untargeted poisoning attack for HGNNs, MGHGA, which focuses on modifying node features. We consider the process of HGNNs training and use a surrogate model to implement the attack before hypergraph modeling. Precisely, MGHGA consists of two parts: feature selection and feature modification. We use a momentum gradient mechanism to choose the attack node features in the feature selection module. In the feature modification module, we use two feature generation approaches (direct modification and sign gradient) to enable MGHGA to be employed on discrete and continuous datasets. We conduct extensive experiments on seven benchmark datasets to validate the attack performance of MGHGA in the node and the visual object classification tasks. The results show that MGHGA improves performance by an average of 2% compared to the baselines.