Backdoor Attacks to Graph Neural Networks

Zaixi Zhang, Jinyuan Jia, Binghui Wang, Neil Zhenqiang Gong

Abstract: In this work, we propose the first backdoor attack to graph neural networks (GNN). Specifically, we propose a \emph{subgraph based backdoor attack} to GNN for graph classification. In our backdoor attack, a GNN classifier predicts an attacker-chosen target label for a testing graph once a predefined subgraph is injected to the testing graph. Our empirical results on three real-world graph datasets show that our backdoor attacks are effective with a small impact on a GNN's prediction accuracy for clean testing graphs.

One-sentence Summary: In this work, we propose the first backdoor attack to graph neural networks (GNN).

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