Attacking Graph Classification via Bayesian Optimisation

Xingchen Wan, Henry Kenlay, Binxin Ru, Arno Blaas, Michael Osborne, Xiaowen Dong

Keywords: adversarial attack, Bayesian optimisation, graph classification

TL;DR: We present an effective black-box and query-efficient adversarial attack on graph classification models

Abstract: Graph neural networks have been shown to be vulnerable to adversarial attacks. While the majority of the literature focuses on such vulnerability in node-level classification tasks, little effort has been dedicated to attacks on graph-level classification, an important problem with numerous real-life applications such as biochemistry and social network analysis. The few existing methods often require unrealistic setups, such as access to internal information of the victim models, or an impractically-large number of queries. We present a novel Bayesian optimisation-based attack method for graph classification models. Our method is black-box, query-efficient and parsimonious with respect to the perturbation applied. We empirically validate the effectiveness and flexibility of the proposed method and analyse patterns behind the adversarial samples produced, which may shed further light on the adversarial robustness of graph classification models.