Are Defenses for Graph Neural Networks Robust?

Mujkanovic, Felix and Geisler, Simon and Günnemann, Stephan and Bojchevski, Aleksandar
(2022) Are Defenses for Graph Neural Networks Robust?
In: NeurIPS.
Conference: NeurIPS Conference on Neural Information Processing Systems

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A cursory reading of the literature suggests that we have made a lot of progress in designing effective adversarial defenses for Graph Neural Networks (GNNs). Yet, the standard methodology has a serious flaw – virtually all of the defenses are evaluated against non-adaptive attacks leading to overly optimistic robustness estimates. We perform a thorough robustness analysis of 7 of the most popular defenses spanning the entire spectrum of strategies, i.e., aimed at improving the graph, the architecture, or the training. The results are sobering – most defenses show no or only marginal improvement compared to an undefended baseline. We advocate using custom adaptive attacks as a gold standard and we outline the lessons we learned from successfully designing such attacks. Moreover, our diverse collection of perturbed graphs forms a (black-box) unit test offering a first glance at a model's robustness.


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