UnboundAttack: Generating Unbounded Adversarial Attacks to Graph Neural Networks

Graph Neural Networks (GNNs) have demonstrated state-of-the-art performance in various graph representation learning tasks. Recently, studies revealed their vulnerability to adversarial attacks. While the available attack strategies are based on applying perturbations on existing graphs within a specific budget, proposed defense mechanisms successfully guard against this type of attack. This paper proposes a new perspective founded on unrestricted adversarial examples. We propose to produce adversarial attacks by generating completely new data points instead of perturbing existing ones. We introduce a framework, so-called UnboundAttack, leveraging the advancements in graph generation to produce graphs preserving the semantics of the available training data while misleading the targeted classifier. Importantly, our method does not assume any knowledge about the underlying architecture. Finally, we validate the effectiveness of our proposed method in a realistic setting related to molecular graphs.