T-Attack: Toward Black-Box Adversarial Attacks on GNN-Based Trust Prediction in OSNs

The remarkably developed graph neural networks (GNNs) are extensively applied to specific tasks in online social networks (OSNs), especially in the vital domain of social trust. Meanwhile, the vulnerability of GNN applied in trust assessment can be exposed leveraging the deployment of subtly designed adversarial attacks. However, the predominant adversarial attack strategies targeting GNN are manipulating graph structure, which is not well-suited for social trust prediction tasks. In this article, we craft a novel black-box attack strategy, T-Attack, aimed at trust evaluation tasks, without tampering with the network structure of the specific trust prediction models. Specifically, a surrogate model is initially established to replicate trust prediction models based on GNN. The attack strategy on the surrogate model is formulated by adding unnoticed perturbations to user features related to network structure and manipulating the existing trust rating based on prior knowledge of social trust propagation, thereby avoiding a traditional attack against the GNN-based trust prediction model via modifying graph structure. By leveraging transferable attacks, our attack strategy can also distort the predictions of GNN-based trust prediction models. Through implementing extensive experiments in untargeted attack scenarios, we demonstrate the predictive performance of our crafted surrogate model and verify the effectiveness of the attack strategy on various GNN-based trust prediction models.