Adversarial Attacks in Graph Neural Networks

Adversarial attacks pose significant security threats to machine learning models, including graph neural networks (GNNs), by making small perturbations to input data that cause incorrect predictions. This tutorial covers implementing four types of adversarial attacks (FGSM, PGD, Carlini & Wagner, DeepFool) on GNNs using the PyTorch-Geometric library and the Cora dataset. It demonstrates the impact of these attacks on model accuracy and suggests mixed training with clean and perturbed data as a defense strategy.