  Real-time traffic prediction models play a pivotal role in smart mobility
systems and have been widely used in route guidance, emerging mobility
services, and advanced traffic management systems. With the availability of
massive traffic data, neural network-based deep learning methods, especially
the graph convolutional networks (GCN) have demonstrated outstanding
performance in mining spatio-temporal information and achieving high prediction
accuracy. Recent studies reveal the vulnerability of GCN under adversarial
attacks, while there is a lack of studies to understand the vulnerability
issues of the GCN-based traffic prediction models. Given this, this paper
proposes a new task -- diffusion attack, to study the robustness of GCN-based
traffic prediction models. The diffusion attack aims to select and attack a
small set of nodes to degrade the performance of the entire prediction model.
To conduct the diffusion attack, we propose a novel attack algorithm, which
consists of two major components: 1) approximating the gradient of the
black-box prediction model with Simultaneous Perturbation Stochastic
Approximation (SPSA); 2) adapting the knapsack greedy algorithm to select the
attack nodes. The proposed algorithm is examined with three GCN-based traffic
prediction models: St-Gcn, T-Gcn, and A3t-Gcn on two cities. The proposed
algorithm demonstrates high efficiency in the adversarial attack tasks under
various scenarios, and it can still generate adversarial samples under the drop
regularization such as DropOut, DropNode, and DropEdge. The research outcomes
could help to improve the robustness of the GCN-based traffic prediction models
and better protect the smart mobility systems. Our code is available at
https://github.com/LYZ98/Adversarial-Diffusion-Attacks-on-Graph-based-Traffic-Prediction-Models
