  Spatiotemporal forecasting plays an essential role in various applications in
intelligent transportation systems (ITS), such as route planning, navigation,
and traffic control and management. Deep Spatiotemporal graph neural networks
(GNNs), which capture both spatial and temporal patterns, have achieved great
success in traffic forecasting applications. Understanding how GNNs-based
forecasting work and the vulnerability and robustness of these models becomes
critical to real-world applications. For example, if spatiotemporal GNNs are
vulnerable in real-world traffic prediction applications, a hacker can easily
manipulate the results and cause serious traffic congestion and even a
city-scale breakdown. However, despite that recent studies have demonstrated
that deep neural networks (DNNs) are vulnerable to carefully designed
perturbations in multiple domains like objection classification and graph
representation, current adversarial works cannot be directly applied to
spatiotemporal forecasting due to the causal nature and spatiotemporal
mechanisms in forecasting models. To fill this gap, in this paper we design
Spatially Focused Attack (SFA) to break spatiotemporal GNNs by attacking a
single vertex. To achieve this, we first propose the inverse estimation to
address the causality issue; then, we apply genetic algorithms with a universal
attack method as the evaluation function to locate the weakest vertex; finally,
perturbations are generated by solving an inverse estimation-based optimization
problem. We conduct experiments on real-world traffic data and our results show
that perturbations in one vertex designed by SA can be diffused into a large
part of the graph.
