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Adversarial Diffusion Attacks on Graph-Based Traffic Prediction Models
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DOI：10.1109/JIOT.2023.3290401.png)
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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 graph convolutional networks (GCNs) 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 article proposes a new task-diffusion attack, to study the robustness of GCN-based traffic prediction models. The diffusion attack aims to select and simulate attacks on a small set of nodes to degrade the performance of the traffic prediction models, and it can be used to examine vulnerabilities of the traffic prediction models. 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) and 2) adapting the knapsack greedy algorithm to select the attack nodes. The proposed algorithm is examined with three GCN-based traffic prediction models: 1) ST-GCN; 2) T-GCN; and 3) A3T-GCN on four cities. The proposed algorithm demonstrates high efficiency in adversarial attack tasks under various scenarios, and it can still generate adversarial samples under the drop regularization, such as DROP OUT, DROP NODE, and DROP EDGE. The research outcomes could help to improve the robustness of the GCN-based traffic prediction models and better protect the smart mobility systems.
Keyword:
Adversarial attack
deep learning
graph convolutional network (GCN)
intelligent transportation systems (ITSs)
traffic prediction
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
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