Adversarial Diffusion Attacks on Multimodal Traffic Prediction Models

Abstract

Abstract: 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 issu

Research Question

How do adversarial diffusion attacks impact the reasoning accuracy of multimodal traffic prediction models compared to standard graph convolutional networks?

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Provenance