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STAA: Spatio-Temporal Adversarial Attack Based on Attention Mechanism

Published: 03 October 2023 Publication History

Abstract

Traffic prediction models play a crucial role in estimating future traffic conditions based on historical data and road network structures. However, these models are vulnerable to adversarial attacks, wherein malicious attackers can manipulate key nodes to generate misleading traffic predictions with significant deviations. Existing studies have assessed the robustness of traffic prediction models by estimating gradients to select victim nodes and subsequently conducting adversarial attacks. Nonetheless, these attack methods fail to consider the temporal characteristics of the traffic data itself and overlook the private constraints in federated learning-based traffic prediction models. To address these problems, we propose a novel adversarial attack method for traffic prediction models based on the attention mechanism. Our algorithm leverages the attention mechanism to select victim nodes and subsequently executes spatio-temporal adversarial attacks. Experimental results demonstrate that our method achieves superior attack effectiveness for the same attack cost, leading to larger prediction errors in the traffic prediction model.

References

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Zhu L, Feng K, Pu Z, Adversarial diffusion attacks on graph-based traffic prediction models[J]. arXiv preprint arXiv:2104.09369, 2021.
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Liu F, Liu H, Jiang W. Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models[J]. arXiv preprint arXiv:2210.02447, 2022.
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Lan S, Ma Y, Huang W, Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting[C]//International Conference on Machine Learning. PMLR, 2022: 11906-11917.
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Kurakin A, Goodfellow I, Bengio S. Adversarial machine learning at scale[J]. arXiv preprint arXiv:1611.01236, 2016.

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        CCRIS '23: Proceedings of the 2023 4th International Conference on Control, Robotics and Intelligent System
        August 2023
        215 pages
        ISBN:9798400708190
        DOI:10.1145/3622896
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 03 October 2023

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        Author Tags

        1. Adversarial Attack
        2. Spatiotemporal Prediction
        3. Traffic Prediction

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