Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models
Authors: Fan LIU, Hao Liu, Wenzhao Jiang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two real-world datasets show that the proposed two-step framework achieves up to 67.8% performance degradation on various advanced spatiotemporal forecasting models. |
| Researcher Affiliation | Academia | Fan LIU AI Thrust&RBM, The Hong Kong University of Science and Technology (Guangzhou) fliu236@connect.hkust-gz.edu.cn&liufan@ust.hk Hao LIU AI Thrust, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou HKUST Fok Ying Tung Research Institute CSE, The Hong Kong University of Science and Technology liuh@ust.hk Wenzhao Jiang AI Thrust, The Hong Kong University of Science and Technology (Guangzhou) wjiang431@connect.hkust-gz.edu.cn |
| Pseudocode | Yes | Algorithm 1: Adversarial spatiotemporal attack under the grey-box setting |
| Open Source Code | Yes | Our code is available in https://github.com/usail-hkust/Adv-ST. |
| Open Datasets | Yes | Datasets. We use two popular real-world datasets to demonstrate the effectiveness of the proposed adversarial attack framework. (1) PEMS-BAY [18] traffic dataset is derived from the California Transportation Agencies (Cal Trans) Performance Measurement System (Pe MS) ranging from January 1, 2017 to May 31, 2017. ... (2) METR-LA [19] is a traffic speed dataset collected from 207 Los Angeles County roadway sensors. |
| Dataset Splits | Yes | For evaluation, all datasets are chronologically ordered, we take the first 70% for training, the following 10% for validation, and the rest 20% for testing. |
| Hardware Specification | Yes | All experiments are implemented with Py Torch and performed on a Linux server with 4 RTX 3090 GPUs. |
| Software Dependencies | No | The paper states "All experiments are implemented with Py Torch" but does not specify the version number of PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Implementation details. All experiments are implemented with Py Torch and performed on a Linux server with 4 RTX 3090 GPUs.The traffic speed is normalized to [0, 1]. The input length T and output length τ are set to 12. We select 10% nodes from the whole nodes as the victim nodes, and ε is set to 0.5. The batch size γ is set to 64. The iteration K is set to 5, and the step size α is set to 0.1. |