Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting
Traffic forecasting by graph-based AI is a critical component of intelligent transportation systems, motivating security research on robustness to malicious sensor readings. We argue that prior robustness evaluations are largely shaped by unrealistic threat models and untargeted objectives, so both attacks and defenses must be revisited. We study a practical adversary with limited model knowledge and the ability to monitor and manipulate only a few road sensors. More importantly, practical attacks can be localized to specific links or routes, causing incorrect estimated arrival times or unnecessary rerouting while leaving the broader network largely unaffected. This targeted setting remai...
| Authors | Qingzhao Zhang |
|---|---|
| Primary category | cs.CR |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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