PracticalAdversarialAttacksonSpatiotemporal TrafficForecastingModels
–Neural Information Processing Systems
However, existing methods assume a reliable and unbiased forecasting environment, which isnot always available inthe wild. Inthis work, we investigate the vulnerability ofspatiotemporal trafficforecasting models andpropose apractical adversarial spatiotemporal attack framework.
Neural Information Processing Systems
Feb-9-2026, 23:42:52 GMT
- Country:
- Asia
- China > Guangdong Province
- Guangzhou (0.05)
- Macao (0.04)
- China > Guangdong Province
- Europe
- Sweden > Stockholm
- Stockholm (0.04)
- United Kingdom > England
- Greater London > London (0.04)
- Sweden > Stockholm
- North America
- Canada
- Alberta > Census Division No. 15
- Improvement District No. 9 > Banff (0.04)
- British Columbia > Metro Vancouver Regional District
- Vancouver (0.05)
- Quebec > Montreal (0.04)
- Alberta > Census Division No. 15
- United States
- California (0.04)
- Hawaii > Honolulu County
- Honolulu (0.04)
- Canada
- Oceania > Australia
- Queensland (0.04)
- Asia
- Genre:
- Research Report (0.68)
- Industry:
- Information Technology > Security & Privacy (0.33)
- Transportation (0.48)
- Technology: