From the 1 of 12 linked papers with an AI index.
12 papers
Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting
Qingzhao Zhang
The paper examines realistic, targeted adversarial attacks on graph-based traffic forecasting models and proposes a physics‑informed detection‑based defense that improves robustnes…
Banshee: Target Switch Attacks on Gimbal-Stabilized Visual Tracking Systems via Acoustic Injection
Jiarui Li, Joseph Brewington, Qingzhao Zhang +1
Gimbal-stabilized visual tracking is critical for modern autonomous systems such as Unmanned Aerial Vehicles (UAVs). While prior work shows acoustic signals can disturb gimbal inte…
Adversarial Trust Poisoning in Vehicular Collaborative Perception
Yutong Liu, Chenyi Wang, Ming F. Li +1
Collaborative perception (CP) enables connected and autonomous vehicles to share sensor data and jointly reason about their environment. To defend against adversaries that fabricat…
CLAP: Contrastive Latent-space Prompt Optimization for End-to-end Autonomous Driving
Ruiyang Zhu, Yuehan He, Boyuan Zheng +4
End-to-end autonomous driving systems powered by Vision-Language-Action (VLA) models achieve strong performance on common driving scenarios, yet remain brittle in rare but safety-c…
Still Camouflage, Moving Illusion: View-Induced Trajectory Manipulation in Autonomous Driving
Shuo Ju, Qingzhao Zhang, Huashan Chen +6
Existing physical adversarial attacks on vision-based autonomous driving induce time-evolving perception errors, including biased object tracking or trajectory prediction, through…
From Stealthy Data Fabrication to Unsafe Driving: Realistic Scenario Attacks on Collaborative Perception
Qingzhao Zhang, Runting Zhang, Z. Morley Mao
Collaborative perception allows connected and autonomous vehicles (CAVs) to improve perception by sharing sensory data, but it also introduces security risks from manipulated input…