49 citations · 54 across the 17 of their papers we have counts for
8 papers · 1 filter
Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting
Qingzhao Zhang
Traffic forecasting by graph-based AI is a critical component of intelligent transportation systems, motivating security research on robustness to malicious sensor readings. We arg…
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…
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…
SoK: How Sensor Attacks Disrupt Autonomous Vehicles: An End-to-end Analysis, Challenges, and Missed Threats
Qingzhao Zhang, Shaocheng Luo, Z. Morley Mao +2
Autonomous vehicles, including self-driving cars, ground robots, and drones, rely on multi-modal sensor pipelines for safe operation, yet remain vulnerable to adversarial sensor at…
LLM Safeguard is a Double-Edged Sword: Exploiting False Positives for Denial-of-Service Attacks
Qingzhao Zhang, Ziyang Xiong, Z. Morley Mao
Safety is a paramount concern for large language models (LLMs) in open deployment, motivating the development of safeguard methods that enforce ethical and responsible use through…
On Data Fabrication in Collaborative Vehicular Perception: Attacks and Countermeasures
Qingzhao Zhang, Shuowei Jin, Ruiyang Zhu +4
Collaborative perception, which greatly enhances the sensing capability of connected and autonomous vehicles (CAVs) by incorporating data from external resources, also brings forth…
What Would Trojans Do? Exploiting Partial-Information Vulnerabilities in Autonomous Vehicle Sensing
R. Spencer Hallyburton, Qingzhao Zhang, Z. Morley Mao +2
Safety-critical sensors in autonomous vehicles (AVs) form an essential part of the vehicle's trusted computing base (TCB), yet they are highly susceptible to attacks. Alarmingly, T…