226 citations · 301 across the 14 of their papers we have counts for
14 papers
T2I-Based Physical-World Appearance Attack against Traffic Sign Recognition Systems in Autonomous Driving
Chen Ma, Ningfei Wang, Junhao Zheng +4
Traffic Sign Recognition (TSR) systems play a critical role in Autonomous Driving (AD) systems, enabling real-time detection of road signs, such as STOP and speed limit signs. Whil…
FlyTrap: Physical Distance-Pulling Attack Towards Camera-based Autonomous Target Tracking Systems
Shaoyuan Xie, Mohamad Habib Fakih, Junchi Lu +6
Autonomous Target Tracking (ATT) systems, especially ATT drones, are widely used in applications such as surveillance, border control, and law enforcement, while also being misused…
Revisiting Physical-World Adversarial Attack on Traffic Sign Recognition: A Commercial Systems Perspective
Ningfei Wang, Shaoyuan Xie, Takami Sato +3
Traffic Sign Recognition (TSR) is crucial for safe and correct driving automation. Recent works revealed a general vulnerability of TSR models to physical-world adversarial attacks…
SlowPerception: Physical-World Latency Attack against Visual Perception in Autonomous Driving
Chen Ma, Ningfei Wang, Zhengyu Zhao +2
Autonomous Driving (AD) systems critically depend on visual perception for real-time object detection and multiple object tracking (MOT) to ensure safe driving. However, high laten…
ControlLoc: Physical-World Hijacking Attack on Visual Perception in Autonomous Driving
Chen Ma, Ningfei Wang, Zhengyu Zhao +3
Recent research in adversarial machine learning has focused on visual perception in Autonomous Driving (AD) and has shown that printed adversarial patches can attack object detecto…
Towards Robustness Analysis of E-Commerce Ranking System
Ningfei Wang, Yupin Huang, Han Cheng +3
Information retrieval (IR) is a pivotal component in various applications. Recent advances in machine learning (ML) have enabled the integration of ML algorithms into IR, particula…
SlowTrack: Increasing the Latency of Camera-based Perception in Autonomous Driving Using Adversarial Examples
Chen Ma, Ningfei Wang, Qi Alfred Chen +1
In Autonomous Driving (AD), real-time perception is a critical component responsible for detecting surrounding objects to ensure safe driving. While researchers have extensively ex…
Does Physical Adversarial Example Really Matter to Autonomous Driving? Towards System-Level Effect of Adversarial Object Evasion Attack
Ningfei Wang, Yunpeng Luo, Takami Sato +2
In autonomous driving (AD), accurate perception is indispensable to achieving safe and secure driving. Due to its safety-criticality, the security of AD perception has been widely…
Intriguing Properties of Diffusion Models: An Empirical Study of the Natural Attack Capability in Text-to-Image Generative Models
Takami Sato, Justin Yue, Nanze Chen +2
Denoising probabilistic diffusion models have shown breakthrough performance to generate more photo-realistic images or human-level illustrations than the prior models such as GANs…
SoK: On the Semantic AI Security in Autonomous Driving
Junjie Shen, Ningfei Wang, Ziwen Wan +10
Autonomous Driving (AD) systems rely on AI components to make safety and correct driving decisions. Unfortunately, today's AI algorithms are known to be generally vulnerable to adv…
Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks
Yulong Cao*, Ningfei Wang*, Chaowei Xiao* +6
In Autonomous Driving (AD) systems, perception is both security and safety critical. Despite various prior studies on its security issues, all of them only consider attacks on came…
Dirty Road Can Attack: Security of Deep Learning based Automated Lane Centering under Physical-World Attack
Takami Sato, Junjie Shen, Ningfei Wang +3
Automated Lane Centering (ALC) systems are convenient and widely deployed today, but also highly security and safety critical. In this work, we are the first to systematically stud…
Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack
Takami Sato, Junjie Shen, Ningfei Wang +3
Lane-Keeping Assistance System (LKAS) is convenient and widely available today, but also extremely security and safety critical. In this work, we design and implement the first sys…
Interpretable Deep Learning under Fire
Xinyang Zhang, Ningfei Wang, Hua Shen +3
Providing explanations for deep neural network (DNN) models is crucial for their use in security-sensitive domains. A plethora of interpretation models have been proposed to help u…