25 citations · 94 across the 15 of their papers we have counts for
16 papers
Trapped by Their Own Light: Deployable and Stealth Retroreflective Patch Attacks on Traffic Sign Recognition Systems
Go Tsuruoka, Takami Sato, Qi Alfred Chen +4
Traffic sign recognition plays a critical role in ensuring safe and efficient transportation of autonomous vehicles but remain vulnerable to adversarial attacks using stickers or l…
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…
Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-based Decision-Making Systems
Ruochen Jiao, Shaoyuan Xie, Justin Yue +5
Large Language Models (LLMs) have shown significant promise in real-world decision-making tasks for embodied artificial intelligence, especially when fine-tuned to leverage their i…
Invisible Reflections: Leveraging Infrared Laser Reflections to Target Traffic Sign Perception
Takami Sato, Sri Hrushikesh Varma Bhupathiraju, Michael Clifford +3
All vehicles must follow the rules that govern traffic behavior, regardless of whether the vehicles are human-driven or Connected Autonomous Vehicles (CAVs). Road signs indicate lo…
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…
Learning Representation for Anomaly Detection of Vehicle Trajectories
Ruochen Jiao, Juyang Bai, Xiangguo Liu +4
Predicting the future trajectories of surrounding vehicles based on their history trajectories is a critical task in autonomous driving. However, when small crafted perturbations a…
LiDAR Spoofing Meets the New-Gen: Capability Improvements, Broken Assumptions, and New Attack Strategies
Takami Sato, Yuki Hayakawa, Ryo Suzuki +3
LiDAR (Light Detection And Ranging) is an indispensable sensor for precise long- and wide-range 3D sensing, which directly benefited the recent rapid deployment of autonomous drivi…
Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction
Ruochen Jiao, Xiangguo Liu, Takami Sato +2
Predicting the trajectories of surrounding objects is a critical task for self-driving vehicles and many other autonomous systems. Recent works demonstrate that adversarial attacks…
Towards Driving-Oriented Metric for Lane Detection Models
Takami Sato, Qi Alfred Chen
After the 2017 TuSimple Lane Detection Challenge, its dataset and evaluation based on accuracy and F1 score have become the de facto standard to measure the performance of lane det…
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…
On Robustness of Lane Detection Models to Physical-World Adversarial Attacks in Autonomous Driving
Takami Sato, Qi Alfred Chen
After the 2017 TuSimple Lane Detection Challenge, its evaluation based on accuracy and F1 score has become the de facto standard to measure the performance of lane detection method…
End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering
Ruochen Jiao, Hengyi Liang, Takami Sato +3
In the development of advanced driver-assistance systems (ADAS) and autonomous vehicles, machine learning techniques that are based on deep neural networks (DNNs) have been widely…
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…