Invisible for both camera and lidar: Security of multi-sensor fusion based perception in autonomous driving under physical-world attacks
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 camera-or …
various prior studies on its security issues, all of them only consider attacks on camera-or …
Physical attack on monocular depth estimation with optimal adversarial patches
Deep learning has substantially boosted the performance of Monocular Depth Estimation
(MDE), a critical component in fully vision-based autonomous driving (AD) systems (eg …
(MDE), a critical component in fully vision-based autonomous driving (AD) systems (eg …
Does physical adversarial example really matter to autonomous driving? towards system-level effect of adversarial object evasion attack
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 …
secure driving. Due to its safety-criticality, the security of AD perception has been widely …
Slowtrack: Increasing the latency of camera-based perception in autonomous driving using adversarial examples
In Autonomous Driving (AD), real-time perception is a critical component responsible for
detecting surrounding objects to ensure safe driving. While researchers have extensively …
detecting surrounding objects to ensure safe driving. While researchers have extensively …
Wip: Towards the practicality of the adversarial attack on object tracking in autonomous driving
Recently, adversarial examples against object detection have been widely studied.
However, it is difficult for these attacks to have an impact on visual perception in …
However, it is difficult for these attacks to have an impact on visual perception in …
Lateral-direction localization attack in high-level autonomous driving: Domain-specific defense opportunity via lane detection
Localization in high-level Autonomous Driving (AD) systems is highly security critical.
Recently, researchers found that state-of-the-art Multi-Sensor Fusion (MSF) based …
Recently, researchers found that state-of-the-art Multi-Sensor Fusion (MSF) based …
End-to-end uncertainty-based mitigation of adversarial attacks to automated lane centering
In the development of advanced driver-assistance systems (ADAS) and autonomous
vehicles, machine learning techniques that are based on deep neural networks (DNNs) …
vehicles, machine learning techniques that are based on deep neural networks (DNNs) …
APARATE: Adaptive adversarial patch for CNN-based monocular depth estimation for autonomous navigation
In recent times, monocular depth estimation (MDE) has experienced significant
advancements in performance, largely attributed to the integration of innovative …
advancements in performance, largely attributed to the integration of innovative …
Bounding perception neural network uncertainty for safe control of autonomous systems
Future autonomous systems will rely on advanced sensors and deep neural networks for
perceiving the environment, and then utilize the perceived information for system planning …
perceiving the environment, and then utilize the perceived information for system planning …
[PDF][PDF] WIP: Deployability improvement, stealthiness user study, and safety impact assessment on real vehicle for dirty road patch attack
Automated Lane Centering (ALC) systems are convenient and widely deployed today, but
also highly security and safety critical. Recently, Dirty Road Patch (DRP) attack is proposed …
also highly security and safety critical. Recently, Dirty Road Patch (DRP) attack is proposed …