Advances in adversarial attacks and defenses in computer vision: A survey
Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …
ability to accurately solve complex problems is employed in vision research to learn deep …
Opportunities and challenges in deep learning adversarial robustness: A survey
As we seek to deploy machine learning models beyond virtual and controlled domains, it is
critical to analyze not only the accuracy or the fact that it works most of the time, but if such a …
critical to analyze not only the accuracy or the fact that it works most of the time, but if such a …
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 …
EV AA - Exchange Vanishing Adversarial Attack on LiDAR Point Clouds in Autonomous Vehicles
In addition to red-green-blue (RGB) camera sensors, light detection and ranging (LiDAR)
plays an important role in autonomous vehicles (AVs) to perceive their surroundings. Deep …
plays an important role in autonomous vehicles (AVs) to perceive their surroundings. Deep …
Threat of adversarial attacks on deep learning in computer vision: A survey
Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …
computer vision, it has become the workhorse for applications ranging from self-driving cars …
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 …
Dirty road can attack: Security of deep learning based automated lane centering under {Physical-World} attack
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 study the …
also highly security and safety critical. In this work, we are the first to systematically study the …
Rethinking the trigger of backdoor attack
Backdoor attack intends to inject hidden backdoor into the deep neural networks (DNNs),
such that the prediction of the infected model will be maliciously changed if the hidden …
such that the prediction of the infected model will be maliciously changed if the hidden …
A survey on scenario-based testing for automated driving systems in high-fidelity simulation
Automated Driving Systems (ADSs) have seen rapid progress in recent years. To ensure the
safety and reliability of these systems, extensive testings are being conducted before their …
safety and reliability of these systems, extensive testings are being conducted before their …
Can we use arbitrary objects to attack lidar perception in autonomous driving?
As an effective way to acquire accurate information about the driving environment, LiDAR
perception has been widely adopted in autonomous driving. The state-of-the-art LiDAR …
perception has been widely adopted in autonomous driving. The state-of-the-art LiDAR …