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 …
Deep learning adversarial attacks and defenses in autonomous vehicles: a systematic literature review from a safety perspective
Abstract The integration of Deep Learning (DL) algorithms in Autonomous Vehicles (AVs)
has revolutionized their precision in navigating various driving scenarios, ranging from anti …
has revolutionized their precision in navigating various driving scenarios, ranging from anti …
Physical adversarial attacks for camera-based smart systems: Current trends, categorization, applications, research challenges, and future outlook
Deep Neural Networks (DNNs) have shown impressive performance in computer vision
tasks; however, their vulnerability to adversarial attacks raises concerns regarding their …
tasks; however, their vulnerability to adversarial attacks raises concerns regarding their …
Evaluating the robustness of semantic segmentation for autonomous driving against real-world adversarial patch attacks
Deep learning and convolutional neural networks allow achieving impressive performance
in computer vision tasks, such as object detection and semantic segmentation (SS) …
in computer vision tasks, such as object detection and semantic segmentation (SS) …
On the real-world adversarial robustness of real-time semantic segmentation models for autonomous driving
The existence of real-world adversarial examples (RWAEs)(commonly in the form of
patches) poses a serious threat for the use of deep learning models in safety-critical …
patches) poses a serious threat for the use of deep learning models in safety-critical …
Adversarial patch attacks and defences in vision-based tasks: A survey
Adversarial attacks in deep learning models, especially for safety-critical systems, are
gaining more and more attention in recent years, due to the lack of trust in the security and …
gaining more and more attention in recent years, due to the lack of trust in the security and …
Improving feature stability during upsampling–spectral artifacts and the importance of spatial context
Pixel-wise predictions are required in a wide variety of tasks such as image restoration,
image segmentation, or disparity estimation. Common models involve several stages of data …
image segmentation, or disparity estimation. Common models involve several stages of data …
Physical 3D adversarial attacks against monocular depth estimation in autonomous driving
Deep learning-based monocular depth estimation (MDE) extensively applied in autonomous
driving is known to be vulnerable to adversarial attacks. Previous physical attacks against …
driving is known to be vulnerable to adversarial attacks. Previous physical attacks against …
[HTML][HTML] A qualitative AI security risk assessment of autonomous vehicles
This paper systematically analyzes the security risks associated with artificial intelligence
(AI) components in autonomous vehicles (AVs). Given the increasing reliance on AI for …
(AI) components in autonomous vehicles (AVs). Given the increasing reliance on AI for …
Saam: Stealthy adversarial attack on monocular depth estimation
Monocular depth estimation (MDE) is an important task in scene understanding, and
significant improvements in its performance have been witnessed with the utilization of …
significant improvements in its performance have been witnessed with the utilization of …