Adversarial attacks can compromise the robustness of real-world detection models.
However, evaluating these models under real-world conditions poses challenges due to …

Deep neural networks (DNNs) are found to be vulnerable against adversarial examples,
which are carefully crafted inputs with a small magnitude of perturbation aiming to induce …

Deep Neural Networks (DNNs) have tremendous potential in advancing the vision for self-
driving cars. However, the security of DNN models in this context leads to major safety …

Adversarial attacks can make deep neural network (DNN) models predict incorrect output
labels, such as misclassified traffic signs, for autonomous vehicle (AV) perception modules …

The evolution of automotive technology will eventually permit the automated driving system
on the vehicle to handle all circumstances. Human occupants will be just passengers. This …

Deep neural network model is the core technology for sensors of the autonomous driving
platform to perceive the external environment. Recent research have shown that it has a …

Segmentation models have been found to be vulnerable to targeted/non-targeted
adversarial attacks. However, damaged predictions make it easy to unearth an attack. In this …

Video object segmentation has been applied to various computer vision tasks, such as video
editing, autonomous driving, and human-robot interaction. However, the methods based on …

In Autonomous Driving (AD) systems, perception is both security and safety-critical. Among
different attacks on AD perception, object-hiding adversarial attack is one of the most critical …

Abstract Although Deep neural networks (DNNs) are being pervasively used in vision-based
autonomous driving systems, they are found vulnerable to adversarial attacks where small …