Connected and autonomous vehicles (CAV aka driverless cars) offset human response for
transportation infrastructure, enhancing traffic efficiency, travel leisure, and road safety …

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 …

Vehicles have increasingly deployed object detectors to perceive running conditions, and
deep learning networks have been widely adopted by those detectors. Growing neural …

Machine learning has made significant progress in image recognition, natural language
processing, and autonomous driving. However, the generation of adversarial examples has …

Deep learning (DL) has shown great success in many human-related tasks, which has led to
its adoption in many computer vision based applications, such as security surveillance …

Deep learning solutions are vulnerable to adversarial perturbations and can lead a" frog"
image to be misclassified as a" deer" or random pattern into" guitar". Adversarial attack …

Abstract Despite their performance, Artificial Neural Networks are not reliable enough for
most of industrial applications. They are sensitive to noises, rotations, blurs and adversarial …

Applications such as autonomous vehicles and medical screening use deep learning
models to localize and identify hundreds of objects in a single frame. In the past, it has been …

Existing neural networks for computer vision tasks are vulnerable to adversarial attacks:
adding imperceptible perturbations to the input images can fool these models into making a …

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 …