Sixth-generation (6G) mobile networks will have to cope with diverse threats on a space-air-
ground integrated network environment, novel technologies, and an accessible user …

Deep neural network-based image classifications are vulnerable to adversarial
perturbations. The image classifications can be easily fooled by adding artificial small and …

Deep learning and convolutional neural networks allow achieving impressive performance
in computer vision tasks, such as object detection and semantic segmentation (SS) …

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 …

Many machine learning approaches have been successfully applied to
electroencephalogram (EEG) based brain–computer interfaces (BCIs). Most existing …

Deep neural networks (DNNs) for highly automated driving are often trained on a large and
diverse dataset, and evaluation metrics are reported usually on a per-frame basis. However …

The robustness of deep neural networks is an increasingly essential issue as they become
more and more prevalent in several real-world applications like autonomous vehicles. If …

Research and development of electroencephalogram (EEG) based brain-computer
interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain …

State-of-the-art deep neural networks have been shown to be extremely powerful in a variety
of perceptual tasks like semantic segmentation. However, these networks are vulnerable to …

In supervised learning, a deep neural network's performance is measured using ground
truth data. In semantic segmentation, ground truth data is sparse, requires an expensive …