The deployment of the fifth-generation (5G) wireless networks in Internet of Everything (IoE)
applications and future networks (eg, sixth-generation (6G) networks) has raised a number …

Recent studies have shown that DNNs can be compromised by backdoor attacks crafted at
training time. A backdoor attack installs a backdoor into the victim model by injecting a …

Deep neural networks (DNNs) are known vulnerable to backdoor attacks, a training time
attack that injects a trigger pattern into a small proportion of training data so as to control the …

Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by
imperceptible perturbations. A range of defense techniques have been proposed to improve …

Deep neural networks (DNNs) have become popular for medical image analysis tasks like
cancer diagnosis and lesion detection. However, a recent study demonstrates that medical …

Video action recognition is one of the representative tasks for video understanding. Over the
last decade, we have witnessed great advancements in video action recognition thanks to …

Skip connections are an essential component of current state-of-the-art deep neural
networks (DNNs) such as ResNet, WideResNet, DenseNet, and ResNeXt. Despite their …

Deep neural networks (DNNs) are vulnerable to backdoor attacks which can hide backdoor
triggers in DNNs by poisoning training data. A backdoored model behaves normally on …

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. Existing
works have mostly focused on either digital adversarial examples created via small and …

Vision transformers (ViTs) process input images as sequences of patches via self-attention;
a radically different architecture than convolutional neural networks (CNNs). This makes it …