Adversarial attacks and defenses in machine learning and deep neural network (DNN) have
been gaining significant attention due to the rapidly growing applications of deep learning in …

Detecting the salient objects in a remote sensing image has wide applications. Many
existing deep learning methods have been proposed for Salient Object Detection (SOD) in …

While deep neural networks (DNNs) have revolutionized many fields, their fragility to
carefully designed adversarial attacks impedes the usage of DNNs in safety-critical …

Dataset distillation is an advanced technique aimed at compressing datasets into
significantly smaller counterparts, while preserving formidable training performance …

Deep neural networks have demonstrated susceptibility to adversarial attacks. Adversarial
defense techniques often focus on one-shot setting to maintain robustness against attack …

With the widespread adoption of edge computing, compressing deep neural networks
(DNNs) via knowledge distillation (KD) has emerged as a popular technique for resource …

Despite providing high-performance solutions for computer vision tasks, the deep neural
network (DNN) model has been proved to be extremely vulnerable to adversarial attacks …

This paper addresses the tradeoff between standard accuracy on clean examples and
robustness against adversarial examples in deep neural networks (DNNs). Although …

Training deep neural networks (DNNs) with altered data, known as adversarial training, is
essential for improving their robustness. A significant challenge emerges as the robustness …

Adversarial attacks pose a huge challenge to the deployment of deep neural networks
(DNNs) in security-sensitive applications. Adversarial defense methods are developed to …