Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

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 …

Adversarial purification refers to a class of defense methods that remove adversarial
perturbations using a generative model. These methods do not make assumptions on the …

The study on improving the robustness of deep neural networks against adversarial
examples grows rapidly in recent years. Among them, adversarial training is the most …

Analog in-memory computing—a promising approach for energy-efficient acceleration of
deep learning workloads—computes matrix-vector multiplications but only approximately …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

The current industrial scenario has witnessed the application of several artificial intelligence-
based technologies for mining and processing IoMT-based big data. An emerging …

Abstract Security of modern Deep Neural Networks (DNNs) is under severe scrutiny as the
deployment of these models become widespread in many intelligence-based applications …

Recent advancements in Deep Neural Networks (DNNs) have enabled widespread
deployment in multiple security-sensitive domains. The need for resource-intensive training …

Current state-of-the-art object recognition models are largely based on convolutional neural
network (CNN) architectures, which are loosely inspired by the primate visual system …