Deep learning has evolved as a strong and efficient framework that can be applied to a
broad spectrum of complex learning problems which were difficult to solve using the …

Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based
methodological development in a broad range of domains. In this rapidly evolving field …

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 …

Deep learning has emerged as a strong and efficient framework that can be applied to a
broad spectrum of complex learning problems which were difficult to solve using the …

Abstract Over the years, Machine Learning models have been successfully employed on
neuroimaging data for accurately predicting brain age. Deviations from the healthy brain …

Deep neural networks and machine-learning algorithms are pervasively used in several
applications, ranging from computer vision to computer security. In most of these …

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 …

In recent years, artificial intelligence technologies have been widely used in computer
vision, natural language processing, automatic driving, and other fields. However, artificial …

Transferability captures the ability of an attack against a machine-learning model to be
effective against a different, potentially unknown, model. Empirical evidence for …

State-of-the-art classifiers have been shown to be largely vulnerable to adversarial
perturbations. One of the most effective strategies to improve robustness is adversarial …