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

Deep learning (DL) has shown great success in many human-related tasks, which has led to
its adoption in many computer vision based applications, such as security surveillance …

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

This paper investigates strategies that defend against adversarial-example attacks on image-
classification systems by transforming the inputs before feeding them to the system …

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 …

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

This paper addresses the problem of semi-supervised transfer learning with limited cross-
modality data in remote sensing. A large amount of multi-modal earth observation images …

As we seek to deploy machine learning models beyond virtual and controlled domains, it is
critical to analyze not only the accuracy or the fact that it works most of the time, but if such a …

Crafting adversarial examples has become an important technique to evaluate the
robustness of deep neural networks (DNNs). However, most existing works focus on …

Despite the improved accuracy of deep neural networks, the discovery of adversarial
examples has raised serious safety concerns. Most existing approaches for crafting …