Recent advances in attention-based networks have shown that Vision Transformers can
achieve state-of-the-art or near state-of-the-art results on many image classification tasks …

The field of adversarial machine learning has experienced a near exponential growth in the
amount of papers being produced since 2018. This massive information output has yet to be …

Abstract Although Convolutional Neural Networks (CNNs) achieve high accuracy on image
recognition tasks, they lack robustness against realistic corruptions and fail catastrophically …

We explore existing political commitments by states regarding the development and use of
lethal autonomous weapon systems. We carry out two background reviewing efforts, the first …

Abstract Models built using machine learning algorithms (MLA) have been used to handle
numerous challenges in various farming systems. In this study we wanted to propose an …

Adversarial machine learning defenses have primarily been focused on mitigating static,
white-box attacks. However, it remains an open question whether such defenses are robust …

Universal adversarial attacks, which hinder most deep neural network (DNN) tasks using
only a single perturbation called universal adversarial perturbation (UAP), are a realistic …

Adversarial examples that can fool neural network classifiers have attracted much attention.
Existing approaches to detect adversarial examples leverage a supervised scheme in …

Recent advances in adversarial machine learning have shown that defenses considered to
be robust are actually susceptible to adversarial attacks which are specifically customized to …

Randomized smoothing has become a leading method for achieving certified robustness in
deep classifiers against l_ {p}-norm adversarial perturbations. Current approaches for …