The success of machine learning is fueled by the increasing availability of computing power
and large training datasets. The training data is used to learn new models or update existing …

Abstract Adversarial Machine Learning (AML) is a recently introduced technique, aiming to
deceive Machine Learning (ML) models by providing falsified inputs to render those models …

Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …

Warning: this paper contains content that may be inappropriate or offensive. As generative
models become available for public use in various applications, testing and analyzing …

Diffusion-based purification defenses leverage diffusion models to remove crafted
perturbations of adversarial examples and achieve state-of-the-art robustness. Recent …

The problem of malicious software (malware) detection and classification is a complex task,
and there is no perfect approach. There is still a lot of work to be done. Unlike most other …

The increasing digitization and datification of all aspects of people's daily life, and the
consequent growth in the use of personal data, are increasingly challenging the current …

Hundreds of defenses have been proposed to make deep neural networks robust against
minimal (adversarial) input perturbations. However, only a handful of these defenses held …

Spiking neural networks (SNNs) provide an energy-efficient alternative to a variety of
artificial neural network (ANN) based AI applications. As the progress in neuromorphic …

Adversarial examples are commonly created by solving a constrained optimization problem,
typically using sign-based methods like Fast Gradient Sign Method (FGSM). These attacks …