Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …

Abstract Machine learning (ML), especially deep neural networks (DNNs) have been widely
used in various applications, including several safety-critical ones (eg autonomous driving) …

Currently, machine learning (ML) techniques are at the heart of smart cyber-physical
systems (CPSs) and Internet-of-Things (loT). This article discusses various challenges and …

The problem of adversarial examples, evasion attacks on machine learning classifiers, has
proven extremely difficult to solve. This is true even in the black-box threat model, as is the …

Machine learning, in particular deep learning, is being used in almost all the aspects of life
to facilitate humans, specifically in mobile and Internet of Things (IoT)-based applications …

In the Machine Learning era, Deep Neural Networks (DNNs) have taken the spotlight, due to
their unmatchable performance in several applications, such as image processing, computer …

Adversarial attacks, which attract explosive rese-arch attention in recent years, have
achieved fantastic success in fooling neural networks, especially for image-classification …

Adversarial examples have emerged as a significant threat to machine learning algorithms,
especially to the convolutional neural networks (CNNs). In this paper, we propose two …

Due to the excessive use of cloud-based machine learning (ML) services, the smart cyber-
physical systems (CPS) are increasingly becoming vulnerable to black-box attacks on their …

Most of the data manipulation attacks on deep neural networks (DNNs) during the training
stage introduce a perceptible noise that can be catered by preprocessing during inference …