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

Many machine learning algorithms are vulnerable to almost imperceptible perturbations of
their inputs. So far it was unclear how much risk adversarial perturbations carry for the safety …

Machine learning (ML) models may be deemed confidential due to their sensitive training
data, commercial value, or use in security applications. Increasingly often, confidential ML …

Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML
techniques unlock the potential of IoT with intelligence, and IoT applications increasingly …

In security-sensitive applications, the success of machine learning depends on a thorough
vetting of their resistance to adversarial data. In one pertinent, well-motivated attack …

Machine learning (ML) applications are increasingly prevalent. Protecting the confidentiality
of ML models becomes paramount for two reasons:(a) a model can be a business …

Advances in machine learning (ML) in recent years have enabled a dizzying array of
applications such as data analytics, autonomous systems, and security diagnostics. ML is …

We study the problem of generating adversarial examples in a black-box setting in which
only loss-oracle access to a model is available. We introduce a framework that conceptually …

Advances in machine learning (ML) in recent years have enabled a dizzying array of
applications such as data analytics, autonomous systems, and security diagnostics. ML is …

In this article, we review previous work on biometric security under a recent framework
proposed in the field of adversarial machine learning. This allows us to highlight novel …