Deep neural networks are at the forefront of machine learning research. However, despite
achieving impressive performance on complex tasks, they can be very sensitive: Small …

Machine Learning (ML) models are susceptible to adversarial samples that appear as
normal samples but have some imperceptible noise added to them with the intention of …

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

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 …

To cope with the increasing variability and sophistication of modern attacks, machine
learning has been widely adopted as a statistically-sound tool for malware detection …

Pattern classification systems are commonly used in adversarial applications, like biometric
authentication, network intrusion detection, and spam filtering, in which data can be …

Machine learning has been pervasively used in a wide range of applications due to its
technical breakthroughs in recent years. It has demonstrated significant success in dealing …

In adversarial classification tasks like spam filtering and intrusion detection, malicious
adversaries may manipulate data to thwart the outcome of an automatic analysis. Thus …

Cardinality estimation is a fundamental but long unresolved problem in query optimization.
Recently, multiple papers from different research groups consistently report that learned …

Recently, many studies have demonstrated deep neural network (DNN) classifiers can be
fooled by the adversarial example, which is crafted via introducing some perturbations into …