Over the past two decades, biometric recognition has exploded into a plethora of different
applications around the globe. This proliferation can be attributed to the high levels of …

Deep neural networks (DNNs) have transformed several artificial intelligence research
areas including computer vision, speech recognition, and natural language processing …

Recently Adversarial Examples (AEs) that deceive deep learning models have been a topic
of intense research interest. Compared with the AEs in the digital space, the physical …

Over the last few years, the phenomenon of adversarial examples—maliciously constructed
inputs that fool trained machine learning models—has captured the attention of the research …

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 …

Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples,
which can produce erroneous predictions by injecting imperceptible perturbations. In this …

Malware poses a severe threat to cyber security. Attackers use malware to achieve their
malicious purposes, such as unauthorized access, stealing confidential data, blackmailing …

Deep neural networks are vulnerable to adversarial attacks. Among different attack settings,
the most challenging yet the most practical one is the hard-label setting where the attacker …

Deep neural networks are vulnerable to adversarial attacks which can fool them by adding
minuscule perturbations to the input images. The robustness of existing defenses suffers …

Convolutional Neural Networks have achieved significant success across multiple computer
vision tasks. However, they are vulnerable to carefully crafted, human-imperceptible …