Neural networks are vulnerable to adversarial examples, malicious inputs crafted to fool
trained models. Adversarial examples often exhibit black-box transfer, meaning that …

Face recognition has achieved great success in the last five years due to the development of
deep learning methods. However, deep convolutional neural networks (DCNNs) have been …

Face recognition systems have been shown to be vulnerable to adversarial faces resulting
from adding small perturbations to probe images. Such adversarial images can lead state-of …

As billions of personal data being shared through social media and network, the data
privacy and security have drawn an increasing attention. Several attempts have been made …

We present a new white-box backdoor attack that exploits a vulnerability of convolutional
neural networks (CNNs). In particular, we examine the application of facial recognition …

We consider the blackbox transfer-based targeted adversarial attack threat model in the
realm of deep neural network (DNN) image classifiers. Rather than focusing on crossing …

Deep neural network (DNN) architecture based models have high expressive power and
learning capacity. However, they are essentially a black box method since it is not easy to …

Adversarial examples for neural network image classifiers are known to be transferable:
examples optimized to be misclassified by a source classifier are often misclassified as well …

Abstract The Deep neural networks (DNNs) have achieved great success on a variety of
computer vision tasks, however, they are highly vulnerable to adversarial attacks. To …

In this paper we address the vulnerabilities of iris recognition systems to both image-based
impersonation attacks and Presentation Attacks (PAs) in physical environments. While …