Deep learning has recently achieved great success in many visual recognition tasks.
However, the deep neural networks (DNNs) are often perceived as black-boxes, making …

Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

Backdoor attack intends to embed hidden backdoors into deep neural networks (DNNs), so
that the attacked models perform well on benign samples, whereas their predictions will be …

To have a better understanding and usage of Convolution Neural Networks (CNNs), the
visualization and interpretation of CNNs has attracted increasing attention in recent years. In …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

While deep face recognition (FR) systems have shown amazing performance in
identification and verification, they also arouse privacy concerns for their excessive …

Deep neural networks (DNNs) are vulnerable to backdoor attacks which can hide backdoor
triggers in DNNs by poisoning training data. A backdoored model behaves normally on …

Deep neural networks are vulnerable to adversarial examples, which becomes one of the
most important research problems in the development of deep learning. While a lot of efforts …

Abstract Deep Convolutional Neural Networks (CNNs) have been pushing the frontier of
face recognition over past years. However, existing CNN models are far less accurate when …

We consider the black-box adversarial setting, where the adversary has to generate
adversarial perturbations without access to the target models to compute gradients. Previous …