Generative Adversarial Networks (GANs) have promoted a variety of applications in
computer vision and natural language processing, among others, due to its generative …

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 …

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

As we seek to deploy machine learning models beyond virtual and controlled domains, it is
critical to analyze not only the accuracy or the fact that it works most of the time, but if such a …

Deep neural networks, particularly face recognition models, have been shown to be
vulnerable to both digital and physical adversarial examples. However, existing adversarial …

Deep neural networks (DNNs) have become essential for aerial detection. However, DNNs
are vulnerable to adversarial examples, which pose great security concerns for security …

The success of deep learning based face recognition systems has given rise to serious
privacy concerns due to their ability to enable unauthorized tracking of users in the digital …

Very recently, few certified defense methods have been developed to provably guarantee
the robustness of a text classifier to adversarial synonym substitutions. However, all the …

Synthetic aperture radar (SAR) has all-day and all-weather characteristics and plays an
extremely important role in the military field. The breakthroughs in deep learning methods …

Generative Adversarial Networks (GANs) are a remarkable creation with regard to deep
generative models. Thanks to their ability to learn from complex data distributions, GANs …