Deep learning applies multiple processing layers to learn representations of data with
multiple levels of feature extraction. This emerging technique has reshaped the research …

Security and privacy of users have become significant concerns due to the involvement of
the Internet of Things (IoT) devices in numerous applications. Cyber threats are growing at …

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 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 …

Due to the importance of security, the adversarial attack has become an increasingly
popular area for deep learning, especially the black-box adversarial attack, which can only …

Deep learning algorithms provide state-of-the-art results on a multitude of applications.
However, it is also well established that they are highly vulnerable to adversarial …

Face recognition algorithms have demonstrated very high recognition performance,
suggesting suitability for real world applications. Despite the enhanced accuracies …

Multivariate time series (MTS) regression tasks are common in many real-world data mining
applications including finance, cybersecurity, energy, healthcare, prognostics, and many …

Face recognition (FR) systems have demonstrated reliable verification performance,
suggesting suitability for real-world applications ranging from photo tagging in social media …

Visual content has become the primary source of information, as evident in the billions of
images and videos, shared and uploaded every single day. This has led to an increase in …