Deep learning models have achieved great success in solving a variety of natural language
processing (NLP) problems. An ever-growing body of research, however, illustrates the …

The performance of a biometric system that relies on a single biometric modality (eg,
fingerprints only) is often stymied by various factors such as poor data quality or limited …

Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in
securing face recognition systems from presentation attacks (PAs). As more and more …

Medical IoT devices are rapidly becoming part of management ecosystems for pandemics
such as COVID-19. Existing research shows that deep learning (DL) algorithms have been …

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 …

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 …

Accurate detection of the fake but photorealistic images is one of the most challenging tasks
to address social, biometrics security and privacy related concerns in our community. Earlier …

The rising popularity of deep neural networks (DNNs) in computer vision has raised
concerns about their robustness in the real world. Recent works in this field have well …

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 …