Deep Learning (DL) is the most efficient technique to handle a wide range of challenging
problems such as data analytics, diagnosing diseases, detecting anomalies, etc. The …

The performance of face analysis and recognition systems depends on the quality of the
acquired face data, which is influenced by numerous factors. Automatically assessing the …

Deep learning has been successfully applied to solve various complex problems ranging
from big data analytics to computer vision and human-level control. Deep learning advances …

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 …

It is becoming increasingly easy to automatically replace a face of one person in a video with
the face of another person by using a pre-trained generative adversarial network (GAN) …

The new developments in deep generative networks have significantly improve the quality
and efficiency in generating realistically-looking fake face videos. In this work, we describe a …

The recent proliferation of fake portrait videos poses direct threats on society, law, and
privacy [1]. Believing the fake video of a politician, distributing fake pornographic content of …

Research on non-intrusive software-based face spoofing detection schemes has been
mainly focused on the analysis of the luminance information of the face images, hence …

With the powerful deep network architectures, such as generative adversarial networks, one
can easily generate photorealistic images. Although the generated images are not …

Research on face spoofing detection has mainly been focused on analyzing the luminance
of the face images, hence discarding the chrominance information which can be useful for …