Adversarial attacks and defenses in machine learning and deep neural network (DNN) have
been gaining significant attention due to the rapidly growing applications of deep learning in …

The deployment of the fifth-generation (5G) wireless networks in Internet of Everything (IoE)
applications and future networks (eg, sixth-generation (6G) networks) has raised a number …

Deep learning has presented remarkable progress in various tasks. Despite the excellent
performance, deep learning models remain not robust, especially to well-designed …

Recent state-of-the-art face recognition (FR) approaches have achieved impressive
performance, yet unconstrained face recognition still represents an open problem. Face …

Evasion Attacks (EA) are used to test the robustness of trained neural networks by distorting
input data to misguide the model into incorrect classifications. Creating these attacks is a …

Modern image-to-text systems typically adopt the encoder-decoder framework, which
comprises two main components: an image encoder, responsible for extracting image …

Deep learning-based identity management systems, such as face authentication systems,
are vulnerable to adversarial attacks. However, existing attacks are typically designed for …

Automatic modulation recognition (AMR) of radio signal is an important research topic in the
area of non-cooperative communication and cognitive radio. Recently deep learning (DL) …

The handy biometric data is a double-edged sword, paving the way of the prosperity of
biometric authentication systems but bringing the personal privacy concern. To alleviate the …

In this fast-moving digital era, millions of images are added to repositories every millisecond.
These images are context-rich images with ample underlying data that are extracted and …