Concerns about cybersecurity and attack methods have risen in the information age. Many
techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs) …

In recent years, advancements in machine learning (ML) techniques, in particular, deep
learning (DL) methods have gained a lot of momentum in solving inverse imaging problems …

Generative adversarial networks (GANs) have rapidly emerged as powerful tools for
generating realistic and diverse data across various domains, including computer vision and …

Convolution Neural Network (CNN) models have gained ground in research activities
particularly in medical images used for Diabetes Retinopathy (DR) detection. X-ray, MRI …

Fast adversarial training (FAT) is an efficient method to improve robustness in white-box
attack scenarios. However, the original FAT suffers from catastrophic overfitting, which …

Adversarial training (AT) has been demonstrated to be effective in improving model
robustness by leveraging adversarial examples for training. However, most AT methods are …

Deep neural networks (DNNs) have achieved remarkable performance across a wide range
of applications, while they are vulnerable to adversarial examples, which motivates the …

This study evaluated the generation of adversarial examples and the subsequent
robustness of an image classification model. The attacks were performed using the Fast …

Deep neural networks (DNNs) are shown to be vulnerable to universal adversarial
perturbations (UAP), a single quasi-imperceptible perturbation that deceives the DNNs on …

Highly realistic imaging and video synthesis have become possible and relatively simple
tasks with the rapid growth of generative adversarial networks (GANs). GAN-related …