Modern deep learning methods have long been considered black boxes due to the lack of
insights into their decision-making process. However, recent advances in explainable …

As a safety-related application, visual systems based on deep neural networks (DNNs) in
modern unmanned aerial vehicles (UAVs) show adversarial vulnerability when performing …

The increasing integration of artificial intelligence (AI) within cybersecurity has necessitated
stronger encryption methods to ensure data security. This paper presents a comparative …

Deep neural networks (DNNs) have been widely utilized in automatic visual navigation and
recognition on modern unmanned aerial vehicles (UAVs), achieving state-of-the-art …

The vulnerability of modern neural networks to random noise and deliberate attacks has
raised concerns about their robustness, particularly as they are increasingly utilized in safety …

As a safety-related application, visual systems based on convolutional neural networks
(CNNs) in modern unmanned aerial vehicles (UAVs) show adversarial vulnerability when …

A spectral approximation of a Boolean function is proposed for approximating the decision
boundary of an ensemble of Deep Neural Networks (DNNs) solving two-class pattern …

Abstract Deep Neural Networks (DNNs) can be easily fooled by inputs that are crafted by
adversaries. For example, an adversarial image can be forged by adding to an image a tiny …

Objectives: This research paper aims to develop a novel method for identifying gradient-
based data poisoning attacks on industrial applications like autonomous vehicles and …

Adversarial attacks, or attacks committed by an adversary to hijack a system, are prevalent in
the deep learning tasks of computer vision and are one of the greatest threats to these …