Deep learning plays a significant role in developing a robust and constructive framework for
tackling complex learning tasks. Consequently, it is widely utilized in many security-critical …

Adversarial risk quantifies the performance of classifiers on adversarially perturbed data.
Numerous definitions of adversarial risk—not all mathematically rigorous and differing subtly …

Early diagnosis of Alzheimer's disease plays a crucial role in treatment planning that might
slow down the disease's progression. This problem is commonly posed as a classification …

Adversarial training is the most popular and general strategy to improve Deep Neural
Network (DNN) robustness against adversarial noises. Many adversarial training methods …

Despite many proposed algorithms to provide robustness to deep learning (DL) models, DL
models remain susceptible to adversarial attacks. We hypothesize that the adversarial …

Conventional deep neural networks (DNNs) have been shown to be vulnerable to images
with adversarial perturbations, referred to as adversarial examples. In this study, we propose …

Adversarial attacks threaten the reliability and security of Deep Neural Networks (DNNs),
necessitating the need to develop robust defensive mechanisms beyond traditional …

Deep neural networks (DNNs), especially convolutional neural networks (CNNs), have
achieved remarkable state-of-the-art performance in various applications [3]. However …

Generative modeling and adversarial learning have significantly advanced the field of
computer vision, particularly in object recognition and synthesis, unsupervised domain …

Adversarial Training (AT) aims to alleviate the vulnerability of deep neural networks to
adversarial perturbations. However, the AT techniques struggle to maintain the performance …