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 …

Deep learning models have revolutionized numerous fields, yet their decision-making
processes often remain opaque, earning them the characterization of “black-box” models …

Deep neural networks (DNNs) have achieved state-of-the-art performance on face
recognition (FR) tasks in the last decade. In real scenarios, the deployment of DNNs …

Unsupervised domain adaptive object detection aims to adapt a well-trained detector from
its original source domain with rich labeled data to a new target domain with unlabeled data …

Recent adversarial attack works attempt to improve the transferability by applying various
differentiable transformations on input images. Considering the differentiable …

This article explores the application of deep neural networks (DNNs) for optimized
backstepping control design in a category of stochastic nonlinear strict-feedback systems …

Recent years have witnessed extensive applications of Deep Neural Networks (DNNs) in
various vision tasks. However, DNNs are vulnerable to adversarial images crafted by …

Recent studies have found that deep learning models are vulnerable to adversarial
examples, demonstrating that applying a certain imperceptible perturbation on clean …

This paper concentrates on the impact of stochastic function perturbation (SFP) on the set
stability of PBNs. The problem of robust set stability is divided into two cases, and they are …

Deep neural network (DNNs) based Automatic Speech Recognition (ASR) systems are
known vulnerable to adversarial attacks that are maliciously implemented by adding small …