Privacy leakage is one of the most critical issues in machine learning and has attracted
growing interest for tasks such as demonstrating potential threats in model attacks and …

The studies on black-box and evolutionary algorithm-based adversarial attacks have
become increasingly popular due to the intractable acquisition of the structural knowledge of …

In spite of the ongoing evolution of deep learning Convolutional Neural Networks (CNNs)
remain the de facto choice for numerous vision applications. To foster trust researchers have …

Adversarial sample generation problem is a hot issue in the security field of deep learning.
Evolutionary algorithm has been widely used to solve this problem in recent years because …

The rapidly exploding of user data, especially applications of neural networks, involves
analyzing data collected from individuals, which brings convenience to life. Meanwhile …

Deep neural networks have achieved excellent performance in various research and
applications, but they have proven to be susceptible to adversarial examples. Generating …

Abstract Deep Neural Networks have been widely used in many fields. However, studies
have shown that DNNs are easily attacked by adversarial examples, which have tiny …

Adversarial attacks expose the vulnerabilities of machine learning models that could alter
the models' outputs by slightly perturbing the input data of the models. In this paper, we …

Deep Neural Networks (DNNs) have become the standard-de-facto technology in most
computer vision applications due to the exceptional performance and versatile applicability …