Over the past decade, deep learning has revolutionized conventional tasks that rely on hand-
craft feature extraction with its strong feature learning capability, leading to substantial …

Deep neural networks (DNNs) have found widespread applications in interpreting remote
sensing (RS) imagery. However, it has been demonstrated in previous works that DNNs are …

Abstract Unmanned Aerial Vehicle (UAV) remote sensing object recognition plays a vital
role in a variety of sectors including military, agriculture, forestry, and construction. Accurate …

Face recognition is a prevailing authentication solution in numerous biometric applications.
Physical adversarial attacks, as an important surrogate, can identify the weaknesses of face …

Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples,
which can produce erroneous predictions by injecting imperceptible perturbations. In this …

Patch-based physical attacks have increasingly aroused concerns. However, most existing
methods focus on obscuring targets captured on the ground, and some of these methods are …

The research on attack transferability is of great importance as it can guide how to conduct
an adversarial attack without knowing any information about target models. However, it …

In recent years, face recognition has achieved promising results along with the development
of advanced Deep Neural Networks (DNNs). The existing face recognition systems are …

Deep neural networks (DNNs) are shown to be vulnerable to adversarial examples. A well-
trained model can be easily attacked by adding small perturbations to the original data. One …

Adversarial machine learning (AML) studies the adversarial phenomenon of machine
learning, which may make inconsistent or unexpected predictions with humans. Some …