Benefiting from the rapid development of deep learning, 2D and 3D computer vision
applications are deployed in many safe-critical systems, such as autopilot and identity …

Deep learning, as an important topic of artificial intelligence, has been widely applied in
various fields, especially in computer vision applications, such as image classification and …

In the realm of decision-based attacks, which aim to mislead target models by manipulating
output labels, the advent of the polar coordinates attack has marked a significant evolution …

One area of current research on adversarial attacks is how to generate plausible adversarial
examples when only a small number of datasets are available. Current adversarial attack …

With the wide application of deep neural network models in various computer vision tasks,
there has been a proliferation of adversarial example generation strategies aimed at deeply …

In recent years, research on adversarial attack techniques for remote sensing object
detection (RSOD) has made great progress. Still, most of the research nowadays is on end …

In generating adversarial examples, the conventional black-box attack methods rely on
sufficient feedback from the to-be-attacked models by repeatedly querying until the attack is …

The vulnerability of neural network classifiers to adversarial attacks is a major obstacle to
their deployment in safety-critical applications. Regularization of network parameters during …

Recent studies have shown that video action recognition models are also vulnerable to
fooling by adversarial samples. However, currently existing video attack methods usually …

Adversarial examples represent a serious issue for the application of machine learning
models in many sensitive domains. For generating adversarial examples, decision based …