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 …

In recent years, deep learning-based image classification models have been extensively
studied in academia and widely applied in industry. However, deep learning is inherently …

Deep learning-based lane detection (LD) plays a critical role in autonomous driving
systems, such as adaptive cruise control. However, it is vulnerable to backdoor attacks …

This work investigates efficient score-based black-box adversarial attacks with high Attack
Success Rate (ASR) and good generalizability. We design a novel attack method based on …

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

Existing black-box attacks have demonstrated promising potential in creating adversarial
examples (AE) to deceive deep learning models. Most of these attacks need to handle a vast …

In the rapidly evolving field of machine learning, adversarial attacks present a significant
challenge to model robustness and security. Decision-based attacks, which only require …

Adversarial examples are well-known tools to evaluate the vulnerability of deep neural
networks (DNNs). Although lots of adversarial attack algorithms have been developed, it is …

Since the training data of the target model is not available in the black-box substitute attack,
most recent schemes utilize generative adversarial networks (GANs) to generate data for …

Deep learning-based person re-identification (re-id) models are widely employed in
surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial …