Transfer-based attacks generate adversarial examples on the surrogate model, which can
mislead other black-box models without access, making it promising to attack real-world …

Compared to white-box adversarial attacks, black-box adversarial attacks are more
applicable in practical scenarios and have received significant attention. However, most …

Although recent deep neural network algorithm has shown tremendous success in several
computer vision tasks, their vulnerability against minute adversarial perturbations has raised …

The problem of adversarial attacks to a black-box model when no queries are allowed has
posed a great challenge to the community and has been extensively investigated. In this …

Image classification models based on deep neural networks have made great improvements
on various tasks, but they are still vulnerable to adversarial examples that could increase the …

Contemporary research on adversarial attacks in Intelligent Internet of Things focuses on
balancing two key aspects: 1) transferability and 2) imperceptibility. However, achieving a …

Deep neural networks have been used extensively for diverse visual tasks, including object
detection, face recognition, and image classification. However, they face several security …

Despite the fact that adversarial training has become the de facto method for improving the
robustness of deep neural networks, it is well-known that vanilla adversarial training suffers …

Although deep neural networks (DNNs) have advanced performance in many application
scenarios, they are vulnerable to the attacks of adversarial examples that are crafted by …

Completely Automated Public Turing test to tell Computers and Humans Apart, short for
CAPTCHA, is an essential and relatively easy way to defend against malicious attacks …