Deep neural networks (DNNs) are vulnerable to adversarial attacks which can fool the
classifiers by adding small perturbations to the original example. The added perturbations in …

Adversarial attacks provide a good way to study the robustness of deep learning models.
One category of methods in transfer-based black-box attack utilizes several image …

LiDAR sensors are widely used in many safety-critical applications such as autonomous
driving and drone control, and the collected data called point clouds are subsequently …

Adversarial examples have attracted widespread attention in security-critical applications
because of their transferability across different models. Although many methods have been …

Adversarial examples crafted by adding perturbations imperceptible to humans can deceive
neural networks. Recent studies identify the adversarial transferability across various …

The integration of visual and textual data in Vision-Language Pre-training (VLP) models is
crucial for enhancing vision-language understanding. However, the adversarial robustness …

Data augmentation (DA) plays a critical role in improving the generalization of deep learning
models. Recent works on automatically searching for DA policies from data have achieved …

Numerous works study black-box attacks on image classifiers, where adversaries generate
adversarial examples against unknown target models without having access to their internal …

Malicious attackers generate adversarial instances by introducing imperceptible
perturbations into data. Even in the black-box setting where model details are concealed …

The susceptibility of deep neural networks (DNNs) to adversarial examples has raised
significant concerns regarding the security and reliability of artificial intelligence systems …