In adversarial machine learning, deep neural networks can fit the adversarial examples on
the training dataset but have poor generalization ability on the test set. This phenomenon is …

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 …

In adversarial machine learning, neural networks suffer from a significant issue known as
robust overfitting, where the robust test accuracy decreases over epochs (Rice et al., 2020) …

Abstract Training Deep Neural Networks (DNNs) with adversarial examples often results in
poor generalization to test-time adversarial data. This paper investigates this issue, known …

Curriculum adversarial training empirically finds that gradually increasing the hardness of
adversarial examples can further improve the adversarial robustness of the trained model …

While adversarial training methods have significantly improved the robustness of deep
neural networks against norm-bounded adversarial perturbations, the generalization gap …

Due to the security concerns arising from adversarial vulnerability in deep metric learning
models, it is essential to enhance their adversarial robustness for secure neural network …

Most existing works focus on improving robustness against adversarial attacks bounded by
a single $ l_p $ norm using adversarial training (AT). However, these AT models' multiple …

Existing certified training methods can only train models to be robust against a certain
perturbation type (eg $ l_\infty $ or $ l_2 $). However, an $ l_\infty $ certifiably robust model …

In adversarial training (AT), most existing works focus on AT with a single type of
perturbation, such as the $\ell_\infty $ attacks. However, deep neural networks (DNNs) are …