C Li, Z Zhu, R Niu, Y Zhao - Computers & Security, 2024 - Elsevier
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 …
L Shi, W Liu - Proceedings of the AAAI Conference on Artificial …, 2024 - ojs.aaai.org
Curriculum adversarial training empirically finds that gradually increasing the hardness of adversarial examples can further improve the adversarial robustness of the trained model …
J Xiao, Q Long, W Su - The Thirty Seventh Annual …, 2024 - proceedings.mlr.press
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 …
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) …
J Xiao, Z Qin, Y Fan, B Wu, J Wang… - The Second Workshop …, 2023 - openreview.net
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 …