22 citations · 93 across the 25 of their papers we have counts for
6 papers · 1 filter
Generalizable Black-Box Adversarial Attack with Meta Learning
Fei Yin, Yong Zhang, Baoyuan Wu +4
In the scenario of black-box adversarial attack, the target model's parameters are unknown, and the attacker aims to find a successful adversarial perturbation based on query feedb…
Adversarial Rademacher Complexity of Deep Neural Networks
Jiancong Xiao, Yanbo Fan, Ruoyu Sun +1
Deep neural networks (DNNs) are highly vulnerable to adversarial attacks. Ideally, a robust model should perform well on both perturbed training data and unseen perturbed test data…
Stability Analysis and Generalization Bounds of Adversarial Training
Jiancong Xiao, Yanbo Fan, Ruoyu Sun +2
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…
Adaptive Smoothness-weighted Adversarial Training for Multiple Perturbations with Its Stability Analysis
Jiancong Xiao, Zeyu Qin, Yanbo Fan +3
Adversarial Training (AT) has been demonstrated as one of the most effective methods against adversarial examples. While most existing works focus on AT with a single type of pertu…
Understanding Adversarial Robustness Against On-manifold Adversarial Examples
Jiancong Xiao, Liusha Yang, Yanbo Fan +2
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…
Fast Adversarial Training with Adaptive Step Size
Zhichao Huang, Yanbo Fan, Chen Liu +5
While adversarial training and its variants have shown to be the most effective algorithms to defend against adversarial attacks, their extremely slow training process makes it har…