7 citations · 11 across the 3 of their papers we have counts for
4 papers · 1 filter
Investigating Catastrophic Overfitting in Fast Adversarial Training: A Self-fitting Perspective
Zhengbao He, Tao Li, Sizhe Chen +1
Although fast adversarial training provides an efficient approach for building robust networks, it may suffer from a serious problem known as catastrophic overfitting (CO), where m…
HRFA: High-Resolution Feature-based Attack
Zhixing Ye, Sizhe Chen, Peidong Zhang +2
Adversarial attacks have long been developed for revealing the vulnerability of Deep Neural Networks (DNNs) by adding imperceptible perturbations to the input. Most methods generat…
Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet
Sizhe Chen, Zhengbao He, Chengjin Sun +2
Adversarial attacks on deep neural networks (DNNs) have been found for several years. However, the existing adversarial attacks have high success rates only when the information of…
DAmageNet: A Universal Adversarial Dataset
Sizhe Chen, Xiaolin Huang, Zhengbao He +1
It is now well known that deep neural networks (DNNs) are vulnerable to adversarial attack. Adversarial samples are similar to the clean ones, but are able to cheat the attacked DN…