13 citations · 24 across the 5 of their papers we have counts for
5 papers
Double Backpropagation for Training Autoencoders against Adversarial Attack
Chengjin Sun, Sizhe Chen, Xiaolin Huang
Deep learning, as widely known, is vulnerable to adversarial samples. This paper focuses on the adversarial attack on autoencoders. Safety of the autoencoders (AEs) is important be…
Type I Attack for Generative Models
Chengjin Sun, Sizhe Chen, Jia Cai +1
Generative models are popular tools with a wide range of applications. Nevertheless, it is as vulnerable to adversarial samples as classifiers. The existing attack methods mainly f…
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…