7 citations · 11 across the 3 of their papers we have counts for
3 papers
cs.CV2020★ 4 cited
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…
cs.CV2020
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…
cs.LG2019★ 7 cited
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…