10 citations · 15 across the 7 of their papers we have counts for
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cs.CV2020★ 10 cited
Towards Robust Neural Networks via Orthogonal Diversity
Kun Fang, Qinghua Tao, Yingwen Wu +5
Deep Neural Networks (DNNs) are vulnerable to invisible perturbations on the images generated by adversarial attacks, which raises researches on the adversarial robustness of DNNs.…
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.LG2020
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…