18 citations · 39 across the 3 of their papers we have counts for
4 papers
Enhance the Visual Representation via Discrete Adversarial Training
Xiaofeng Mao, Yuefeng Chen, Ranjie Duan +6
Adversarial Training (AT), which is commonly accepted as one of the most effective approaches defending against adversarial examples, can largely harm the standard performance, thu…
AdvDrop: Adversarial Attack to DNNs by Dropping Information
Ranjie Duan, Yuefeng Chen, Dantong Niu +3
Human can easily recognize visual objects with lost information: even losing most details with only contour reserved, e.g. cartoon. However, in terms of visual perception of Deep N…
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink
Ranjie Duan, Xiaofeng Mao, A. K. Qin +4
Though it is well known that the performance of deep neural networks (DNNs) degrades under certain light conditions, there exists no study on the threats of light beams emitted fro…
Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles
Ranjie Duan, Xingjun Ma, Yisen Wang +3
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. Existing works have mostly focused on either digital adversarial examples created via small and impe…