3 citations · 4 across the 3 of their papers we have counts for
5 papers
Adversarial Examples Detection beyond Image Space
Kejiang Chen, Yuefeng Chen, Hang Zhou +4
Deep neural networks have been proved that they are vulnerable to adversarial examples, which are generated by adding human-imperceptible perturbations to images. To defend these a…
LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud-based Deep Networks
Hang Zhou, Dongdong Chen, Jing Liao +6
Deep neural networks have made tremendous progress in 3D point-cloud recognition. Recent works have shown that these 3D recognition networks are also vulnerable to adversarial samp…
Self-supervised Adversarial Training
Kejiang Chen, Hang Zhou, Yuefeng Chen +6
Recent work has demonstrated that neural networks are vulnerable to adversarial examples. To escape from the predicament, many works try to harden the model in various ways, in whi…
DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense
Hang Zhou, Kejiang Chen, Weiming Zhang +3
Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose a Denoiser and UPsampler Network (DUP-Ne…
Distribution-Preserving Steganography Based on Text-to-Speech Generative Models
Kejiang Chen, Hang Zhou, Hanqing Zhao +3
Steganography is the art and science of hiding secret messages in public communication so that the presence of the secret messages cannot be detected. There are two distribution-pr…