235 citations · 837 across the 41 of their papers we have counts for
7 papers · 1 filter
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…
CAAD 2018: Iterative Ensemble Adversarial Attack
Jiayang Liu, Weiming Zhang, Nenghai Yu
Deep Neural Networks (DNNs) have recently led to significant improvements in many fields. However, DNNs are vulnerable to adversarial examples which are samples with imperceptible…
Emerging Applications of Reversible Data Hiding
Dongdong Hou, Weiming Zhang, Jiayang Liu +3
Reversible data hiding (RDH) is one special type of information hiding, by which the host sequence as well as the embedded data can be both restored from the marked sequence withou…
CAAD 2018: Powerful None-Access Black-Box Attack Based on Adversarial Transformation Network
Xiaoyi Dong, Weiming Zhang, Nenghai Yu
In this paper, we propose an improvement of Adversarial Transformation Networks(ATN) to generate adversarial examples, which can fool white-box models and black-box models with a s…
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…
Unauthorized AI cannot Recognize Me: Reversible Adversarial Example
Jiayang Liu, Weiming Zhang, Kazuto Fukuchi +2
In this study, we propose a new methodology to control how user's data is recognized and used by AI via exploiting the properties of adversarial examples. For this purpose, we prop…