Consensus Adversarial Defense Method Based on Augmented Examples

Author:

Ding Xintao1ORCID,Cheng Yongqiang2ORCID,Luo Yonglong1ORCID,Li Qingde2ORCID,Gope Prosanta3ORCID

Affiliation:

1. School of Computer and Information, Anhui Normal University, Wuhu, China

2. Department of Computer Science and Technology, University of Hull, Hull, U.K.

3. Department of Computer Science, University of Sheffield, Regent Court, Sheffield, U.K.

Funder

Natural Science Foundation of Anhui Province

National Natural Science Foundation of China

Designing an all-in-one Vital Sign Point of Care Analysis Tool project

Innovative U.K.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Reference33 articles.

1. Improving adversarial robustness via promoting ensemble diversity;pang;Proc 36th Int Conf Mach Learn,0

2. Deep Residual Learning for Image Recognition

3. ImageNet Large Scale Visual Recognition Challenge

4. Learning multiple layers of features from tiny images;krizhevsky,2009

5. DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks

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