TY - GEN

T1 - Deep minimax probability machine

AU - He, Lirong

AU - Guo, Ziyi

AU - Huang, Kaizhu

AU - Xu, Zenglin

N1 - Funding Information:
This paper was in part supported by Grants from the Natural Science Foundation of China(No. 61572111), and two Fundamental Research Funds for the Central Universities of China (Nos.ZYGX2016Z003, ZYGX2017KYQD177),
Publisher Copyright:
© 2019 IEEE.

PY - 2019/11

Y1 - 2019/11

N2 - Deep neural networks enjoy a powerful representation and have proven effective in a number of applications. However, recent advances show that deep neural networks are vulnerable to adversarial attacks incurred by the so-called adversarial examples. Although the adversarial example is only slightly different from the input sample, the neural network classifies it as the wrong class. In order to alleviate this problem, we propose the Deep Minimax Probability Machine (DeepMPM), which applies MPM to deep neural networks in an end-to-end fashion. In a worst-case scenario, MPM tries to minimize an upper bound of misclassification probabilities, considering the global information (i.e., mean and covariance information of each class). DeepMPM can be more robust since it learns the worst-case bound on the probability of misclassification of future data.

AB - Deep neural networks enjoy a powerful representation and have proven effective in a number of applications. However, recent advances show that deep neural networks are vulnerable to adversarial attacks incurred by the so-called adversarial examples. Although the adversarial example is only slightly different from the input sample, the neural network classifies it as the wrong class. In order to alleviate this problem, we propose the Deep Minimax Probability Machine (DeepMPM), which applies MPM to deep neural networks in an end-to-end fashion. In a worst-case scenario, MPM tries to minimize an upper bound of misclassification probabilities, considering the global information (i.e., mean and covariance information of each class). DeepMPM can be more robust since it learns the worst-case bound on the probability of misclassification of future data.

KW - Adversarial attacks

KW - Deep neural networks

KW - Mimimax probability machine

UR - http://www.scopus.com/inward/record.url?scp=85078764095&partnerID=8YFLogxK

U2 - 10.1109/ICDMW.2019.00127

DO - 10.1109/ICDMW.2019.00127

M3 - Conference Proceeding

AN - SCOPUS:85078764095

T3 - IEEE International Conference on Data Mining Workshops, ICDMW

SP - 869

EP - 876

BT - Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019

A2 - Papapetrou, Panagiotis

A2 - Cheng, Xueqi

A2 - He, Qing

PB - IEEE Computer Society

T2 - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019

Y2 - 8 November 2019 through 11 November 2019

ER -