Adversarial learning: A critical review and active learning study

D. J. Miller, X. Hu, Z. Qiu, G. Kesidis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

This papers consists of two parts. The first is a critical review of prior art on adversarial learning, i) identifying some significant limitations of previous works, which have focused mainly on attack exploits and ii) proposing novel defenses against adversarial attacks. The second part is an experimental study considering the adversarial active learning scenario and an investigation of the efficacy of a mixed sample selection strategy for combating an adversary who attempts to disrupt the classifier learning.

Original languageEnglish (US)
Title of host publication2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Proceedings
EditorsNaonori Ueda, Jen-Tzung Chien, Tomoko Matsui, Jan Larsen, Shinji Watanabe
PublisherIEEE Computer Society
Pages1-6
Number of pages6
ISBN (Electronic)9781509063413
DOIs
StatePublished - Dec 5 2017
Event2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Tokyo, Japan
Duration: Sep 25 2017Sep 28 2017

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2017-September
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Other

Other2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017
CountryJapan
CityTokyo
Period9/25/179/28/17

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All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Signal Processing

Cite this

Miller, D. J., Hu, X., Qiu, Z., & Kesidis, G. (2017). Adversarial learning: A critical review and active learning study. In N. Ueda, J-T. Chien, T. Matsui, J. Larsen, & S. Watanabe (Eds.), 2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Proceedings (pp. 1-6). (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2017-September). IEEE Computer Society. https://doi.org/10.1109/MLSP.2017.8168163