@inproceedings{469364f06cde4aa5904f1917d3b54284,
title = "Adversarial learning: A critical review and active learning study",
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.",
author = "Miller, \{D. J.\} and X. Hu and Z. Qiu and G. Kesidis",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 27th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 ; Conference date: 25-09-2017 Through 28-09-2017",
year = "2017",
month = dec,
day = "5",
doi = "10.1109/MLSP.2017.8168163",
language = "English (US)",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
pages = "1--6",
editor = "Naonori Ueda and Shinji Watanabe and Tomoko Matsui and Jen-Tzung Chien and Jan Larsen",
booktitle = "2017 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2017",
address = "United States",
}