@inproceedings{2de16f8eb29e4273b5adf9c4591e17ec,
title = "Adversarial label flips attack on support vector machines",
abstract = "To develop a robust classification algorithm in the adversarial setting, it is important to understand the adversary's strategy. We address the problem of label flips attack where an adversary contaminates the training set through flipping labels. By analyzing the objective of the adversary, we formulate an optimization framework for finding the label flips that maximize the classification error. An algorithm for attacking support vector machines is derived. Experiments demonstrate that the accuracy of classifiers is significantly degraded under the attack.",
author = "Han Xiao and Huang Xiao and Claudia Eckert",
year = "2012",
doi = "10.3233/978-1-61499-098-7-870",
language = "English",
isbn = "9781614990970",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press BV",
pages = "870--875",
booktitle = "ECAI 2012 - 20th European Conference on Artificial Intelligence, 27-31 August 2012, Montpellier, France - Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstration",
note = "20th European Conference on Artificial Intelligence, ECAI 2012 ; Conference date: 27-08-2012 Through 31-08-2012",
}