@inproceedings{ef229520e681408a9dbc4ec965a2b011,
title = "Learner-Independent Targeted Data Omission Attacks",
abstract = "In this paper we introduce the data omission attack—a new type of attack against learning mechanisms. The attack can be seen as a specific type of a poisoning attack. However, while poisoning attacks typically corrupt data in various ways including addition, omission and modification, to optimize the attack, we focus on omission only, which is much simpler to implement and analyze. A major advantage of our attack method is its generality. While poisoning attacks are usually optimized for a specific learner and prove ineffective against others, our attack is effective against a variety of learners. We demonstrate this effectiveness via a series of attack experiments against various learning mechanisms. We show that, with a relatively low attack budget, our omission attack succeeds regardless of the target learner.",
keywords = "Adversarial ML, Machine learning",
author = "Guy Barash and Onn Shehory and Sarit Kraus and Eitan Farchi",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 3rd International Workshop on Engineering Dependable and Secure Machine Learning Systems, EDSMLS 2020 ; Conference date: 07-02-2020 Through 07-02-2020",
year = "2020",
doi = "10.1007/978-3-030-62144-5\_3",
language = "الإنجليزيّة",
isbn = "9783030621438",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "23--41",
editor = "Onn Shehory and Eitan Farchi and Guy Barash",
booktitle = "Engineering Dependable and Secure Machine Learning Systems - Third International Workshop, EDSMLS 2020, Revised Selected Papers",
address = "ألمانيا",
}