Universal Rules for Fooling Deep Neural Networks based Text Classification

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

4 被引用数 (Scopus)

抄録

Recently, deep learning based natural language processing techniques are being extensively used to deal with spam mail, censorship evaluation in social networks, among others. However, there is only a couple of works evaluating the vulnerabilities of such deep neural networks. Here, we go beyond attacks to investigate, for the first time, universal rules, i.e., rules that are sample agnostic and therefore could turn any text sample in an adversarial one. In fact, the universal rules do not use any information from the method itself (no information from the method, gradient information or training dataset information is used), making them black-box universal attacks. In other words, the universal rules are sample and method agnostic. By proposing a coevolutionary optimization algorithm we show that it is possible to create universal rules that can automatically craft imperceptible adversarial samples (only less than five perturbations which are close to misspelling are inserted in the text sample). A comparison with a random search algorithm further justifies the strength of the method. Thus, universal rules for fooling networks are here shown to exist. Hopefully, the results from this work will impact the development of yet more sample and model agnostic attacks as well as their defenses.

本文言語英語
ホスト出版物のタイトル2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ2221-2228
ページ数8
ISBN(電子版)9781728121536
DOI
出版ステータス出版済み - 6 2019
イベント2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, ニュージ―ランド
継続期間: 6 10 20196 13 2019

出版物シリーズ

名前2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

会議

会議2019 IEEE Congress on Evolutionary Computation, CEC 2019
国/地域ニュージ―ランド
CityWellington
Period6/10/196/13/19

All Science Journal Classification (ASJC) codes

  • 計算数学
  • モデリングとシミュレーション

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