Crafting adversarial input sequences for recurrent neural networks

Nicolas Papernot, Patrick McDaniel, Ananthram Swami, Richard Harang

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

50 Scopus citations

Abstract

Machine learning models are frequently used to solve complex security problems, as well as to make decisions in sensitive situations like guiding autonomous vehicles or predicting financial market behaviors. Previous efforts have shown that numerous machine learning models are vulnerable to adversarial manipulations of their inputs taking the form of adversarial samples. Such inputs are crafted by adding carefully selected perturbations to legitimate inputs so as to force the machine learning model to misbehave, for instance by outputting a wrong class if the machine learning task of interest is classification. In fact, to the best of our knowledge, all previous work on adversarial samples crafting for neural networks considered models used to solve classification tasks, most frequently in computer vision applications. In this paper, we investigate adversarial input sequences for recurrent neural networks processing sequential data. We show that the classes of algorithms introduced previously to craft adversarial samples misclassified by feed-forward neural networks can be adapted to recurrent neural networks. In a experiment, we show that adversaries can craft adversarial sequences misleading both categorical and sequential recurrent neural networks.

Original languageEnglish (US)
Title of host publicationMILCOM 2016 - 2016 IEEE Military Communications Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages49-54
Number of pages6
ISBN (Electronic)9781509037810
DOIs
StatePublished - Dec 22 2016
Event35th IEEE Military Communications Conference, MILCOM 2016 - Baltimore, United States
Duration: Nov 1 2016Nov 3 2016

Publication series

NameProceedings - IEEE Military Communications Conference MILCOM

Other

Other35th IEEE Military Communications Conference, MILCOM 2016
CountryUnited States
CityBaltimore
Period11/1/1611/3/16

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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    Papernot, N., McDaniel, P., Swami, A., & Harang, R. (2016). Crafting adversarial input sequences for recurrent neural networks. In MILCOM 2016 - 2016 IEEE Military Communications Conference (pp. 49-54). [7795300] (Proceedings - IEEE Military Communications Conference MILCOM). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MILCOM.2016.7795300