Random ensemble of locally optimum detectors for detection of adversarial examples

Amish Goel, Pierre Moulin

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

Abstract

Deep neural networks achieve state-of-the-art performance for several image classification problems but have been shown to be easily fooled by adversarial perturbations which slightly modify a legitimate image in a specific direction and are visually indistinguishable from the original. This presents a security risk for applications such as autonomous systems. We tackle the problem of detecting such »forgeries» using a locally optimal detector which is well suited to detecting weak signal perturbations. We present a procedure for learning the forgery detector from a training set, using Gaussian Mixture Models (GMM) for modeling image patches. A random ensemble of patches is used for detection of the forgery. The reliability of our forgery detector is assessed for several image classification tasks.

Original languageEnglish (US)
Title of host publication2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1189-1193
Number of pages5
ISBN (Electronic)9781728112954
DOIs
StatePublished - Feb 20 2019
Event2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States
Duration: Nov 26 2018Nov 29 2018

Publication series

Name2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings

Conference

Conference2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
CountryUnited States
CityAnaheim
Period11/26/1811/29/18

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

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    Goel, A., & Moulin, P. (2019). Random ensemble of locally optimum detectors for detection of adversarial examples. In 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings (pp. 1189-1193). [8646479] (2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2018.8646479