Defending deep neural networks from adversarial attacks on three-dimensional images by compressive sensing

Vladislav Kravets, Bahram Javidi, Adrian Stern

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We demonstrate the utility of compressive sensing to defend against adversarial attacks on deep learning classifiers and to encrypt the 3D image, thus, to avoid counterattacks.

Original languageEnglish
Title of host publication3D Image Acquisition and Display
Subtitle of host publicationTechnology, Perception and Applications, 3D 2021
PublisherThe Optical Society
ISBN (Electronic)9781557528209
StatePublished - 1 Jan 2021
Event3D Image Acquisition and Display: Technology, Perception and Applications, 3D 2021 - Part of Imaging and Applied Optics Congress 2021 - Virtual, Online, United States
Duration: 19 Jul 202123 Jul 2021

Publication series

NameOptics InfoBase Conference Papers

Conference

Conference3D Image Acquisition and Display: Technology, Perception and Applications, 3D 2021 - Part of Imaging and Applied Optics Congress 2021
Country/TerritoryUnited States
CityVirtual, Online
Period19/07/2123/07/21

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Mechanics of Materials

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