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

Vladislav Kravets; Bahram Javidi; Adrian Stern

doi:10.1364/3D.2021.3Tu1C.1

OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP)
OSA Technical Digest (Optica Publishing Group, 2021),
paper 3Tu1C.1
•https://doi.org/10.1364/3D.2021.3Tu1C.1

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

Vladislav Kravets, Bahram Javidi, and Adrian Stern

3D Image Acquisition and Display: Technology, Perception and Applications 2021

Washington, DC United States
19–23 July 2021
ISBN: 978-1-943580-89-7

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Novel Applications of 3D Imaging (3Tu1C)

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V. Kravets, B. Javidi, and A. Stern, "Defending deep neural networks from adversarial attacks on three-dimensional images by compressive sensing," in OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP), H. Hua, B. Javidi, M. Martinez-Corral, O. Matoba, A. Stern, S. Thibault, T. Alieva, J. Ke, F. Willomitzer, F. Okten, P. Silveira, P. Banerjee, E. Stoykova, D. Chu, J. Park, F. Imai, C. Joo, M. Digman, D. Gardner, S. Gladysz, D. LeMaster, S. Basu, and O. Korotkova, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper 3Tu1C.1.

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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.

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