Compressive imaging for defending deep neural networks from adversarial attacks

Vladislav Kravets, Bahram Javidi, Adrian Stern

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Despite their outstanding performance, convolutional deep neural networks (DNNs) are vulnerable to small adversarial perturbations. In this Letter, we introduce a novel approach to thwart adversarial attacks. We propose to employ compressive sensing (CS) to defend DNNs from adversarial attacks, and at the same time to encode the image, thus preventing counterattacks. We present computer simulations and optical experimental results of object classification in adversarial images captured with a CS single pixel camera.

Original languageEnglish
Pages (from-to)1951-1954
Number of pages4
JournalOptics Letters
Issue number8
StatePublished - 15 Apr 2021

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics


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