Date of Award

8-13-2019

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Anu Bourgeois

Second Advisor

Rajshekhar Sunderraman

Third Advisor

Yubao Wu

Abstract

Deep Neural Networks have the tendency to be easily fooled and research has shown that these neural networks consider unrecognizable images as recognizable. And, essentially this could lead to a lot of problems in secure systems based on image recognition. As, a solution to this problem, this paper a denoising architecture that extracts the noise from an image thus enabling the neural network to accurately label an image.

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