Abstract
Convolutional Neural Networks (CNNs) have been at the frontier of the revolution within the field of computer vision. Since the advent of AlexNet in 2012, neural networks with CNN architectures have surpassed human-level capabilities for many cognitive tasks. As the neural networks are integrated in many safety critical applications such as autonomous vehicles, it is critical that they are robust and resilient to errors. Unfortunately, it has recently been observed that deep neural network models are susceptible to adversarial perturbations which are imperceptible to human vision. In this thesis, we propose a solution to defend neural networks against white box adversarial attacks. The proposed defense is based on activation pattern analysis in the frequency domain. The technique is evaluated and compared with state-of-the-art techniques on the CIFAR-10 dataset.
Notes
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Graduation Date
2022
Semester
Summer
Advisor
Ewetz, Rickard
Degree
Master of Science (M.S.)
College
College of Engineering and Computer Science
Department
Computer Science
Degree Program
Computer Science
Identifier
CFE0009258; DP0026862
URL
https://purls.library.ucf.edu/go/DP0026862
Language
English
Release Date
August 2022
Length of Campus-only Access
None
Access Status
Masters Thesis (Open Access)
STARS Citation
Shah, Sharvil, "Methods For Defending Neural Networks Against Adversarial Attacks" (2022). Electronic Theses and Dissertations, 2020-. 1287.
https://stars.library.ucf.edu/etd2020/1287