Semantic Adversarial Attack on Support Vector Machine

Author

Yessica Rodriguez, The University of Texas Rio Grande Valley

Date of Award

5-2021

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Dr. Hansheng Lei

Second Advisor

Dr. Fitratullah Khan

Third Advisor

Dr. Mahmoud K. Quweider

Abstract

Despite the breakthroughs in machine learning, most classifiers are not robust against adversarial attacks. They can be easily fooled by adversarial examples. These examples can be created in a variety of ways. In this thesis, the ideas of detecting edges or critical pixels in an image are investigated that could be used for fooling classifiers. Identifying those critical pixels in an image can lead the way to fix the vulnerabilities and thus making it robust against cyber-attacks. For testing, a Support Vector Machine (SVM) is used to see the success of the adversarial examples generated.

Comments

Copyright 2021 Yessica Rodriguez. All Rights Reserved.

https://go.openathens.net/redirector/utrgv.edu?url=https://www.proquest.com/dissertations-theses/semantic-adversarial-attack-on-support-vector/docview/2564508851/se-2?accountid=7119

Recommended Citation

Rodriguez, Yessica, "Semantic Adversarial Attack on Support Vector Machine" (2021). Theses and Dissertations. 953.
https://scholarworks.utrgv.edu/etd/953