Date of Award
8-1-2022
Document Type
Thesis
Degree Name
M.S. in Engineering Science
First Advisor
Charles Fleming
Second Advisor
Yixin Chen
Third Advisor
Charlie Walter
School
University of Mississippi
Relational Format
dissertation/thesis
Abstract
In recent years, the amount of data being produced has increased tremendously. This data has allowed us to create and train machine learning models that are being used nowadays. Though these models have proven to be very effective in classification and regression problems, they do have a vulnerability. This vulnerability is exploited by adding adversarial perturbations to the original data or in the deployed model. These adversarial attacks are done by making minute changes to the data to confuse the machine learning model. This can lead to models’ misrepresentation and drop the accuracy. For the span of this paper, I have used various statistical and visualization analysis methods to find the effect of adversarial perturbations on the CIFAR10 image data. Some of these methods involved were mean, standard deviation, variance, covariance, the probability distribution of color channels, etc. This paper discusses the insights found during the analysis of the CIFAR10 image dataset and the future work to be expected in this field.
Recommended Citation
Zafar, Hamza Ali, "Exploratory Analysis of Effects of Data on Adversarial Perturbations" (2022). Electronic Theses and Dissertations. 2413.
https://egrove.olemiss.edu/etd/2413
Concentration/Emphasis
Computer Sciences