Date of Award
5-2023
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Electrical and Computer Engineering
Committee Chair/Advisor
Yingjie Lao
Committee Member
Long Cheng
Committee Member
Richard Groff
Committee Member
Adam Hoover
Committee Member
Apoorva Kapadia
Abstract
Adversarial deep learning is the field of study which analyzes deep learning in the presence of adversarial entities. This entails understanding the capabilities, objectives, and attack scenarios available to the adversary to develop defensive mechanisms and avenues of robustness available to the benign parties. Understanding this facet of deep learning helps us improve the safety of the deep learning systems against external threats from adversaries. However, of equal importance, this perspective also helps the industry understand and respond to critical failures in the technology. The expectation of future success has driven significant interest in developing this technology broadly. Adversarial deep learning stands as a balancing force to ensure these developments remain grounded in the real-world and proceed along a responsible trajectory. Recently, the growth of deep learning has begun intersecting with the computer hardware domain to improve performance and efficiency for resource constrained application domains. The works investigated in this dissertation constitute our pioneering efforts in migrating adversarial deep learning into the hardware domain alongside its parent field of research.
Recommended Citation
Clements, Joseph, "Adversarial Deep Learning and Security with a Hardware Perspective" (2023). All Dissertations. 3352.
https://tigerprints.clemson.edu/all_dissertations/3352
Author ORCID Identifier
0000-0002-8371-8602