Date of Award
5-2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Electrical and Computer Engineering (Holcomb Dept. of)
Committee Chair/Advisor
Yingjie Lao
Committee Member
Jon Calhoun
Committee Member
Shuhong Gao
Committee Member
Yongkai Wu
Abstract
Deep neural networks (DNNs) have achieved unprecedented success in many fields. However, robustness and trustworthiness have become emerging concerns since DNNs are vulnerable to various attacks and susceptible to data distributional shifts. Attacks such as data poisoning and out-of-distribution scenarios such as natural corruption significantly undermine the performance and robustness of DNNs in model training and inference and impose uncertainty and insecurity on the deployment in real-world applications. Thus, it is crucial to investigate threats and challenges against deep neural networks, develop corresponding countermeasures, and dig into design tactics to secure their safety and reliability. The works investigated in this dissertation constitute our pioneering efforts in robust and trustworthy deep learning from perspectives of attacks, defenses and designs.
Recommended Citation
Zhao, Bingyin, "Robust and Trustworthy Deep Learning: Attacks, Defenses and Designs" (2024). All Dissertations. 3637.
https://open.clemson.edu/all_dissertations/3637
Author ORCID Identifier
https://orcid.org/0000-0003-0372-8198
Included in
Artificial Intelligence and Robotics Commons, Electrical and Computer Engineering Commons