Exploring Vulnerability and Robustness of Deep Learning Systems
Abstract
Deep neural networks have enabled modern computer vision systems to reach new heights of performance on a variety of challenging tasks. Despite the accuracy and efficiency benefits, the highly parameterized non-linear nature of deep networks makes them very difficult to interpret and prone to failure in the presence of an adversary or anomalous data. This vulnerability makes the integration of these models into our real-world systems troubling. This project has two broad threads: (1) we explore the vulnerability of deep neural networks by developing state-of-the-art adversarial attacks, and (2) we improve the robustness of models in challenging operating environments such as in open-world target recognition and federating learning scenarios.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 2022
- Accession Number
- AD1170105
Entities
People
- Jingyang Zhang
- Mathew Inkawhich
- Randy Linderman
- Yiran Chen
Organizations
- Duke University