Keywords
Adversarial Robustness; Neural Networks; Attribution Analysis
Abstract
Computer vision algorithms, including image classifiers and object detectors, play a pivotal role in various cyber-physical systems, spanning from facial recognition to self-driving vehicles and security surveillance. However, the emergence of real-world adversarial patches, which can be as simple as stickers, poses a significant threat to the reliability of AI models utilized within these systems. To address this challenge, several defense mechanisms such as PatchGuard, Minority Report, and (De)Randomized Smoothing have been proposed to enhance the resilience of AI models against such attacks. In this thesis, we introduce a novel framework that integrates masking with attribution analysis to robustify AI models against adversarial patch assaults. Attribution analysis identifies the crucial pixels influencing the model's decision-making process. Subsequently, inspired by the Derandomized Smoothing defense strategy, we employ a masking approach to mask these important pixels. Our experimental findings demonstrate improved robustness against adversarial attacks, at the expense of a slight degradation in clean accuracy.
Thesis Completion Year
2024
Thesis Completion Semester
Spring
Thesis Chair
Ewetz, Rickard
College
College of Engineering and Computer Science
Department
Department of Computer Science
Thesis Discipline
Computer Science
Language
English
Access Status
Open Access
Length of Campus Access
None
Campus Location
Orlando (Main) Campus
STARS Citation
Mahalder, Atandra, "Improving the Robustness of Neural Networks to Adversarial Patch Attacks Using Masking and Attribution Analysis" (2024). Honors Undergraduate Theses. 5.
https://stars.library.ucf.edu/hut2024/5