Literature Review on Adversarial Machine Learning (FINS)

Surveying adversarial ML research with a focus on security applications.

PI / Advisor: Dr. Damon Woodard
Institution / Department: Florida Institute for National Security (FINS), University of Florida
Timeframe: May 2025–present

Research Focus

This project is a literature review examining recent advances in adversarial machine learning, with an emphasis on applications relevant to national security. The review investigates how adversarial attacks are formulated, defenses are designed, and vulnerabilities are analyzed in modern ML systems.

Responsibilities

I am compiling and synthesizing findings from leading conferences and journals in ML and security, organizing them into themes that will inform future projects at FINS. This work is ongoing and aims to provide both a technical overview and practical insights for research directions.

Adversarial panda example
Figure adapted from Goodfellow, Shlens, and Szegedy (2015), “Explaining and Harnessing Adversarial Examples”.