2 papers
cs.CR2025
Busting the Paper Ballot: Voting Meets Adversarial Machine Learning
Kaleel Mahmood, Caleb Manicke, Ethan Rathbun +5
We show the security risk associated with using machine learning classifiers in United States election tabulators. The central classification task in election tabulation is decidin…
cs.LG2024
Theoretical Corrections and the Leveraging of Reinforcement Learning to Enhance Triangle Attack
Nicole Meng, Caleb Manicke, David Chen +4
Adversarial examples represent a serious issue for the application of machine learning models in many sensitive domains. For generating adversarial examples, decision based black-b…