3 papers
cs.CR2026
Sublinear Risk-Limiting Audits from Direct Ballot Selection and Statistical Ballot Manifests
Benjamin Fuller, Abigail Harrison, Alexander Russell
Risk-limiting audits (RLAs) are post-election auditing procedures that rigorously guarantee a specified maximum probability that an incorrect electoral outcome will not be detected…
cs.LG2026
Analyzing Physical Adversarial Example Threats to Machine Learning in Election Systems
Khaleque Md Aashiq Kamal, Surya Eada, Aayushi Verma +4
Developments in the machine learning voting domain have shown both promising results and risks. Trained models perform well on ballot classification tasks (> 99% accuracy) but are…
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…