BubbleSig: Same-Hand Ballot Stuffing Detection

To enhance the integrity of mail-in voting, we introduce BubbleSig, an AI-assisted framework that detects samehand ballot stuffing by analyzing "signature-like" patterns on ballot marks. This method is voter-independent that relies solely on discrepancies in marking styles across different ballots, thus preserving voter anonymity and avoiding the use of biometric or historical voter data, e.g., fingerprints or signatures. Capable of handling diverse ballot formats and layouts with a single model, BubbleSig eliminates the need for retraining across election cycles. Its efficacy is demonstrated through real election data, achieving an F1 score of 0.925 on Same-Hand Ballot Stuffing Detection dataset, 100% accuracy in both mark and ballot level stuffing detection for a small set of real ballots known filled by the same person, and notable mean Average Precision (mAP) and Hit Rate (HR) scores in retrieving ballots suspected of stuffing, using expanded test sets containing both real and synthetic ballots. Our experimental results demonstrate the model's promise to handle diverse data collection processes, including variations in scanner types and scanning resolutions, and generalizability to various real-world ballot formats and layouts, underscoring its practical applicability. While the AI tool significantly aids in flagging potential ballot stuffing activities, final adjudications on ballot legitimacy remain with election officials, who examine suspicious ballots returned by the AI tool using the physical evidence on paper ballots.