A Two-Stage, Object-Centric Deep Learning Framework for Robust Exam Cheating Detection

ArXi:2604.16234v1 Announce Type: cross Academic integrity continues to face the persistent challenge of examination cheating. Traditional invigilation relies on human observation, which is inefficient, costly, and prone to errors at scale. Although some existing AI-powered monitoring systems have been deployed and trusted, many lack transparency or require multi-layered architectures to achieve the desired performance.