  Examinations are a crucial part of the learning process, and academic
institutions invest significant resources into maintaining their integrity by
preventing cheating from students or facilitators. However, cheating has become
rampant in examination setups, compromising their integrity. The traditional
method of relying on invigilators to monitor every student is impractical and
ineffective. To address this issue, there is a need to continuously record exam
sessions to monitor students for suspicious activities. However, these
recordings are often too lengthy for invigilators to analyze effectively, and
fatigue may cause them to miss significant details. To widen the coverage,
invigilators could use fixed overhead or wearable cameras. This paper
introduces a framework that uses automation to analyze videos and detect
suspicious activities during examinations efficiently and effectively. We
utilized the OpenPose framework and Convolutional Neural Network (CNN) to
identify students exchanging objects during exams. This detection system is
vital in preventing cheating and promoting academic integrity, fairness, and
quality education for institutions.
