Exam Cheating Detection System with Multiple-Human Pose Estimation

Cheating in exams is a persistent problem that contributes to academic dishonesty. In this paper we explore a variety of related work proposed as a solution for exam cheating, then we propose an exam cheating detection system that works for both on-site and online examinations. The proposed system applies Human Pose Estimation that includes both single-user and multiple-user tracking algorithms. Based on video footage, the system can detect whether or not a student is cheating by continuously validating their head posture and hand movement conditions during the exam. The system doesn't fully imply a student is cheating, instead, we use the term ‘warning’ for the output to indicate that the student has met an abnormal condition that is similar to cheating behavior. At last, we validate the system usage in real-life examination environments through two different experiments that resulted in accuracy numbers of 92%-97% in cheating detection.

I. Introduction

Exam cheating is a very common issue in many communi-ties. It is considered the number one cause of inefficiency and unproductiveness that range from primary education to higher education [1]. While there is on-site cheating that occurs in places such as classrooms and lecture halls, with the COVID-19 pandemic, online cheating has increased dramatically be-cause of the switch from offline to online learning at homes. Many students have acknowledged to cheating on online tests as a result of the stress caused by the COVID-19 quarantine and their concern about their GPAs [2].