Abstract :

This Paper Presents An AIPowered Smart Examination Malpractice Detection System Designed To Improve The Security And Fairness Of Online Examinations. The Main Objective Of This Project Is To Detect And Prevent Malpractice Automatically By Monitoring Students During An Exam Using Artificial Intelligence (AI) And Computer Vision. As Online Examinations Have Become More Common, It Has Become Difficult For Human Invigilators To Monitor Every Student Continuously. This May Lead To Cheating And Unfair Examination Practices. To Solve This Problem, The Proposed System Uses A Webcam To Monitor Students In Real Time. It Verifies The Students Identity Using Face Detection And Ensures That Only One Person Is Present During The Examination. The System Also Detects Suspicious Activities Such As Looking Away For A Long Time, Multiple People Appearing In Front Of The Camera, The Students Face Not Being Visible, And Mobile Phone Usage. These Activities Are Recorded Automatically, And Alerts Are Generated Whenever Malpractice Is Detected. The System Is Developed Using Python, Flask, OpenCV, And MediaPipe. OpenCV And MediaPipe Are Used For Realtime Face Detection And Activity Monitoring, While Flask Provides A Simple And Easy-to-use Web Interface. The Detected Violations Are Stored In The System And Can Be Viewed Later By The Administrator As Examination Reports. KEYWORDS: Artificial Intelligence (AI), Machine Learning (ML), Computer Vision, OpenCV, MediaPipe, Face Detection, Face Recognition, Head Pose Estimation

Published:

05-8-2026

Issue:

Vol. 26 No. 8 (2026)

Page Nos:

646 - 653

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite