AI-POWERED SMART EXAMINATION MALPRACTICE DETECTION SYSTEM

Abstract

The AI-Powered Smart Examination Malpractice Detection System is an intelligent online proctoring solution developed to ensure fairness, security, and transparency in online examinations using Artificial Intelligence (AI) and Computer Vision. The system addresses the increasing challenges of examination malpractice, where traditional invigilation methods are costly, time-consuming, and unable to effectively monitor multiple candidates simultaneously. It automates the examination process by registering students through facial image capture and continuously verifying their identity during the examination using face recognition techniques. The system also detects suspicious activities such as unknown or multiple faces, abnormal head movements, and prohibited objects like mobile phones through real-time webcam monitoring. Whenever a violation is detected, it generates instant alerts, records malpractice incidents, and can automatically terminate the examination if repeated violations occur. The application is developed using Python, Flask, HTML, CSS, JavaScript, OpenCV, NumPy, Pandas, SQLite, and the YOLO object detection algorithm. It employs HOG-based face detection, face encoding and matching, Euclidean distance-based face comparison, and realtime computer vision processing to provide accurate identity verification and continuous monitoring. By automating student authentication, live surveillance, alert generation, attendance tracking, and report management, the proposed system minimizes manual intervention, enhances examination integrity, and provides a reliable, efficient, and cost-effective solution for educational institutions and online learning platforms conducting secure remote examinations. KEYWORDS: Artificial Intelligence (AI), Online Examination, Face Recognition, YOLO Object Detection, Real-Time Monitoring, Malpractice Detection, Computer Vision, Flask.