International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | To Implement an Intelligent Analysis System for Exam Malpractice Prevention |
|---|---|
| ABSTRACT | Exam malpractice significantly undermines the credibility and fairness of educational assessments, particularly in the era of online and remote examinations. Traditional manual invigilation methods lack scalability, consistency, and accuracy, leading to significant challenges in maintaining academic integrity. This project presents an intelligent analysis system for exam malpractice prevention that leverages artificial intelligence, computer vision, and deep learning technologies to provide automated, real-time detection of suspicious activities during examinations. The proposed system integrates seven detection models working in parallel: gaze tracking, face recognition, audio detection, behavior analysis, pose estimation, hand gesture detection, and object detection. Each model specializes in identifying specific types of malpractice, including looking away from the screen, identity verification failures, multiple people detection, suspicious hand movements, unauthorized posture changes, hand gesture patterns, and prohibited objects such as mobile phones or notes. The system operates through two primary modes: real-time camera monitoring for live examination supervision with instant alerts, and local video file analysis for post-examination review with detailed violation reports. Built using Python, Flask, OpenCV, MediaPipe, and TensorFlow, the system achieves an average detection accuracy of 86% while maintaining real-time performance at 28-30 FPS. |
| AUTHOR | GANDHIMATHI.D, GANGA.V, SINDHUJA.S, SARANYA.G Assistant Professor, Department of Computer Science and Engineering, P.S.V College of Engineering and Technology, Mittapalli, Krishnagiri, India. UG Scholar, Department of Computer Science and Engineering, P.S.V College of Engineering and Technology, Mittapalli, Krishnagiri, India. |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404050 |
| pdf/50_To Implement an Intelligent Analysis System for Exam Malpractice Prevention.pdf | |
| KEYWORDS | |
| References | [1] Mrs. Bhagya, Balaji N, Chandan R, Ganesh M, Jeevan Yadav S, "Automated Detection of Exam Malpractice", IJARCCE, 2025. Available: https://ijarcce.com/papers/automated-detection-of-exam-malpractice/ [2] Aislyn Engineering Projects Team, "AI Exam Proctor System Using Computer Vision and Deep Learning", Aislyn Engineering Projects, 2024. Available: https://aislyn.in/engineering-projects/ai-exam-proctor-system [3] Multiple Researchers, "Real-Time Exam Monitoring with YOLO", IJARSCT, 2024. Available: https://www.ijarsct.co.in/Paper24437.pdf [4] Various Researchers, "Smart Proctoring System Using Machine Learning", IJRPR, 2024. Available: https://ijrpr.com/uploads/V6ISSUE8/IJRPR52234.pdf [5] Ready Tensor AI Team, "Exam Malpractice Detector with OpenCV", Ready Tensor AI, 2024. Available: https://app.readytensor.ai/publications/exam-malpractice-detector [6] Multiple Researchers, "Multimodal Authentication System", JISEM Journal, 2025. Available: https://jisem-journal.com/index.php/journal/article/view/11354 [7] Various Researchers, "Head Pose Estimation for Proctoring", IJRPR, 2024. Available: https://ijrpr.com/uploads/V6ISSUE8/IJRPR52234.pdf [8] Multiple Researchers, "Object Detection for Unauthorized Items", IJARSCT, 2024. Available: https://www.ijarsct.co.in/Paper24437.pdf [9] Various Researchers, "Real-Time Integrity Monitoring Using Deep Learning", SCITEPRESS, 2025. Available: https://www.scitepress.org/Papers/2025/136207/136207.pdf [10] Multiple Researchers, "Behavioral Analysis in Online Exams", Various Sources, 2024. [11] Redmon, J., et al., "You Only Look Once: Unified, Real-Time Object Detection", IEEE CVPR, 2016. [12] Lugaresi, C., et al., "MediaPipe: A Framework for Building Perception Pipelines", arXiv:1906.08172, 2019. [13] Abadi, M., et al., "TensorFlow: A System for Large-Scale Machine Learning", 12th USENIX Symposium on OSDI, 2016. [14] Bradski, G., "The OpenCV Library", Dr. Dobb's Journal of Software Tools, 2000. |
