Real-Time Integrity Monitoring in Online Exams Using Deep Learning Model

Guruprasad Konnurmath, Pratham Shirol, Nitin Nagaral, Devaraj Hireraddi, Kushal Patil, Girish Dongrekar

2025

Abstract

The rapid shift towards online education and remote assessments has intensified the challenge of ensuring academic integrity. This research presents a comprehensive integrity monitoring called as cheat detection system utilizing the YOLOv8 model, a state of art deep learning model designed to enhance the credibility of online examinations. Our system employs a live video feed from a webcam to monitor examinees or individuals taking an examination in real-time, detecting multiple persons, unauthorized gadgets (such as mobile phones, laptops, and headphones), and eye movements indicative of potential cheating. The YOLOv8 model is trained to accurately recognize these objects and behaviors, triggering immediate alerts upon suspicious detection. The research paper details the design, implementation, and evaluation of this system, demonstrating its efficacy in maintaining the integrity of online exams. Our results indicate that the system can significantly reduce cheating incidents, offering a robust solution applicable to educational institutions, certification bodies, and other scenarios requiring stringent monitoring.

Paper Citation

in Harvard Style

Konnurmath G., Shirol P., Nagaral N., Hireraddi D., Patil K. and Dongrekar G. (2025). Real-Time Integrity Monitoring in Online Exams Using Deep Learning Model. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 427-432. DOI: 10.5220/0013620700004664

in Bibtex Style

@conference{incoft25,
author={Guruprasad Konnurmath and Pratham Shirol and Nitin Nagaral and Devaraj Hireraddi and Kushal Patil and Girish Dongrekar},
title={Real-Time Integrity Monitoring in Online Exams Using Deep Learning Model},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={427-432},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013620700004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Real-Time Integrity Monitoring in Online Exams Using Deep Learning Model
SN - 978-989-758-763-4
AU - Konnurmath G.
AU - Shirol P.
AU - Nagaral N.
AU - Hireraddi D.
AU - Patil K.
AU - Dongrekar G.
PY - 2025
SP - 427
EP - 432
DO - 10.5220/0013620700004664
PB - SciTePress