REAL-TIME CHEATING DETECTION IN ONLINE EXAMS USING YOLOV11

Vinnakoti Swarna Latha Vikas, N.S.C Mohana Rao

Abstract


With the rise of online examinations, ensuring academic integrity has become increasingly challenging due to the lack of human supervision. This research explores the use of pre-trained convolutional neural networks (CNNs) to detect abnormal behaviors such as head movement, device usage, multiple persons, and talking during exams. The original study achieved strong results using YOLOv5, InceptionV3, and DenseNet121. However, to further enhance detection performance, this extension introduces YOLOv11, which surpasses previous models in precision, recall, and MAP50-95 metrics. Our approach extracts motion-based keyframes from video streams and applies deep learning to classify cheating activities in real-time via webcam integration. Comparative results indicate YOLOv11 consistently exceeds 70% MAP50-95 across all classes, improving on YOLOv8’s variability and YOLOv5’s limitations. This extension demonstrates that advanced CNN architectures can significantly bolster the reliability of online proctoring systems and help restore fairness in virtual academic assessments.

References


A. Gupta and A. Bhat, ‘‘Bluetooth camera based online examination system with deep learning,’’ in Proc. 6th Int. Conf. Intell. Comput. Control Syst. (ICICCS), May 2022, pp. 1477–1480, doi: 10.1109/ICICCS53718.2022.9788147.

M. M. Masud, K. Hayawi, S. S. Mathew, T. Michael, and M. E. Barachi, ‘‘Smart online exam proctoring assist for cheating detection,’’ Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13087. Cham, Switzerland: Springer, 2022, pp. 118–132, doi: 10.1007/978-3-030-95405- 5_9.

R. M. Al_airaji, I. A. Aljazaery, H. T. S. Alrikabi, and A. H. M. Alaidi, ‘‘Automated cheating detection based on video surveillance in the examination classes,’’ Int. J. Interact. Mobile Technol. (iJIM), vol. 16, no. 08, pp. 124–137, Apr. 2022, doi: 10.3991/ijim.v16i08.30157.

R. M. Alairaji, I. A. Aljazaery, and H. S. Alrikabi, ‘‘Abnormal behavior detection of students in the examination Hall from surveillance videos,’’ in Advanced Computational Paradigms and Hybrid Intelligent Computing. Singapore: Springer, 2022, pp. 113–125, doi: 10.1007/978-981-16-4369- 9_12.

A. A. Malik, M. Hassan, M. Rizwan, I. Mushtaque, T. A. Lak, and M. Hussain, ‘‘Impact of academic cheating and perceived online learning effectiveness on academic performance during the COVID-19 pandemic among Pakistani students,’’ Frontiers Psychol., vol. 14, Mar. 2023, Art. no. 1124095.

D. L. McCabe, ‘‘Cheating among college and university students: A north American perspective,’’ Int. J. Educ. Integrity, vol. 1, no. 1, Nov. 2005, doi: 10.21913/ijei.v1i1.14.

S. A. Butt, ‘‘Analysis of unfair means cases in computer-based examination systems,’’ Pacific Sci. Rev. B, Humanities Social Sci., vol. 2, no. 2, pp. 75–79, Jul. 2016.

M. Ramzan and A. Abid, Automatic Unusual Activities Recognition Using Deep Learning in Academia. Accessed: Jun. 20, 2022.[Online]. Available: https://www.academia.edu/download/74918847/pdf.pdf

T. S. Kumar and G. Narmatha, ‘‘Video analysis for malpractice detection in classroom examination,’’ in Proc. Int. Conf. Soft Comput. Syst., in Advances in Intelligent Systems and Computing, vol. 397, 2016, pp. 135–146, doi: 10.1007/978-81-322-2671-0_13.

[Z. Li, Z. Zhu, and T. Yang, ‘‘A multi-index examination cheating detection method based on neural network,’’ in Proc. IEEE 31st Int. Conf. Tools Artif. Intell. (ICTAI), Nov. 2019, pp. 575–581.

N. Malhotra, R. Suri, P. Verma, and R. Kumar, ‘‘Smart artificial intelligence based online proctoring system,’’ in Proc. IEEE Delhi Sect. Conf. (DELCON), Feb. 2022, pp. 1–5, doi: 10.1109/DELCON54057.2022.9753313.

D. Komosny and S. U. Rehman, ‘‘A method for cheating indication in unproctored on-line exams,’’ Sensors, vol. 22, no. 2, p. 654, Jan. 2022, doi: 10.3390/s22020654.

A. Singh and S. Das, ‘‘A cheating detection system in online examinations based on the analysis of eye-gaze and head-pose,’’ in Proc. Int. Conf. Emerg. Trends Artif.Intell. Smart Syst., Jun. 2022, doi: 10.4108/EAI.16-4- 2022.2318165.

L. C. OwTiong and H. J. Lee, ‘‘E-cheating prevention measures: Detection of cheating at online examinations using deep learning approach—A case study,’’ 2021, arXiv:2101.09841.

G. Kasliwal, ‘‘Cheating detection in online examinations,’’ Master’s Projects, San José State Univ., San Jose, CA, USA, Tech. Rep., 2015, doi: 10.31979/etd.y292-cddh.

D. Dobrovska, ‘‘Technical student electronic cheating on examination,’’ in Proc. Int. Conf. Interact. Collaborative Learn., in Advances in Intelligent Systems and Computing, vol. 544, 2017, pp. 525–531, doi: 10.1007/978- 3-319-50337-0_49.

S. Hu, X. Jia, and Y. Fu, ‘‘Research on abnormal behavior detection of online examination based on image information,’’ in Proc. 10th Int. Conf. Intell. Hum.-Mach. Syst. Cybern. (IHMSC), vol. 2, Aug. 2018, pp. 88–91, doi: 10.1109/IHMSC.2018.10127.

A. Fayyoumi and A. Zarrad, ‘‘Novel solution based on face recognition to address identity theft and cheating in online examination systems,’’ Adv. Internet Things, vol. 4, no. 2, pp. 5–12, 2014, doi: 10.4236/AIT.2014.42002.

E. Bilen and A. Matros, ‘‘Online cheating amid COVID-19,’’ J. Econ. Behav. Org., vol. 182, pp. 196–211, Feb. 2021, doi: 10.1016/j.jebo. 2020.12.004.

R. Comas-Forgas, T. Lancaster, A. Calvo-Sastre, and J. Sureda-Negre, ‘‘Exam cheating and academic integrity breaches during the COVID-19 pandemic: An analysis of internet search activity in Spain,’’ Heliyon, vol. 7,


Full Text: PDF [Full Text]

Refbacks

  • There are currently no refbacks.


Copyright © 2013, All rights reserved.| ijseat.com

Creative Commons License
International Journal of Science Engineering and Advance Technology is licensed under a Creative Commons Attribution 3.0 Unported License.Based on a work at IJSEat , Permissions beyond the scope of this license may be available at http://creativecommons.org/licenses/by/3.0/deed.en_GB.

Â