A Novel CNN-RNN Model for E-Cheating Detection Based on Video Surveillance


  • Aqeel Zaffar Dextercode LLC, Software company Islamabad
  • Muhammad Jawad Pakistan Ordinance Factory, Wah Cantt.
  • Muzammil Shabbir


Detect Suspicious Activities, Person Detection, Person Tracking, YOLOv7, CNN-RNN, Physical Exam, DeepSort, Face Recognition, Smart Attendance


Nowadays, everything needs to be digitized, and scientific knowledge is constantly bringing comfort and change to everyday life. Autonomous systems have become a prominent technology in recent years, with a variety of applications in different fields. Our proposed system is designed to maintain academic integrity in exams. The system uses computer vision techniques to monitor the behavior of students during the exam and detect any suspicious activities, such as looking at someone else's paper or using unauthorized materials. The proposed E-Cheating Detection consists of four core steps: 1) Student/Person Detection and Tracking, 2) Detect suspicious activities, 3) Generating alerts, and 4) Mark attendance. Student detection from videos is performed by using YOLOv7 and DeepSort tracker is used to track the detected persons that are being detected by the YOLOv7 algorithm. To classify suspicious activities (such as the exchange of paper, and giving codes to one another, etc.) the system uses CNN-RNN architecture in which the inceptionV3 model is used for feature extraction. The system will generate real-time alerts for suspicious behavior by sending an email via SMTP to the exam administration /invigilator. The system marks the student's attendance by recognizing and matching student faces that are stored in the database. The performance of the system will be evaluated by conducting a series of experiments using simulated scenarios, and the results will demonstrate the effectiveness of the proposed system in detecting suspicious activities during physical exams. The proposed system has the potential to promote exam integrity and create a fair environment for all students, ultimately improving the education system's quality.

Keywords: Detect Suspicious Activities; CNN-RNN, YOLOv7; DeepSort; Face Recognition; Physical Exam; Smart Attendance


S. Zhao et al., "VESPA: A General System for Vision-Based Extrasensory Perception Anti-Cheating in Online FPS Games," IEEE Transactions on Games, pp. 1-10, 2023.

M. L. Farnese, C. Tramontano, R. Fida, and M. Paciello, "Cheating behaviors in academic context: Does academic moral disengagement matter?," Procedia-Social and Behavioral Sciences, vol. 29, pp. 356-365, 2011.

D. L. McCabe, L. K. Treviño, and K. D. Butterfield, "Cheating in academic institutions: A decade of research," Ethics &Behavior, vol. 11, no. 3, pp. 219-232, 2001.

P. M. Newton and K. Essex, "How common is cheating in online exams and did it increase during the COVID-19 pandemic? A Systematic Review," Journal of Academic Ethics, pp. 1-21, 2023.

D. A. Odongo, E. Agyemang, and J. B. Forkuor, "Innovative approaches to cheating: An exploration of examination cheating techniques among tertiary students," Education Research International, vol. 2021, pp. 1-7, 2021.

J. Nishchal, S. Reddy, and P. N. Navya, "Automated Cheating Detection in Exams using Posture and Emotion Analysis," in 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), 2-4 July 2020, pp. 1-6,2020.

Y. Ma, D. L. McCabe, and R. Liu, "Students’ academic cheating in Chinese universities: Prevalence, influencing factors, and proposed action," Journal of Academic ethics, vol. 11, no. 3, pp. 169-184, 2013.

R. Bawarith, A. Basuhail, A. Fattouh, and S. Gamalel-Din, "E-exam cheating detection system," International Journal of Advanced Computer Science and Applications, vol. 8, no. 4, pp. 1-6, 2017.

Y. Atoum, L. Chen, A. X. Liu, S. D. Hsu, and X. Liu, "Automated online exam proctoring," IEEE Transactions on Multimedia, vol. 19, no. 7, pp. 1609-1624, 2017.

F. Choo and K. Tan, "Abrupt academic dishonesty: Pressure, opportunity, and deterrence," The International Journal of Management Education, vol. 21, no. 2, pp. 1-15, 2023.

F. Ozdamli, A. Aljarrah, D. Karagozlu, and M. Ababneh, "Facial Recognition System to Detect Student Emotions and Cheating in Distance Learning," Sustainability, vol. 14, no. 20, p. 13230, 2022. [Online]. Available: https://www.mdpi.com/2071-1050/14/20/13230.

N. Gupta and B. B. Agarwal, "Suspicious Activity Classification in Classrooms using Deep Learning," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12226-12230, 2023.

A. Nigam, R. Pasricha, T. Singh, and P. Churi, "A Systematic Review on AI-based Proctoring Systems: Past, Present and Future," Education and Information Technologies, vol. 26, pp. 6421-6445, 09/01 2021.

T. Liu, "AI proctoring for offline examinations with 2-Longitudinal-Stream Convolutional Neural Networks," Computers and Education: Artificial Intelligence, vol. 4, pp. 1-15, 2023.

L. Baran and P. K. Jonason, "Academic dishonesty among university students: The roles of the psychopathy, motivation, and self-efficacy," PLOS ONE, vol. 15, no. 8, pp. 1-18, 2020.

R. a. 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," International Journal of Interactive Mobile Technologies (iJIM), vol. 16, no. 08, pp. pp. 124-137, 04/26 2022.

X. G. Yu, J. Y. Sun, B. He, J. J. Zhuang, and Z. C. Dai, "Design and Implementation of Automatic Invigilation Functions Using the Embedded Technology," Procedia Computer Science, vol. 166, pp. 41-45, 2020/01/01/ 2020.

M. Adil, R. Simon, and S. K. Khatri, "Automated Invigilation System for Detection of Suspicious Activities during Examination," in 2019 Amity International Conference on Artificial Intelligence (AICAI), 4-6 Feb. 2019 , pp. 361-366, 2019.

A. Arinaldi and M. I. Fanany, "Cheating video description based on sequences of gestures," in 2017 5th International Conference on Information and Communication Technology (ICoIC7), 17-19 May 2017 2017, pp. 1-6,2017.

M. Asadullah and S. Nisar, "An automated technique for cheating detection," in 2016 Sixth International Conference on Innovative Computing Technology (INTECH), 24-26 Aug. 2016, pp. 251-255, 2016.

F. Kamalov, H. Sulieman, and D. Santandreu Calonge, "Machine learning based approach to exam cheating detection," PLOS ONE, vol. 16, no. 8, pp. 1-15, 2021.

Y. Atoum, L. Chen, A. X. Liu, S. D. H. Hsu, and X. Liu, "Automated Online Exam Proctoring," IEEE Transactions on Multimedia, vol. 19, no. 7, pp. 1609-1624, 2017.

D. Felsinger, T. Halloluwa, and I. Fonseka, "Video based action detection for online exam proctoring in resource-constrained settings," Education and Information Technologies, pp. 1-15, 2023.

M. Garg and A. Goel, "A systematic literature review on online assessment security: Current challenges and integrity strategies," Computers & Security, vol. 113, p. 1-14, 2022.

M. Garg and A. Goel, "Preserving integrity in online assessment using feature engineering and machine learning," Expert Systems with Applications, vol. 225, p. 1-12, 2023.

J. A. Oravec, "AI, Biometric Analysis, and Emerging Cheating Detection Systems: The Engineering of Academic Integrity?," Education Policy Analysis Archives, vol. 30, no. 175, pp. 1-18, 2022.

S. Wang, D. Wu, and X. Zheng, "TBC-YOLOv7: a refined YOLOv7-based algorithm for tea bud grading detection," Frontiers in Plant Science, vol. 14, pp. 1-7 2023.

A. B. Chien-Yao Wang, Hong-Yuan Mark Liao, "YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors," 6 July, 2023. [Online]. Available: https://arxiv.org/abs/2207.02696

N. Wojke, A. Bewley, and D. Paulus, "Simple online and realtime tracking with a deep association metric," in 2017 IEEE International Conference on Image Processing (ICIP), 17-20 Sept. 2017, pp. 3645-3649, 2017.

C. Fan et al., "ICaps-ResLSTM: Improved capsule network and residual LSTM for EEG emotion recognition," Biomedical Signal Processing and Control, vol. 87, p. 1-15, 2024.

R. S. Priya, S. Shanmugavadivel, M. S. M. Sivaraja, and M. S. Francis, "Network based Learning Platform Application Model for Enhancement of Realtime Working Systems," in 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), 2023: IEEE, pp. 1234-1239.

J. A. Mahajan and A. N. Paithane, "Face detection on distorted images by using quality HOG features," in 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), 10-11 March 2017, pp. 439-444, 2017.

S. Syed Danish Ahmad, U. Muhammand, and G. Eman, "An Intelligent Health Control Security Robotic System," University of Wah Journal of Computer Science, vol. 4, no. 1, pp. 17-30, 01/02 2023. [Online]. Available: https://uwjcs.org.pk/index.php/ojs/article/view/55.

A. Liang, W. Liu, L. Li, M. R. Farid, and V. Le, "Accurate facial landmarks detection for frontal faces with extended tree-structured models," in 2014 22nd International Conference on Pattern Recognition, 2014: IEEE, pp. 538-543.

W. Alsabhan, "Student Cheating Detection in Higher Education by Implementing Machine Learning and LSTM Techniques," Sensors, vol. 23, no. 8, pp. 1-19, 2023.



How to Cite

Aqeel Zaffar, Muhammad Jawad, & Muzammil Shabbir. (2023). A Novel CNN-RNN Model for E-Cheating Detection Based on Video Surveillance. University of Wah Journal of Computer Science, 5, 1–13. Retrieved from http://uwjcs.org.pk/index.php/ojs/article/view/64