An Anti-Cheating System for Online Interviews and Exams
Keywords:
Cheating detection, Face detection, Object detection, Face tracking, Video processingAbstract
Remote examination and job interviews have gained popularity and become indispensable because of both pandemics and the advantage of remote working circumstances. Most businesses and educational organizations use these platforms for recruitment as well as online exams. However, one of the critical problems of the remote examination systems is conducting the exams in a reliable environment. In this work, we present a cheating analysis pipeline for online interviews and exams. The system only requires a video of the candidate, which is recorded during the exam by using a webcam without a need for any extra tool. Then cheating detection pipeline is employed to detect the presence of another person, electronic device usage, and candidate absence status. The pipeline consists of face detection, face recognition, object detection, and face tracking algorithms. To evaluate the performance of the pipeline we collected a private video dataset. The video dataset includes both cheating activities and clean videos. Ultimately, our pipeline presents an efficient and fast guideline for detecting and analyzing cheating actions in an online interview and exam video.
References
S. Manoharan, X. Ye, On upholding academic integrity in online examinations, in: 2020 IEEE Conference on e-Learning, e-Management and e-Services (IC3e), IEEE, 2020, pp. 33-37.
D. L. King, C. J. Case, E-cheating: Incidence and trends among college students., Issues in Information Systems 15 (1) (2014).
Y. Atoum, L. Chen, A. X. Liu, S. D. Hsu, X. Liu, Automated online exam proctoring, IEEE Transactions on Multimedia 19 (7) (2017) 1609-1624.
S. Prathish, K. Bijlani, et al., An intelligent system for online exam monitoring, in: 2016 International Conference on Information Science (ICIS), IEEE, 2016, pp. 138-143.
H. S. Asep, Y. Bandung, A design of continuous user verification for online exam proctoring on m-learning, in: 2019 International Conference on Electrical Engineering and Informatics (ICEEI), IEEE, 2019, pp. 284-289.
A. C. Ozgen, M. U. Öztürk, O. Torun, J. Yang, M. Z. Alparslan, Cheating detection pipeline for online interviews, in: 2021 29th Signal Processing and Communications Applications Conference (SIU), IEEE, 2021, pp. 1-4.
B. Poutre, D. Hedlund, W. Nau, Combining testing software, online proctoring and lockdown browsers to assure a secure assessment environment for students in hybrid or online programs (poster 13), Creighton University, Office of Academic Excellence and Assessment (2015).
S. Arno, A. Galassi, M. Tommasi, A. Saggino, P. Vittorini, State-ofthe-art of commercial proctoring systems and their use in academic online exams, International Journal of Distance Education Technologies (IJDET) 19 (2) (2021) 41-60.
G. Cluskey Jr, C. R. Ehlen, M. H. Raiborn, Thwarting online exam cheating without proctor supervision, Journal of Academic and Business Ethics 4 (1) (2011) 1-7.
A. Wahid, Y. Sengoku, M. Mambo, Toward constructing a secure online examination system, in: Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication, 2015, pp. 1-8.
J. Opgen-Rhein, B. Küppers, U. Schroeder, An application to discover cheating in digital exams, in: Proceedings of the 18th Koli Calling International Conference on Computing Education Research, 2018, pp. 1-5.
P. Guo, et al., The research and application of online examination and monitoring system, in: 2008 IEEE International Symposium on IT in Medicine and Education, IEEE, 2008, pp. 497-502.
I. Y. Jung, H. Y. Yeom, Enhanced security for online exams using group cryptography, IEEE transactions on Education 52 (3) (2009) 340-349.
G. Anielak, G. Jakacki, S. Lasota, Incremental test case generation using bounded model checking: an application to automatic rating, International Journal on Software Tools for Technology Transfer 17 (3) (2015) 339-349.
S. Vamsi, V. Balamurali, K. S. Teja, P. Mallela, Classifying difficulty levels of programming questions on hackerrank, in: Advances in Decision Sciences, Image Processing, Security and Computer Vision, Springer, 2020, pp. 301-308.
H.-K. Pao, J. Fadlil, H.-Y. Lin, K.-T. Chen, Trajectory analysis for user verification and recognition, Knowledge-Based Systems 34 (2012) 81-90.
X. Li, K.-m. Chang, Y. Yuan, A. Hauptmann, Massive open online proctor: Protecting the credibility of moocs certificates, in: Proceedings of the 18th ACM conference on computer supported cooperative work & social computing, 2015, pp. 1129-1137.
L. S. Nguyen, D. Frauendorfer, M. S. Mast, D. Gatica-Perez, Hire me: Computational inference of hirability in employment interviews based on nonverbal behavior, IEEE Transactions on Multimedia 16 (4) (2014) 1018-1031.
A. K. Pandey, S. Kumar, B. Rajendran, B. Bindhumadhava, E-parakh: Unsupervised online examination system, in: 2020 IEEE REGION 10 CONFERENCE (TENCON), IEEE, 2020, pp. 667-671.
E. Winarno, W. Hadikurniawati, I. H. Al Amin, M. Sukur, Anti-cheating presence system based on 3wpca-dual vision face recognition, in: 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), IEEE, 2017, pp. 1-5.
N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05), Vol. 1, Ieee, 2005, pp. 886-893.
D. E. King, Dlib-ml: A machine learning toolkit, The Journal of Machine Learning Research 10 (2009) 1755-1758.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, C. L. Zitnick, Microsoft coco: Common objects in context, in: European conference on computer vision, Springer, 2014, pp. 740-755.
Y. Li, H. Huang, Q. Xie, L. Yao, Q. Chen, Research on a surface defect detection algorithm based on mobilenet-ssd, Applied Sciences 8 (9) (2018) 1678.
M. Danelljan, G. Hager, F. Khan, M. Felsberg, Accurate scale estimation for robust visual tracking, in: British Machine Vision Conference, Nottingham, September 1-5, 2014, Bmva Press, 2014.
X. Liu, N. Krahnstoever, T. Yu, P. Tu, What are customers looking at?, in: 2007 IEEE Conference on Advanced Video and Signal Based Surveillance, IEEE, 2007, pp. 405-410.
M. J. Reale, S. Canavan, L. Yin, K. Hu, T. Hung, A multi-gesture interaction system using a 3-d iris disk model for gaze estimation and an active appearance model for 3-d hand pointing, IEEE Transactions on Multimedia 13 (3) (2011) 474-486.
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