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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65700
Volume:
10 |
6 |
Jun 2026
Pages:
1-3
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Computer Science in AI and ML
G H Raisoni College Of Engineering And Management Pune

Computer Science in AI and ML
G H Raisoni College Of Engineering And Management Pune
Rapid adaptation to the current trends in the domain of online education implies conducting examinations remotely, but this transition brings new threats associated with cheating issues. Traditional monitoring techniques prove to be rather ineffective in terms of detecting cheating activities due to various reasons. This work describes an intelligent proctoring system using audiovisual methods of candidate surveillance. The described system utilizes several factors including the presence of a face, gaze tracking, object recognition, and acoustic monitoring to determine abnormal behavior. [10] In contrast to the solution based on one signal, the suggested technique utilizes combined criteria to enhance the performance of the system and avoid false alarms. The presented approach allows working in real-time mode and is scalable enough to implement it widely.
KEYWORDS
It is clear from the above analysis that the combination of different behavioral parameters makes online exam surveil-lance systems more effective because they minimize reliance on any one parameter. Further developments can concentrate on improving the efficiency and reliability of such systems.
[1] S. K. Singh and R. Kumar, “AI-Based Online Exam Proctoring Systems: A Review,” IEEE Access, vol. 11, pp. 45210–45225, 2023. [2] M. A. Rahman, T. Hossain, and S. Islam, “Multimodal Behavior Analysis for Smart Proctoring Systems,” IEEE Access, vol. 12, pp. 10234–10248, 2024. [3] J. Redmon and A. Farhadi, “YOLOv5: Real-Time Object Detection System,” [4] A. Gupta and P. Sharma, ”Gaze Tracking Using Deep Learning Techniques for Attention Detection,” IEEE Transactions on Artificial Intelligence, vol. 5, no. 2, pp. 210-220. [5] P. Viola and M. Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features,” in Proceedings of CVPR, 2001. [6] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” in International Conference on Learning Representations (ICLR), 2015. [7] A. Krizhevsky, I. Sutskever, and G. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems (NeurIPS), 2012. [8] T. Baltrusaitis, P. Robinson, and L. Morency, “OpenFace: An Open Source Facial Behavior Analysis Toolkit,” in IEEE Winter Conference on Applications of Computer Vision (WACV), 2016. [9] F. Timm and E. Barth, “Accurate Eye Centre Localisation by Means of Gradients,” in VISAPP, 2011. [10] D. E. King, “Dlib-ML: A Machine Learning Toolkit,” Journal of Machine Learning Research, vol. 10, pp. 1755–1758, 2009.
Publication Date
6/30/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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