A Convolutional Neural Network-Based Real-Time Behavioral Detection System for Preventing Cheating in Online Examinations
DOI:
https://doi.org/10.55123/jomlai.v5i1.7676Keywords:
Artefact, Artificial Intelligence, Artificial Neural Networks, Convolution Neural Networks, Deep Learning, Deep Neural Networks, Machine Learning, TensorFlowAbstract
The integrity of online examinations has become a growing concern in digital education, particularly following the rapid shift to remote learning. This study presents the development of a Convolutional Neural Network (CNN)-based Real-Time Behavioral Detection System and Prevention of cheating in online examinations. Specifically, the study identifies and classifies common visual behaviors associated with cheating, such as frequent eye movement, head turning, and the presence of unauthorized individuals. A CNN model was designed and trained on a curated dataset of annotated behavioral frames. The model achieved a classification accuracy of 91.7%, precision of 89.5%, recall of 92.3%, and an F1-score of 90.9%, demonstrating strong performance in real-time cheating behavior detection. A working prototype was developed using Python, TensorFlow, and OpenCV, and successfully integrated into a live monitoring interface capable of issuing alerts, logging incidents, and generating post-exam reports. The system's performance was evaluated across various test scenarios, showing consistent results with an average latency of 0.72 seconds per frame, making it suitable for real-time deployment.. Its implementation offers significant value to educational institutions, exam regulators, and EdTech platforms seeking to ensure fairness and trust in digital examinations.
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