A Two-Stage, Object-Centric Deep Learning Framework for Robust Exam Cheating Detection
2026-04-17 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial Intelligence
AI summaryⓘ
The authors tackled the problem of cheating during exams by improving AI-based monitoring systems. They combined two known AI models: YOLOv8n to find students in images and RexNet-150 to decide if a student is cheating or not. Using a large mixed dataset, their system became much more accurate than older methods and is fast enough to use in big exam settings. They also made sure results are shared privately with students to avoid embarrassment. The authors suggest adding audio or video over time could make it even better.
Academic integrityCheating detectionYOLOv8nRexNet-150Object detectionBehavioral analysisRecall and precisionInference timeEthical AIVideo-based monitoring
Authors
Van-Truong Le, Le-Khanh Nguyen, Trong-Doanh Nguyen
Abstract
Academic integrity continues to face the persistent challenge of examination cheating. Traditional invigilation relies on human observation, which is inefficient, costly, and prone to errors at scale. Although some existing AI-powered monitoring systems have been deployed and trusted, many lack transparency or require multi-layered architectures to achieve the desired performance. To overcome these challenges, we propose an improvement over a simple two-stage framework for exam cheating detection that integrates object detection and behavioral analysis using well-known technologies. First, the state-of-the-art YOLOv8n model is used to localize students in exam-room images. Each detected region is cropped and preprocessed, then classified by a fine-tuned RexNet-150 model as either normal or cheating behavior. The system is trained on a dataset compiled from 10 independent sources with a total of 273,897 samples, achieving 0.95 accuracy, 0.94 recall, 0.96 precision, and 0.95 F1-score - a 13\% increase over a baseline accuracy of 0.82 in video-based cheating detection. In addition, with an average inference time of 13.9 ms per sample, the proposed approach demonstrates robustness and scalability for deployment in large-scale environments. Beyond the technical contribution, the AI-assisted monitoring system also addresses ethical concerns by ensuring that final outcomes are delivered privately to individual students after the examination, for example, via personal email. This prevents public exposure or shaming and offers students an opportunity to reflect on their behavior. For further improvement, it is possible to incorporate additional factors, such as audio data and consecutive frames, to achieve greater accuracy. This study provides a foundation for developing real-time, scalable, ethical, and open-source solutions.