What question did this study set out to answer?

The aim is to evaluate the effectiveness of deep learning technologies for academic integrity in examinations.

April 11, 2026

Research and Analysis of Exam Cheating Detection Based on Deep Learning

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This article reviews deep learning methods for detecting cheating in exams, suggesting implications for education technology.

The aim is to evaluate the effectiveness of deep learning technologies for academic integrity in examinations.
Reviewed existing research on exam cheating detection methods using deep learning techniques.
Examined various object detection models, particularly the YOLO series.
Discussed behavior recognition technology and generative adversarial networks for data augmentation.
Analyzed technical challenges such as small sample sizes and occlusion in behavior recognition.
Identified the potential of multimodal fusion and lightweight deployment in enhancing detection efforts.
Highlighted the importance of balancing model performance with real-time accuracy and ethical considerations.
Outlined existing limitations such as handling complex behaviors and ensuring privacy protection.

Quanyao Liu (2026) studied this question.

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