Deep Learning Models for Detecting Cheating in Online Exams

The rapid shift to online education has introduced significant challenges to maintaining academic integrity in remote assessments, as traditional proctoring methods fall short in preventing cheating. The increase in cheating during online exams highlights the need for efficient, adaptable detection models to uphold academic credibility. This paper presents a comprehensive analysis of various deep learning models for cheating detection in online proctoring systems, evaluating their accuracy, efficiency, and adaptability. We benchmark several advanced architectures, including EfficientNet, MobileNetV2, ResNet variants and more, using two specialized datasets (OEP and OP) tailored for online proctoring contexts. Our findings reveal that EfficientNetB1 and YOLOv5 achieve top performance on the OP dataset, with EfficientNetB1 attaining a peak accuracy of 94.59% and YOLOv5 reaching a mean average precision (mAP@0.5) of 98.3%. For the OEP dataset, ResNet50-CBAM, YOLOv5 and EfficientNetB0 stand out, with ResNet50-CBAM achieving an accuracy of 93.61% and EfficientNetB0 showing robust detection performance with balanced accuracy and computational efficiency. These results underscore the importance of selecting models that balance accuracy and efficiency, supporting scalable, effective cheating detection in online assessments.