COPYNet: Unveiling Suspicious Behaviour in Face-to-Face Exams

dc.contributor.authorSirt, Dogu
dc.contributor.authorSaykol, Ediz
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractThis research is dedicated to the analysis and detection of anomalies within images captured by digital cameras during face-to-face examinations. The focal point is the development of a novel model designed to identify exam activities with a high degree of precision. Central to this work is the creation of the COPYNet Dataset, a substantial collection of approximately 30,000 images. This dataset is pivotal for the development, verification, and performance evaluation of anomaly detection algorithms. It is meticulously segmented into five distinct groups, each corresponding to a particular behavioral category crucial for anomaly detection. To achieve superior performance in image classification, the transfer learning method is independently hybridized with the Faster R-CNN and YOLOv5 algorithms using the pretrained ResNet model. This leads to the creation of a deep neural network framework, COPYNet, designed to generate an anomaly score by modeling typical behavior. Significantly, the COPYNet framework demonstrates remarkable precision (0.90), recall (0.88), and accuracy (0.88), marking a considerable advancement in anomaly detection compared to existing literature. The results underscore the model's capability to accurately categorize diverse activity classes, making it a promising instrument for addressing the challenge of identifying suspicious behaviors during face-to-face exams. Consequently, when the model identifies an unusual activity, it triggers an alert to be dispatched to the proctor, serving as a decision support mechanism for exam invigilators. Given the obtained success rates, our study proposes a promising solution for detecting suspicious behavior during face-to-face exams, surpassing previous studies in the field.en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.publisherInt Information & Engineering Technology Assocen_US
dc.relation.ispartofTraitement Du Signalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.subjectabnormal behavior detectionen_US
dc.subjectexam copy detectionen_US
dc.subjectdeep learningen_US
dc.subjecttransfer learningen_US
dc.titleCOPYNet: Unveiling Suspicious Behaviour in Face-to-Face Examsen_US