Cheating Detection Pipeline for Online Interviews

Abstract

Remote examination and job interviews have gained popularity and become indispensable because of both pandemics and the advantage of remote working circumstances. Most companies and academic institutions utilize these systems for their recruitment processes and also for online exams. However, one of the critical problems of the remote examination systems is conducting the exams in a reliable environment. In this work, we present a cheating analysis pipeline for online interviews and exams. The system only requires a video of the candidate, which is recorded during the exam. Then cheating detection pipeline is employed to detect another person, electronic device usage, and candidate absence status. The pipeline consists of face detection, face recognition, object detection, and face tracking algorithms. To evaluate the performance of the pipeline we collected a private video dataset. The video dataset includes both cheating activities and clean videos. Ultimately, our pipeline presents an efficient and fast guideline to detect and analyze cheating activities in an online interview and exam video.

Multi-Perspective Adaptive Paperless Examination Cheating Detection System Based on Image Recognition

“…Cheating tools, such as books and mobile phones. Abozaid [28], Ahmad [29], Ashwinkumar [30], Ozgen [31], and Sapre [32] used Retinanet, YOLO, and Mobilenet-SSD methods to detect cheating tools or abnormal objects in the examination environment.…”

Cheating Detection for Online Examination Using Clustering Based Approach

“…In addition, Özgen et al [20] developed an online interview anti-cheating system. Their approach involved object and face detection using a HOG-based SVM detector.…”