An Anti-Cheating System for Online Interviews and Exams


  • Azmi Can Ozgen Huawei Turkey R&D Center, Istanbul, Turkey
  • Mahiye Uluyağmur Öztürk Huawei Turkey R&D Center, Istanbul, Turkey
  • Umut Bayraktar Huawei Turkey R&D Center, Istanbul, Turkey
  • Selim Aksoy Department of Computer Engineering, Bilkent University, Ankara 06800, Turkey


Cheating detection, Face detection, Object detection, Face tracking, Video processing


Remote examination and job interviews have gained popularity and become indispensable because of both pandemics and the advantage of remote working circumstances. Most businesses and educational organizations use these platforms for recruitment as well as 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 by using a webcam without a need for any extra tool. Then cheating detection pipeline is employed to detect the presence of 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 for detecting and analyzing cheating actions in an online interview and exam video.


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How to Cite

Ozgen, A. C., Mahiye Uluyağmur Öztürk, Bayraktar, U., & Selim Aksoy. (2021). An Anti-Cheating System for Online Interviews and Exams. American Scientific Research Journal for Engineering, Technology, and Sciences, 83(1), 96–112. Retrieved from