Cheating Detection Pipeline for Online Interviews and Exams
This work addresses cheating detection for companies and academic institutions using online systems, but it is incremental as it combines existing algorithms like face detection and object detection.
The authors tackled the problem of ensuring reliability in remote exams and interviews by developing a cheating detection pipeline that analyzes candidate videos to detect unauthorized persons, electronic device usage, and candidate absence, achieving efficient and fast performance as validated on a private dataset.
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.