  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.
