Online proctoring has become a necessity in online teaching. Video-based
crowd-sourced online proctoring solutions are being used, where an exam-taking
student’s video is monitored by third parties, leading to privacy concerns. In
this paper, we propose a privacy-preserving online proctoring system. The
proposed image-hashing-based system can detect the student’s excessive face and
body movement (i.e., anomalies) that is resulted when the student tries to
cheat in the exam. The detection can be done even if the student’s face is
blurred or masked in video frames. Experiment with an in-house dataset shows
the usability of the proposed system.

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