Image-Hashing-Based Anomaly Detection for Privacy-Preserving Online Proctoring
This addresses privacy issues for students in online education by offering a less invasive monitoring method, though it appears incremental as it builds on existing anomaly detection techniques.
The paper tackles privacy concerns in online proctoring by proposing an image-hashing-based system to detect excessive face and body movements indicative of cheating, even with blurred or masked faces, and demonstrates its usability on an in-house dataset.
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.