Privacy-Preserving Online Proctoring using Image-Hashing Anomaly Detection

Yaqub, W; Mohanty, M; Suleiman, B

Privacy-Preserving Online Proctoring using Image-Hashing Anomaly Detection

Yaqub, W Mohanty, M

Suleiman, B

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2022 International Wireless Communications and Mobile Computing (IWCMC), 2022, 00, pp. 1113-1118
Issue Date:: 2022

Closed Access

	Filename	Description	Size
	Privacy-Preserving_Online_Proctoring_using_Image-Hashing_Anomaly_Detection.pdf	Published version	3.3 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Yaqub, W
dc.contributor.author	Mohanty, M https://orcid.org/0000-0002-0258-4586
dc.contributor.author	Suleiman, B
dc.date	2022-05-30
dc.date.accessioned	2023-06-05T06:55:53Z
dc.date.available	2023-06-05T06:55:53Z
dc.date.issued	2022
dc.identifier.citation	2022 International Wireless Communications and Mobile Computing (IWCMC), 2022, 00, pp. 1113-1118
dc.identifier.isbn	978-1-6654-6749-0
dc.identifier.issn	2376-6492
dc.identifier.issn	2376-6506
dc.identifier.uri	http://hdl.handle.net/10453/170653
dc.description.abstract	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 moni-tored 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.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2022 International Wireless Communications and Mobile Computing (IWCMC)
dc.relation.ispartof	International Wireless Communications and Mobile Computing
dc.relation.isbasedon	10.1109/iwcmc55113.2022.9825119
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Privacy-Preserving Online Proctoring using Image-Hashing Anomaly Detection
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Dubrovnik, Croatia
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - CFS - Centre for Forensic Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-06-05T06:55:51Z
pubs.finish-date	2022-06-03
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2022-05-30
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

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 moni-tored 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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/170653