Efficient Video Privacy Protection Against Malicious Face Recognition Models

Guo, E; Li, P; Yu, S; Wang, H

Efficient Video Privacy Protection Against Malicious Face Recognition Models

Guo, E Li, P Yu, S

Wang, H

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Open Journal of the Computer Society, 2022, 3, pp. 271-280
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Guo, E
dc.contributor.author	Li, P
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.contributor.author	Wang, H
dc.date.accessioned	2023-04-18T01:41:35Z
dc.date.available	2023-04-18T01:41:35Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Open Journal of the Computer Society, 2022, 3, pp. 271-280
dc.identifier.issn	2644-1268
dc.identifier.issn	2644-1268
dc.identifier.uri	http://hdl.handle.net/10453/169879
dc.description.abstract	The proliferation of powerful facial recognition systems poses a serious threat to user privacy. Attackers could train highly accurate facial recognition models using public data on social platforms. Therefore, recent works have proposed image pre-processing techniques to protect user privacy. Without affecting people's normal viewing, these techniques add special noises into images, so that it would be difficult for attackers to train models with high accuracy. However, existing protection techniques are mainly designed for image data protection, and they cannot be directly applied for video data because of high computational overhead. In this paper, we propose an efficient protection method for video privacy that exploits unique features of video protection to eliminate computation redundancy for computational acceleration. The evaluation results under various benchmarks demonstrate that our method significantly outperforms the traditional methods by reducing computation overhead by 35.5%.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Open Journal of the Computer Society
dc.relation.isbasedon	10.1109/OJCS.2022.3218559
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Efficient Video Privacy Protection Against Malicious Face Recognition Models
dc.type	Journal Article
utslib.citation.volume	3
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-04-18T01:41:34Z
pubs.publication-status	Published
pubs.volume	3

Abstract:

The proliferation of powerful facial recognition systems poses a serious threat to user privacy. Attackers could train highly accurate facial recognition models using public data on social platforms. Therefore, recent works have proposed image pre-processing techniques to protect user privacy. Without affecting people's normal viewing, these techniques add special noises into images, so that it would be difficult for attackers to train models with high accuracy. However, existing protection techniques are mainly designed for image data protection, and they cannot be directly applied for video data because of high computational overhead. In this paper, we propose an efficient protection method for video privacy that exploits unique features of video protection to eliminate computation redundancy for computational acceleration. The evaluation results under various benchmarks demonstrate that our method significantly outperforms the traditional methods by reducing computation overhead by 35.5%.

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http://hdl.handle.net/10453/169879