A Hybrid Model for Natural Face De-Identiation with Adjustable Privacy

Wen, Y; Liu, B; Xie, R; Zhu, Y; Cao, J; Song, L

A Hybrid Model for Natural Face De-Identiation with Adjustable Privacy

Wen, Y Liu, B

Xie, R Zhu, Y Cao, J Song, L

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020, 2020, 00, pp. 269-272
Issue Date:: 2020-12-01

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Field	Value	Language
dc.contributor.author	Wen, Y
dc.contributor.author	Liu, B https://orcid.org/0000-0002-3603-6617
dc.contributor.author	Xie, R
dc.contributor.author	Zhu, Y
dc.contributor.author	Cao, J
dc.contributor.author	Song, L
dc.date	2020-12-01
dc.date.accessioned	2021-02-09T01:08:09Z
dc.date.available	2021-02-09T01:08:09Z
dc.date.issued	2020-12-01
dc.identifier.citation	2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020, 2020, 00, pp. 269-272
dc.identifier.isbn	9781728180670
dc.identifier.uri	http://hdl.handle.net/10453/145968
dc.description.abstract	© 2020 IEEE. As more and more personal photos are shared and tagged in social media, security and privacy protection are becoming an unprecedentedly focus of attention. Avoiding privacy risks such as unintended verification, becomes increasingly challenging. To enable people to enjoy uploading photos without having to consider these privacy concerns, it is crucial to study techniques that allow individuals to limit the identity information leaked in visual data. In this paper, we propose a novel hybrid model consists of two stages to generate visually pleasing de-identified face images according to a single input. Meanwhile, we successfully preserve visual similarity with the original face to retain data usability. Our approach combines latest advances in GAN-based face generation with well-designed adjustable randomness. In our experiments we show visually pleasing de-identified output of our method while preserving a high similarity to the original image content. Moreover, our method adapts well to the verificator of unknown structure, which further improves the practical value in our real life.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020
dc.relation.ispartof	2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)
dc.relation.isbasedon	10.1109/VCIP49819.2020.9301866
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	A Hybrid Model for Natural Face De-Identiation with Adjustable Privacy
dc.type	Conference Proceeding
utslib.citation.volume	00
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
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-12-29T00:00:00+1000Z
dc.date.updated	2021-02-09T01:08:05Z
pubs.finish-date	2020-12-04
pubs.publication-status	Published
pubs.start-date	2020-12-01
pubs.volume	00

Abstract:

© 2020 IEEE. As more and more personal photos are shared and tagged in social media, security and privacy protection are becoming an unprecedentedly focus of attention. Avoiding privacy risks such as unintended verification, becomes increasingly challenging. To enable people to enjoy uploading photos without having to consider these privacy concerns, it is crucial to study techniques that allow individuals to limit the identity information leaked in visual data. In this paper, we propose a novel hybrid model consists of two stages to generate visually pleasing de-identified face images according to a single input. Meanwhile, we successfully preserve visual similarity with the original face to retain data usability. Our approach combines latest advances in GAN-based face generation with well-designed adjustable randomness. In our experiments we show visually pleasing de-identified output of our method while preserving a high similarity to the original image content. Moreover, our method adapts well to the verificator of unknown structure, which further improves the practical value in our real life.

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http://hdl.handle.net/10453/145968