Private-encoder: Enforcing privacy in latent space for human face images

Zhao, Y; Liu, B; Zhu, T; Ding, M; Zhou, W

Private-encoder: Enforcing privacy in latent space for human face images

Zhao, Y Liu, B

Zhu, T Ding, M Zhou, W

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Publisher:: WILEY
Publication Type:: Journal Article
Citation:: Concurrency and Computation: Practice and Experience, 2022, 34, (3)
Issue Date:: 2022-02-01

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Field	Value	Language
dc.contributor.author	Zhao, Y
dc.contributor.author	Liu, B https://orcid.org/0000-0002-3603-6617
dc.contributor.author	Zhu, T
dc.contributor.author	Ding, M
dc.contributor.author	Zhou, W https://orcid.org/0000-0002-1680-2521
dc.date.accessioned	2022-01-27T00:05:50Z
dc.date.available	2022-01-27T00:05:50Z
dc.date.issued	2022-02-01
dc.identifier.citation	Concurrency and Computation: Practice and Experience, 2022, 34, (3)
dc.identifier.issn	1532-0626
dc.identifier.issn	1532-0634
dc.identifier.uri	http://hdl.handle.net/10453/153597
dc.description.abstract	The explosive growth of various computer vision technologies generates a tremendous amount of visual data online every day. In addition to bringing convenience and revolutionizing our daily life, image data also reveal a wide range of sensitive information and pose unprecedented privacy leakage risks. Particularly, in the case of photos contain human faces, people can easily access those face images on social media without any consent, and the misuse of personal information could cause serious privacy violation to individuals. Therefore, it is essential to consider sanitizing people's identity information when using images containing human faces. As a result, there has been rapid development in the area of facial anonymization, also called image de-identification. However, due to the emergence of numerous deep-learning based attacks, traditional anonymization methods such as blurring and mosaic are weak and ineffective to protect individual's privacy in face images. To respond to this challenge, this article proposes a novel de-identification method that utilizes a deep neural network. The proposed framework encompasses two modules: encoder network and generator network. The encoder transforms a face image into a high-semantic latent vector of codes, which will be de-identified according to the differential privacy criterion. The generator leverages the unconditional generative adversarial network to synthesize high-quality images based on the modified latent codes from the encoder. Extensive experimental results indicate that our proposed model can protect image privacy while keeping the processed image visual realistic.
dc.language	English
dc.publisher	WILEY
dc.relation	http://purl.org/au-research/grants/arc/LP180101150
dc.relation.ispartof	Concurrency and Computation: Practice and Experience
dc.relation.isbasedon	10.1002/cpe.6548
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0803 Computer Software, 0805 Distributed Computing
dc.subject.classification	Distributed Computing
dc.title	Private-encoder: Enforcing privacy in latent space for human face images
dc.type	Journal Article
utslib.citation.volume	34
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
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	*
utslib.copyright.embargo	2022-08-09T00:00:00+1000Z
dc.date.updated	2022-01-27T00:05:43Z
pubs.issue	3
pubs.publication-status	Accepted
pubs.volume	34
utslib.citation.issue	3

Abstract:

The explosive growth of various computer vision technologies generates a tremendous amount of visual data online every day. In addition to bringing convenience and revolutionizing our daily life, image data also reveal a wide range of sensitive information and pose unprecedented privacy leakage risks. Particularly, in the case of photos contain human faces, people can easily access those face images on social media without any consent, and the misuse of personal information could cause serious privacy violation to individuals. Therefore, it is essential to consider sanitizing people's identity information when using images containing human faces. As a result, there has been rapid development in the area of facial anonymization, also called image de-identification. However, due to the emergence of numerous deep-learning based attacks, traditional anonymization methods such as blurring and mosaic are weak and ineffective to protect individual's privacy in face images. To respond to this challenge, this article proposes a novel de-identification method that utilizes a deep neural network. The proposed framework encompasses two modules: encoder network and generator network. The encoder transforms a face image into a high-semantic latent vector of codes, which will be de-identified according to the differential privacy criterion. The generator leverages the unconditional generative adversarial network to synthesize high-quality images based on the modified latent codes from the encoder. Extensive experimental results indicate that our proposed model can protect image privacy while keeping the processed image visual realistic.

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http://hdl.handle.net/10453/153597