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k-Same-Net : k-Anonymity with generative deep neural networks for face deidentification
ID
Meden, Blaž
(
Author
),
ID
Emeršič, Žiga
(
Author
),
ID
Štruc, Vitomir
(
Author
),
ID
Peer, Peter
(
Author
)
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MD5: E6710497C8C730409F16BC14D0FDE9DC
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http://www.mdpi.com/1099-4300/20/1/60
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Abstract
Image and video data are today being shared between government entities and other relevant stakeholders on a regular basis and require careful handling of the personal information contained therein. A popular approach to ensure privacy protection in such data is the use of deidentification techniques, which aim at concealing the identity of individuals in the imagery while still preserving certain aspects of the data after deidentification. In this work, we propose a novel approach towards face deidentification, called k-Same-Net, which combines recent Generative Neural Networks (GNNs) with the well-known k-Anonymity mechanism and provides formal guarantees regarding privacy protection on a closed set of identities. Our GNN is able to generate synthetic surrogate face images for deidentification by seamlessly combining features of identities used to train the GNN model. Furthermore, it allows us to control the image-generation process with a small set of appearance-related parameters that can be used to alter specific aspects (e.g., facial expressions, age, gender) of the synthesized surrogate images. We demonstrate the feasibility of k-Same-Net in comprehensive experiments on the XM2VTS and CK+ datasets. We evaluate the efficacy of the proposed approach through reidentification experiments with recent recognition models and compare our results with competing deidentification techniques from the literature. We also present facial expression recognition experiments to demonstrate the utility-preservation capabilities of k-Same-Net. Our experimental results suggest that k-Same-Net is a viable option for facial deidentification that exhibits several desirable characteristics when compared to existing solutions in this area.
Language:
English
Keywords:
face deidentification
,
generative neural networks
,
k-Same algorithm
Work type:
Article
Typology:
1.01 - Original Scientific Article
Organization:
FRI - Faculty of Computer and Information Science
FE - Faculty of Electrical Engineering
Publication status:
Published
Publication version:
Version of Record
Year:
2018
Number of pages:
24 str.
Numbering:
Vol. 20, iss. 1, art. 60
PID:
20.500.12556/RUL-131874
UDC:
004.93\'1
ISSN on article:
1099-4300
DOI:
10.3390/e20010060
COBISS.SI-ID:
1537688771
Publication date in RUL:
05.10.2021
Views:
760
Downloads:
162
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Record is a part of a journal
Title:
Entropy
Shortened title:
Entropy
Publisher:
MDPI
ISSN:
1099-4300
COBISS.SI-ID:
515806233
Licences
License:
CC BY 4.0, Creative Commons Attribution 4.0 International
Link:
http://creativecommons.org/licenses/by/4.0/
Description:
This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:
13.01.2018
Secondary language
Language:
Slovenian
Keywords:
deidentifikacija obrazov
,
generativne nevronske mreže
,
algoritem k-Same
Projects
Funder:
ARRS - Slovenian Research Agency
Project number:
P2-0250
Name:
Metrologija in biometrični sistemi
Funder:
ARRS - Slovenian Research Agency
Project number:
P2-0214
Name:
Računalniški vid
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