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Face deidentification with controllable privacy protection
ID
Meden, Blaž
(
Avtor
),
ID
Gonzalez-Hernandez, Manfred
(
Avtor
),
ID
Peer, Peter
(
Avtor
),
ID
Štruc, Vitomir
(
Avtor
)
PDF - Predstavitvena datoteka,
prenos
(8,09 MB)
MD5: FAA077957C12C8A49967A90E0A6A31D6
URL - Izvorni URL, za dostop obiščite
https://www.sciencedirect.com/science/article/pii/S0262885623000525
Galerija slik
Izvleček
Privacy protection has become a crucial concern in today’s digital age. Particularly sensitive here are facial images, which typically not only reveal a person’s identity, but also other sensitive personal information. To address this problem, various face deidentification techniques have been presented in the literature. These techniques try to remove or obscure personal information from facial images while still preserving their usefulness for further analysis. While a considerable amount of work has been proposed on face deidentification, most state-of-the-art solutions still suffer from various drawbacks, and (a) deidentify only a narrow facial area, leaving potentially important contextual information unprotected, (b) modify facial images to such degrees, that image naturalness and facial diversity is suffering in the deidentify images, (c) offer no flexibility in the level of privacy protection ensured, leading to suboptimal deployment in various applications, and (d) often offer an unsatisfactory trade-off between the ability to obscure identity information, quality and naturalness of the deidentified images, and sufficient utility preservation. In this paper, we address these shortcomings with a novel controllable face deidentification technique that balances image quality, identity protection, and data utility for further analysis. The proposed approach utilizes a powerful generative model (StyleGAN2), multiple auxiliary classification models, and carefully designed constraints to guide the deidentification process. The approach is validated across four diverse datasets (CelebA-HQ, RaFD, XM2VTS, AffectNet) and in comparison to 7 state-of-the-art competitors. The results of the experiments demonstrate that the proposed solution leads to: (a) a considerable level of identity protection, (b) valuable preservation of data utility, (c) sufficient diversity among the deidentified faces, and (d) encouraging overall performance.
Jezik:
Angleški jezik
Ključne besede:
face deidentification
,
privacy protection
,
data utility
,
privacy-enhancing technologies
,
face biometrics
,
deep learning
Vrsta gradiva:
Članek v reviji
Tipologija:
1.01 - Izvirni znanstveni članek
Organizacija:
FRI - Fakulteta za računalništvo in informatiko
FE - Fakulteta za elektrotehniko
Status publikacije:
Objavljeno
Različica publikacije:
Objavljena publikacija
Leto izida:
2023
Št. strani:
19 str.
Številčenje:
Vol. 134, art. 104678
PID:
20.500.12556/RUL-146103
UDK:
004.93:57.087.1
ISSN pri članku:
0262-8856
DOI:
10.1016/j.imavis.2023.104678
COBISS.SI-ID:
150487811
Datum objave v RUL:
19.05.2023
Število ogledov:
506
Število prenosov:
103
Metapodatki:
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Objavi na:
Gradivo je del revije
Naslov:
Image and vision computing
Skrajšan naslov:
Image vis. comput.
Založnik:
Elsevier
ISSN:
0262-8856
COBISS.SI-ID:
25590016
Licence
Licenca:
CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:
http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:
To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Sekundarni jezik
Jezik:
Slovenski jezik
Ključne besede:
deidentifikacija obraza
,
varovanje zasebnosti
,
podatkovna uporabnost
,
tehnologije za izboljšanje zasebnosti
,
biometrija obrazov
,
globoko učenje
Projekti
Financer:
ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:
P2-0250
Naslov:
Metrologija in biometrični sistemi
Financer:
ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:
P2-0214
Naslov:
Računalniški vid
Financer:
ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:
J2-1734
Naslov:
Deidentifikacija obrazov z globokimi generativnimi modeli (FaceGEN)
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