Forbes: Face Obfuscation Rendering via Backpropagation Refinement Scheme

Jintae Kim; Seungwon Yang; Seong Gyun Jeong; Chang Su Kim

doi:10.1007/978-3-031-72890-7_4

Forbes: Face Obfuscation Rendering via Backpropagation Refinement Scheme

Jintae Kim, Seungwon Yang, Seong Gyun Jeong, Chang Su Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A novel algorithm for face obfuscation, called Forbes, which aims to obfuscate facial appearance recognizable by humans but preserve the identity and attributes decipherable by machines, is proposed in this paper. Forbes first applies multiple obfuscating transformations with random parameters to an image to remove the identity information distinguishable by humans. Then, it optimizes the parameters to make the transformed image decipherable by machines based on the backpropagation refinement scheme. Finally, it renders an obfuscated image by applying the transformations with the optimized parameters. Experimental results on various datasets demonstrate that Forbes achieves both human indecipherability and machine decipherability excellently. The source codes are available at https://github.com/mcljtkim/Forbes.

Original language	English
Title of host publication	Computer Vision – ECCV 2024 - 18th European Conference, Proceedings
Editors	Aleš Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, Gül Varol
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	54-70
Number of pages	17
ISBN (Print)	9783031728891
DOIs	https://doi.org/10.1007/978-3-031-72890-7_4
Publication status	Published - 2025
Event	18th European Conference on Computer Vision, ECCV 2024 - Milan, Italy Duration: 2024 Sept 29 → 2024 Oct 4

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	15127 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th European Conference on Computer Vision, ECCV 2024
Country/Territory	Italy
City	Milan
Period	24/9/29 → 24/10/4

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Keywords

Face obfuscation
Privacy-enhancing technologies

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-72890-7_4

Cite this

Kim, J., Yang, S., Jeong, S. G., & Kim, C. S. (2025). Forbes: Face Obfuscation Rendering via Backpropagation Refinement Scheme. In A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler, & G. Varol (Eds.), Computer Vision – ECCV 2024 - 18th European Conference, Proceedings (pp. 54-70). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15127 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-72890-7_4

Forbes: Face Obfuscation Rendering via Backpropagation Refinement Scheme. / Kim, Jintae; Yang, Seungwon; Jeong, Seong Gyun et al.
Computer Vision – ECCV 2024 - 18th European Conference, Proceedings. ed. / Aleš Leonardis; Elisa Ricci; Stefan Roth; Olga Russakovsky; Torsten Sattler; Gül Varol. Springer Science and Business Media Deutschland GmbH, 2025. p. 54-70 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15127 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, J, Yang, S, Jeong, SG & Kim, CS 2025, Forbes: Face Obfuscation Rendering via Backpropagation Refinement Scheme. in A Leonardis, E Ricci, S Roth, O Russakovsky, T Sattler & G Varol (eds), Computer Vision – ECCV 2024 - 18th European Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 15127 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 54-70, 18th European Conference on Computer Vision, ECCV 2024, Milan, Italy, 24/9/29. https://doi.org/10.1007/978-3-031-72890-7_4

Kim J, Yang S, Jeong SG, Kim CS. Forbes: Face Obfuscation Rendering via Backpropagation Refinement Scheme. In Leonardis A, Ricci E, Roth S, Russakovsky O, Sattler T, Varol G, editors, Computer Vision – ECCV 2024 - 18th European Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2025. p. 54-70. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-72890-7_4

Kim, Jintae ; Yang, Seungwon ; Jeong, Seong Gyun et al. / Forbes : Face Obfuscation Rendering via Backpropagation Refinement Scheme. Computer Vision – ECCV 2024 - 18th European Conference, Proceedings. editor / Aleš Leonardis ; Elisa Ricci ; Stefan Roth ; Olga Russakovsky ; Torsten Sattler ; Gül Varol. Springer Science and Business Media Deutschland GmbH, 2025. pp. 54-70 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{dcc85399e11440069123719d6a1fde62,

title = "Forbes: Face Obfuscation Rendering via Backpropagation Refinement Scheme",

abstract = "A novel algorithm for face obfuscation, called Forbes, which aims to obfuscate facial appearance recognizable by humans but preserve the identity and attributes decipherable by machines, is proposed in this paper. Forbes first applies multiple obfuscating transformations with random parameters to an image to remove the identity information distinguishable by humans. Then, it optimizes the parameters to make the transformed image decipherable by machines based on the backpropagation refinement scheme. Finally, it renders an obfuscated image by applying the transformations with the optimized parameters. Experimental results on various datasets demonstrate that Forbes achieves both human indecipherability and machine decipherability excellently. The source codes are available at https://github.com/mcljtkim/Forbes.",

keywords = "Face obfuscation, Privacy-enhancing technologies",

author = "Jintae Kim and Seungwon Yang and Jeong, {Seong Gyun} and Kim, {Chang Su}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 18th European Conference on Computer Vision, ECCV 2024 ; Conference date: 29-09-2024 Through 04-10-2024",

year = "2025",

doi = "10.1007/978-3-031-72890-7_4",

language = "English",

isbn = "9783031728891",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "54--70",

editor = "Ale{\v s} Leonardis and Elisa Ricci and Stefan Roth and Olga Russakovsky and Torsten Sattler and G{\"u}l Varol",

booktitle = "Computer Vision – ECCV 2024 - 18th European Conference, Proceedings",

address = "Germany",

}

TY - GEN

T1 - Forbes

T2 - 18th European Conference on Computer Vision, ECCV 2024

AU - Kim, Jintae

AU - Yang, Seungwon

AU - Jeong, Seong Gyun

AU - Kim, Chang Su

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025

Y1 - 2025

N2 - A novel algorithm for face obfuscation, called Forbes, which aims to obfuscate facial appearance recognizable by humans but preserve the identity and attributes decipherable by machines, is proposed in this paper. Forbes first applies multiple obfuscating transformations with random parameters to an image to remove the identity information distinguishable by humans. Then, it optimizes the parameters to make the transformed image decipherable by machines based on the backpropagation refinement scheme. Finally, it renders an obfuscated image by applying the transformations with the optimized parameters. Experimental results on various datasets demonstrate that Forbes achieves both human indecipherability and machine decipherability excellently. The source codes are available at https://github.com/mcljtkim/Forbes.

AB - A novel algorithm for face obfuscation, called Forbes, which aims to obfuscate facial appearance recognizable by humans but preserve the identity and attributes decipherable by machines, is proposed in this paper. Forbes first applies multiple obfuscating transformations with random parameters to an image to remove the identity information distinguishable by humans. Then, it optimizes the parameters to make the transformed image decipherable by machines based on the backpropagation refinement scheme. Finally, it renders an obfuscated image by applying the transformations with the optimized parameters. Experimental results on various datasets demonstrate that Forbes achieves both human indecipherability and machine decipherability excellently. The source codes are available at https://github.com/mcljtkim/Forbes.

KW - Face obfuscation

KW - Privacy-enhancing technologies

UR - http://www.scopus.com/inward/record.url?scp=85212981493&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-72890-7_4

DO - 10.1007/978-3-031-72890-7_4

M3 - Conference contribution

AN - SCOPUS:85212981493

SN - 9783031728891

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 54

EP - 70

BT - Computer Vision – ECCV 2024 - 18th European Conference, Proceedings

A2 - Leonardis, Aleš

A2 - Ricci, Elisa

A2 - Roth, Stefan

A2 - Russakovsky, Olga

A2 - Sattler, Torsten

A2 - Varol, Gül

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 29 September 2024 through 4 October 2024

ER -