Face-Image Source Generator Identification

Mohammad Salama; Hagit Hel-Or

doi:10.1007/978-3-030-68238-5_37

Face-Image Source Generator Identification

Mohammad Salama, Hagit Hel-Or

Department of Computer Science

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

Abstract

Recent advances in deep networks and specifically, Generative Adversarial Networks, have introduced new ways of manipulating and synthesizing “fake” images. Concerns have been raised as to the sinister use of these images, and accordingly challenges have been raised to detect “fake” from “real” images. In this study we address a slightly different problem in image forensics. Rather than discriminating real from fake, we attempt to perform “Source Generator Identification”, i.e. determine the source generator of the synthesized image. In this study we focus on face images. We exploit the specific characteristics associated with each fake face image generator and introduce a face generator representation space (the profile space) which allows a study of the distribution of face generators, their distinctions as well as allows estimating probability of images arising from the same generator.

Original language	English
Title of host publication	Computer Vision – ECCV 2020 Workshops, Proceedings
Editors	Adrien Bartoli, Andrea Fusiello
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	511-527
Number of pages	17
ISBN (Print)	9783030682378
DOIs	https://doi.org/10.1007/978-3-030-68238-5_37
State	Published - 2020
Event	Workshops held at the 16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom Duration: 23 Aug 2020 → 28 Aug 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12539 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Workshops held at the 16th European Conference on Computer Vision, ECCV 2020
Country/Territory	United Kingdom
City	Glasgow
Period	23/08/20 → 28/08/20

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Auto-encoder
Deep learning
Fake vs Real
Generative adversarial networks
Image forensics
Image source identification

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-68238-5_37

Cite this

Salama, M., & Hel-Or, H. (2020). Face-Image Source Generator Identification. In A. Bartoli, & A. Fusiello (Eds.), Computer Vision – ECCV 2020 Workshops, Proceedings (pp. 511-527). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12539 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-68238-5_37

Salama, Mohammad ; Hel-Or, Hagit. / Face-Image Source Generator Identification. Computer Vision – ECCV 2020 Workshops, Proceedings. editor / Adrien Bartoli ; Andrea Fusiello. Springer Science and Business Media Deutschland GmbH, 2020. pp. 511-527 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Recent advances in deep networks and specifically, Generative Adversarial Networks, have introduced new ways of manipulating and synthesizing “fake” images. Concerns have been raised as to the sinister use of these images, and accordingly challenges have been raised to detect “fake” from “real” images. In this study we address a slightly different problem in image forensics. Rather than discriminating real from fake, we attempt to perform “Source Generator Identification”, i.e. determine the source generator of the synthesized image. In this study we focus on face images. We exploit the specific characteristics associated with each fake face image generator and introduce a face generator representation space (the profile space) which allows a study of the distribution of face generators, their distinctions as well as allows estimating probability of images arising from the same generator.",

keywords = "Auto-encoder, Deep learning, Fake vs Real, Generative adversarial networks, Image forensics, Image source identification",

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Salama, M & Hel-Or, H 2020, Face-Image Source Generator Identification. in A Bartoli & A Fusiello (eds), Computer Vision – ECCV 2020 Workshops, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12539 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 511-527, Workshops held at the 16th European Conference on Computer Vision, ECCV 2020, Glasgow, United Kingdom, 23/08/20. https://doi.org/10.1007/978-3-030-68238-5_37

Face-Image Source Generator Identification. / Salama, Mohammad; Hel-Or, Hagit.
Computer Vision – ECCV 2020 Workshops, Proceedings. ed. / Adrien Bartoli; Andrea Fusiello. Springer Science and Business Media Deutschland GmbH, 2020. p. 511-527 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12539 LNCS).

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

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T1 - Face-Image Source Generator Identification

AU - Salama, Mohammad

AU - Hel-Or, Hagit

PY - 2020

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N2 - Recent advances in deep networks and specifically, Generative Adversarial Networks, have introduced new ways of manipulating and synthesizing “fake” images. Concerns have been raised as to the sinister use of these images, and accordingly challenges have been raised to detect “fake” from “real” images. In this study we address a slightly different problem in image forensics. Rather than discriminating real from fake, we attempt to perform “Source Generator Identification”, i.e. determine the source generator of the synthesized image. In this study we focus on face images. We exploit the specific characteristics associated with each fake face image generator and introduce a face generator representation space (the profile space) which allows a study of the distribution of face generators, their distinctions as well as allows estimating probability of images arising from the same generator.

AB - Recent advances in deep networks and specifically, Generative Adversarial Networks, have introduced new ways of manipulating and synthesizing “fake” images. Concerns have been raised as to the sinister use of these images, and accordingly challenges have been raised to detect “fake” from “real” images. In this study we address a slightly different problem in image forensics. Rather than discriminating real from fake, we attempt to perform “Source Generator Identification”, i.e. determine the source generator of the synthesized image. In this study we focus on face images. We exploit the specific characteristics associated with each fake face image generator and introduce a face generator representation space (the profile space) which allows a study of the distribution of face generators, their distinctions as well as allows estimating probability of images arising from the same generator.

KW - Auto-encoder

KW - Deep learning

KW - Fake vs Real

KW - Generative adversarial networks

KW - Image forensics

KW - Image source identification

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DO - 10.1007/978-3-030-68238-5_37

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

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BT - Computer Vision – ECCV 2020 Workshops, Proceedings

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ER -

Salama M, Hel-Or H. Face-Image Source Generator Identification. In Bartoli A, Fusiello A, editors, Computer Vision – ECCV 2020 Workshops, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 511-527. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-68238-5_37

Face-Image Source Generator Identification

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this