Face-Image Source Generator Identification

Mohammad Salama, Hagit Hel-Or

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 languageEnglish
Title of host publicationComputer Vision – ECCV 2020 Workshops, Proceedings
EditorsAdrien Bartoli, Andrea Fusiello
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
ISBN (Print)9783030682378
StatePublished - 2020
EventWorkshops held at the 16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom
Duration: 23 Aug 202028 Aug 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12539 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceWorkshops held at the 16th European Conference on Computer Vision, ECCV 2020
Country/TerritoryUnited Kingdom

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.


  • 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


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