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.
|Title of host publication||Computer Vision – ECCV 2020 Workshops, Proceedings|
|Editors||Adrien Bartoli, Andrea Fusiello|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||17|
|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
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||Workshops held at the 16th European Conference on Computer Vision, ECCV 2020|
|Period||23/08/20 → 28/08/20|
Bibliographical noteFunding Information:
This research was supported by grant no 1455/16 from the Israeli Science Foundation.
© 2020, Springer Nature Switzerland AG.
- Deep learning
- Fake vs Real
- Generative adversarial networks
- Image forensics
- Image source identification
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science (all)