Cross-Dataset Face Manipulation Detection

Burak Bekci, Zahid Akhtar, Hazim Kemal Ekenel

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

6 Citations (Scopus)

Abstract

Easily available recent face image/video manipulation techniques and tools are now being utilized to generate highly realistic manipulated videos known as DeepFakes, which can fool face recognition systems and humans. Thus, it is vital to devise precise manipulation detection methods. Despite the progress, existing mechanisms are limited to the datasets or manipulation types. In this paper, to increase the performance under unseen data and manipulations, a DeepFakes detection framework using metric learning and steganalysis rich models is presented. Extensive empirical analysis on three publicly available datasets, namely, FaceForensics++, CelebDF, and DeepFakeTIMIT, were carried out to evaluate the generalization capability of the proposed approach. The framework attained 5% to 15% accuracy gains under unseen manipulations.

Original languageEnglish
Title of host publication2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728172064
DOIs
Publication statusPublished - 5 Oct 2020
Event28th Signal Processing and Communications Applications Conference, SIU 2020 - Gaziantep, Turkey
Duration: 5 Oct 20207 Oct 2020

Publication series

Name2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings

Conference

Conference28th Signal Processing and Communications Applications Conference, SIU 2020
Country/TerritoryTurkey
CityGaziantep
Period5/10/207/10/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Deep Learning
  • DeepFake
  • Face Manipulation
  • Generalization
  • Metric Learning

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