Learning to detect fake face images in the wild

Chih Chung Hsu, Chia Yen Lee, Yi Xiu Zhuang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

67 Citations (Scopus)

Abstract

Although Generative Adversarial Network (GAN) can be used to generate the realistic image, improper use of these technologies brings hidden concerns. For example, GAN can be used to generate a tampered video for specific people and inappropriate events, creating images that are detrimental to a particular person, and may even affect that personal safety. In this paper, we will develop a deep forgery discriminator (DeepFD) to efficiently and effectively detect the computer-generated images. Directly learning a binary classifier is relatively tricky since it is hard to find the common discriminative features for judging the fake images generated from different GANs. To address this shortcoming, we adopt contrastive loss in seeking the typical features of the synthesized images generated by different GANs and follow by concatenating a classifier to detect such computer-generated images. Experimental results demonstrate that the proposed DeepFD successfully detected 94.7% fake images generated by several state-of-the-art GANs.

Original languageEnglish
Title of host publicationProceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages388-391
Number of pages4
ISBN (Electronic)9781538670361
DOIs
Publication statusPublished - 2018 Jul 2
Event4th International Symposium on Computer, Consumer and Control, IS3C 2018 - Taichung, Taiwan
Duration: 2018 Dec 62018 Dec 8

Publication series

NameProceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018

Conference

Conference4th International Symposium on Computer, Consumer and Control, IS3C 2018
Country/TerritoryTaiwan
CityTaichung
Period18-12-0618-12-08

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Control and Systems Engineering
  • Energy Engineering and Power Technology
  • Computer Science Applications
  • Control and Optimization
  • Signal Processing

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