Learning to detect fake face images in the wild

Chih Chung Hsu, Chia Yen Lee, Yi Xiu Zhuang

研究成果: Conference contribution

69 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題Proceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018
發行者Institute of Electrical and Electronics Engineers Inc.
頁面388-391
頁數4
ISBN(電子)9781538670361
DOIs
出版狀態Published - 2018 7月 2
事件4th International Symposium on Computer, Consumer and Control, IS3C 2018 - Taichung, Taiwan
持續時間: 2018 12月 62018 12月 8

出版系列

名字Proceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018

Conference

Conference4th International Symposium on Computer, Consumer and Control, IS3C 2018
國家/地區Taiwan
城市Taichung
期間18-12-0618-12-08

All Science Journal Classification (ASJC) codes

  • 電腦網路與通信
  • 控制與系統工程
  • 能源工程與電力技術
  • 電腦科學應用
  • 控制和優化
  • 訊號處理

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