Detecting Generated Image Based on a Coupled Network with Two-Step Pairwise Learning

Yi Xiu Zhuang, Chih Chung Hsu

研究成果: Conference contribution

3 引文 斯高帕斯(Scopus)

摘要

With the rapid growth of generative adversarial networks (GANs), a photo-realistic image can be easily generated from a low-dimensional random vector nowadays. However, the generated image can be used to synthesize several persons who may have a potential effect on society with radical contents. Considering that many techniques to produce a photo-realistic facial image based on different GANs are already available, collecting training images of all possible generative models is difficult; hence, the learning-based approach would not effectively detect a fake image generated using an excluded generative model. To overcome this shortcoming, we propose a two-step pairwise learning approach to learn common fake features over the training images generated by using different generative models. First, the triplet loss will be used to simulate the relation between fake and real images and utilized to learn the discriminative features to determine whether an image is real or fake. Then, we propose a novel coupled network to accurately capture local and global image features of the fake or real images. The experimental results demonstrate that the proposed method outperforms the baseline supervised learning methods for fake facial image detection.

原文English
主出版物標題2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
發行者IEEE Computer Society
頁面3212-3216
頁數5
ISBN(電子)9781538662496
DOIs
出版狀態Published - 2019 九月
事件26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan
持續時間: 2019 九月 222019 九月 25

出版系列

名字Proceedings - International Conference on Image Processing, ICIP
2019-September
ISSN(列印)1522-4880

Conference

Conference26th IEEE International Conference on Image Processing, ICIP 2019
國家/地區Taiwan
城市Taipei
期間19-09-2219-09-25

All Science Journal Classification (ASJC) codes

  • 軟體
  • 電腦視覺和模式識別
  • 訊號處理

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