한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제22권12호
  • /
  • Pages.1447-1456
  • /
  • 2019
  • /
  • 1229-7771(pISSN)
  • /
  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
권호 표지
DOI QR코드

DOI QR Code

합성곱신경망 기반의 StyleGAN 이미지 탐지모델

A StyleGAN Image Detection Model Based on Convolutional Neural Network

  • Kim, Jiyeon (Center for Software Educational Innovation, Seoul Women's University) ;
  • Hong, Seung-Ah (Dept. of Information Security, Seoul Women's University) ;
  • Kim, Hamin (Dept. of Information Security, Seoul Women's University)
  • 투고 : 2019.11.28
  • 심사 : 2019.12.16
  • 발행 : 2019.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

As artificial intelligence technology is actively used in image processing, it is possible to generate high-quality fake images based on deep learning. Fake images generated using GAN(Generative Adversarial Network), one of unsupervised learning algorithms, have reached levels that are hard to discriminate from the naked eye. Detecting these fake images is required as they can be abused for crimes such as illegal content production, identity fraud and defamation. In this paper, we develop a deep-learning model based on CNN(Convolutional Neural Network) for the detection of StyleGAN fake images. StyleGAN is one of GAN algorithms and has an excellent performance in generating face images. We experiment with 48 number of experimental scenarios developed by combining parameters of the proposed model. We train and test each scenario with 300,000 number of real and fake face images in order to present a model parameter that improves performance in the detection of fake faces.

키워드

참고문헌

  1. A. Radford, L. Metz, and C. Soumith, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks," Computer Science, Machine Learning, arXiv Preprint arXiv:1511.06434, 2015.
  2. D. Berthelot, T. Schumm, and L. Metz, "Began: Boundary Equilibrium Generative Adversarial Networks," Computer Science, arXiv Preprint arXiv:1703.10717, 2017.
  3. I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, et al., "Generative Adversarial Nets," Advances in Neural Information Processing Systems, pp. 2672-2680, 2014.
  4. T. Karras, S. Laine, and T. Aila, "A Stylebased Generator Architecture for Generative Adversarial Networks," Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4401-4410, 2019.
  5. T. Karras, T. Aila, S. Laine, and J. Lehtinen, ARRAS, "Progressive Growing of Gans for Improved Quality, Stability, and Variation," Computer Science, Neural and Evolutionary Computing, arXiv Preprint arXiv:1710.10196, 2017.
  6. D.H. Kim, C.S. Wan, and K.S. Yeong, “Deep Learning Based Fake Face Detection,” Journal of the Korea Industrial Information Systems Research, Vol. 23, No. 5, pp. 9-17, 2018. https://doi.org/10.9723/jksiis.2018.23.5.009
  7. C.C. Hsu, Y.X. Zhuang, and C.Y. LEE, "Deep Fake Image Detection Based on Pairwise Learning," Preprints 2019, 201905.0013.v1, 2019.
  8. L.M. Dang, S.I. Hassan, S.Y. Im, J.C. Lee, S.J. Lee, and H.J. Moon, et al., "Deep Learning Based Computer Generated Face Identification Using Convolutional Neural Network," Applied Sciences, Vol. 8, No. 2610, doi:10.3390/app8122610, 2018.
  9. C.C. Hsu, C.Y. LEE, and Y.X. Zhuang, "Learning to Detect Fake Face Images in the Wild," Proceeding of IEEE International Symposium on Computer, Consumer and Control Conference, pp. 388-391, 2018.
  10. H. Li, B. Li, S. Tan, and J. Huang, "Detection of Deep Network Generated Images Using Disparities in Color Components," Computer Science, arXiv Preprint arXiv:1808.07276, 2018.
  11. T.D. Nhu, I.S. Na, and S.H. KIM, "Forensics Face Detection From GANs Using Convolutional Neural Network," Proceeding of 2018 International Symposium on Information Technology Convergence(ISITC 2018), 2018.
  12. S.K. Yarlagadda, D. Guera, P. Bestagini, F.M. Zhu, S. Tubaro, and E.J. Delp, "Satellite Image Forgery Detection and Localization Using GAN and One-class Classifier," Electronic Imaging, Vol. 7, pp. 1-9, 2018.
  13. J.Y. Zhu, T. Park, P. Isola, and A.A. Efros, "Unpaired Image-to-image Translation Using Cycle-consistent Adversarial Networks," Proceeding of the IEEE International Conference on Computer Vision, pp. 2223-2232, 2017.
  14. Y.J. Choi, M.J. Choi, M.Y. Kim, J.W. Ha, S.H. Kim, and J.G. Choo, "Stargan: Unified Generative Adversarial Networks for Multi-domain Image-to-image Translation," Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8789-8797, 2018.
  15. L. Nataraj, T.M. Mohammed, S. Chandrasekaran, A. Flenner, J.H. Bappy, A.K. Roy- Chowdhury, et al., "Detecting GAN Generated Fake Images Using Co-occurrence Matrices," Computer Science, Computer Vision and Pattern Recognition, arXiv Preprint arXiv: 1903.06836, 2019.
  16. X. Xuan, B. Peng, W. Wang, and J. Dong, "On the Generalization of GAN Image Forensics," Proceeding of Chinese Conference on Biometric Recognition, Springer, Cham, pp. 134-141, 2019.
  17. S. Tariq, S.Y. Lee, H.Y. Kim, Y.J. Shin, and S.S. Woo, "GAN Is a Friend or Foe?: a Framework to Detect Various Fake Face Images," Proceeding of the 34th ACM/SIGAPP Symposium on Applied Computing, pp. 1296-1303, 2019.
  18. Yang Yang, Lei Zheng, Jiawei Zhang, Qingcai Cui, Zhoujun Li and Philip S. Yu, "TI-CNN: Convolutional Neural Networks for Fake News Detection," arXiv Preprint arXiv:1806.00749, 2018.
  19. X. Zhang, S. Karaman, and S.F. Chang, "Detecting and Simulating Artifacts in GAN Fake Images," Computer Science, Computer Vision and Pattern Recognition, arXiv Preprint arXiv:1907.06515, 2019.
  20. S.Y. Rhyou, H.J. Kim, and K.A. Cha, "Development of Access Management System Based on Face Recognition Using ResNet," Journal of Korea Multimedia Society, Vol. 22, Issue 8, pp. 823-831, 2019. https://doi.org/10.9717/KMMS.2019.22.8.823

피인용 문헌

  1. cGAN을 이용한 OCT 이미지의 층 분할 vol.23, pp.12, 2019, https://doi.org/10.9717/kmms.2020.23.12.1476