Table of Content

Open Access iconOpen Access

ARTICLE

crossmark

Deep Learning for Distinguishing Computer Generated Images and Natural Images: A Survey

Bingtao Hu*, Jinwei Wang

Nanjing University of Information Science and Technology, Nanjing, 210044, China

* Corresponding Author: Bingtao Hu. Email: email

Journal of Information Hiding and Privacy Protection 2020, 2(2), 95-105. https://doi.org/10.32604/jihpp.2020.010464

Abstract

With the development of computer graphics, realistic computer graphics (CG) have become more and more common in our field of vision. This rendered image is invisible to the naked eye. How to effectively identify CG and natural images (NI) has been become a new issue in the field of digital forensics. In recent years, a series of deep learning network frameworks have shown great advantages in the field of images, which provides a good choice for us to solve this problem. This paper aims to track the latest developments and applications of deep learning in the field of CG and NI forensics in a timely manner. Firstly, it introduces the background of deep learning and the knowledge of convolutional neural networks. The purpose is to understand the basic model structure of deep learning applications in the image field, and then outlines the mainstream framework; secondly, it briefly introduces the application of deep learning in CG and NI forensics, and finally points out the problems of deep learning in this field and the prospects for the future.

Keywords


Cite This Article

APA Style
Hu, B., Wang, J. (2020). Deep learning for distinguishing computer generated images and natural images: A survey. Journal of Information Hiding and Privacy Protection, 2(2), 95-105. https://doi.org/10.32604/jihpp.2020.010464
Vancouver Style
Hu B, Wang J. Deep learning for distinguishing computer generated images and natural images: A survey. J Inf Hiding Privacy Protection . 2020;2(2):95-105 https://doi.org/10.32604/jihpp.2020.010464
IEEE Style
B. Hu and J. Wang, "Deep Learning for Distinguishing Computer Generated Images and Natural Images: A Survey," J. Inf. Hiding Privacy Protection , vol. 2, no. 2, pp. 95-105. 2020. https://doi.org/10.32604/jihpp.2020.010464



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1966

    View

  • 1199

    Download

  • 0

    Like

Share Link