@Article{jihpp.2020.010464,
AUTHOR = {Bingtao Hu, Jinwei Wang},
TITLE = {Deep Learning for Distinguishing Computer Generated Images and Natural  Images: A Survey},
JOURNAL = {Journal of Information Hiding and Privacy Protection},
VOLUME = {2},
YEAR = {2020},
NUMBER = {2},
PAGES = {95--105},
URL = {http://www.techscience.com/jihpp/v2n2/40539},
ISSN = {2637-4226},
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.},
DOI = {10.32604/jihpp.2020.010464}
}