Open Access

ARTICLE

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:

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

Received 21 October 2020; Accepted 28 October 2020; Issue published 11 November 2020

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.

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Keywords

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