Open Access

ARTICLE

Deep Learning Based Image Forgery Detection Methods

by Liang Xiu-jian^1,2,*, Sun He²

1 Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology
2 School of Computer Science, Nanjing University of Information Science & Technology, Nanjing, 210044, China

* Corresponding Author: Liang Xiu-jian. Email:

Journal of Cyber Security 2022, 4(2), 119-133. https://doi.org/10.32604/jcs.2022.032915

Received 01 May 2022; Accepted 01 June 2022; Issue published 04 July 2022

Abstract

Increasingly advanced image processing technology has made digital image editing easier and easier. With image processing software at one’s fingertips, one can easily alter the content of an image, and the altered image is so realistic that it is illegible to the naked eye. These tampered images have posed a serious threat to personal privacy, social order, and national security. Therefore, detecting and locating tampered areas in images has important practical significance, and has become an important research topic in the field of multimedia information security. In recent years, deep learning technology has been widely used in image tampering localization, and the achieved performance has significantly surpassed traditional tampering forensics methods. This paper mainly sorts out the relevant knowledge and latest methods in the field of image tampering detection based on deep learning. According to the two types of tampering detection based on deep learning, the detection tasks of the method are detailed separately, and the problems and future prospects in this field are discussed. It is quite different from the existing work: 1) This paper mainly focuses on the problem of image tampering detection, so it does not elaborate on various forensic methods. 2) This paper focuses on the detection method of image tampering based on deep learning. 3) This paper is driven by the needs of tampering targets, so it pays more attention to sorting out methods for different tampering detection tasks.

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Keywords

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