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A Survey on Digital Image Copy-Move Forgery Localization Using Passive Techniques

Weijin Tan1,*, Yunqing Wu1, Peng Wu1, Beijing Chen1,2

School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing, 210044, China.

*Corresponding Author: Weijin Tan. Email: .

Journal of New Media 2019, 1(1), 11-25.


Digital images can be tampered easily with simple image editing software tools. Therefore, image forensic investigation on the authenticity of digital images’ content is increasingly important. Copy-move is one of the most common types of image forgeries. Thus, an overview of the traditional and the recent copy-move forgery localization methods using passive techniques is presented in this paper. These methods are classified into three types: block-based methods, keypoint-based methods, and deep learning-based methods. In addition, the strengths and weaknesses of these methods are compared and analyzed in robustness and computational cost. Finally, further research directions are discussed.


Cite This Article

W. Tan, Y. Wu, P. Wu and B. Chen, "A survey on digital image copy-move forgery localization using passive techniques," Journal of New Media, vol. 1, no.1, pp. 11–25, 2019.


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.
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