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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: email.

Journal of New Media 2019, 1(1), 11-25. https://doi.org/10.32604/jnm.2019.06219

Abstract

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.

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APA Style
Tan, W., Wu, Y., Wu, P., Chen, B. (2019). A survey on digital image copy-move forgery localization using passive techniques. Journal of New Media, 1(1), 11-25. https://doi.org/10.32604/jnm.2019.06219
Vancouver Style
Tan W, Wu Y, Wu P, Chen B. A survey on digital image copy-move forgery localization using passive techniques. J New Media . 2019;1(1):11-25 https://doi.org/10.32604/jnm.2019.06219
IEEE Style
W. Tan, Y. Wu, P. Wu, and B. Chen, “A Survey on Digital Image Copy-Move Forgery Localization Using Passive Techniques,” J. New Media , vol. 1, no. 1, pp. 11-25, 2019. https://doi.org/10.32604/jnm.2019.06219

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cc Copyright © 2019 The Author(s). Published by Tech Science Press.
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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