A Survey on Passive Image Copy-Move Forgery Detection

Zhi Zhang, Chengyou Wang, Xiao Zhou, Journal of Information Processing Systems Vol. 14, No. 1, pp. 6-31, Feb. 2018  

Keywords: Copy-Move Forgery Detection (CMFD), Image Forensics, Image Tamper Detection, Passive Forgery Detection


With the rapid development of the science and technology, it has been becoming more and more convenient to obtain abundant information via the diverse multimedia medium. However, the contents of the multimedia are easily altered with different editing software, and the authenticity and the integrity of multimedia content are under threat. Forensics technology is developed to solve this problem. We focus on reviewing the blind image forensics technologies for copy-move forgery in this survey. Copy-move forgery is one of the most common manners to manipulate images that usually obscure the objects by flat regions or append the objects within the same image. In this paper, two classical models of copy-move forgery are reviewed, and two frameworks of copy-move forgery detection (CMFD) methods are summarized. Then, massive CMFD methods are mainly divided into two types to retrospect the development process of CMFD technologies, including block-based and keypoint-based. Besides, the performance evaluation criterions and the datasets created for evaluating the performance of CMFD methods are also collected in this review. At last, future research directions and conclusions are given to provide beneficial advice for researchers in this field.

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Cite this article
[APA Style]
Zhang, Z., Wang, C., & Zhou, X. (2018). A Survey on Passive Image Copy-Move Forgery Detection. Journal of Information Processing Systems, 14(1), 6-31. DOI: 10.3745/JIPS.02.0078.

[IEEE Style]
Z. Zhang, C. Wang, X. Zhou, "A Survey on Passive Image Copy-Move Forgery Detection," Journal of Information Processing Systems, vol. 14, no. 1, pp. 6-31, 2018. DOI: 10.3745/JIPS.02.0078.

[ACM Style]
Zhi Zhang, Chengyou Wang, and Xiao Zhou. 2018. A Survey on Passive Image Copy-Move Forgery Detection. Journal of Information Processing Systems, 14, 1, (2018), 6-31. DOI: 10.3745/JIPS.02.0078.