Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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A Survey on Passive Image Copy-Move Forgery Detection

  • Zhang, Zhi (School of Mechanical, Electrical and Information Engineering, Shandong University) ;
  • Wang, Chengyou (School of Mechanical, Electrical and Information Engineering, Shandong University) ;
  • Zhou, Xiao (School of Mechanical, Electrical and Information Engineering, Shandong University)
  • Received : 2017.10.20
  • Accepted : 2017.12.26
  • Published : 2018.02.28
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Abstract

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.

Keywords

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Fig. 1. Actual event of Iranian missile: (a) genuine Iranian missile photo, (b) forged Iranian missilephoto published on BBC NEWS, and (c) forged Iranian missile photo with marked region-duplication.

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Fig. 2. Image forensics technologies classification.

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Fig. 3. Two models of copy-move forgery: (a) Luo’s model and (b) Liu’s model.

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Fig. 4. Two frameworks of CMFD methods: (a) framework of block-based CMFD and keypoint-basedCMFD and (b) framework of CMFD based on machine learning. Adapted from G. K. Birajdar and V. H.Mankar. Digital image forgery detection using passive techniques: a survey. Digital Investigation2013;10(3):226-245, with the permission of Elsevier [37].

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Fig. 5. A circular template in Cartesian space. Adapted from J. Zhong et al. A new block-based methodfor copy move forgery detection under image geometric transforms. Multimedia Tools and Applications2017;76(13):14887-14903, with the permission of Springer [68].

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Fig. 6. Example of the CoMoFoD dataset: (a) original image, (b) forged image, (c) forged image withimage blurring, and (d) ground truth map.

Table 1. Block-based CMFD methods comparison

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Table 1. (Continued)

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Table 1. (Continued)

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Table 2. Keypoint-based CMFD methods comparison

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Table 2. (Continued)

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Table 3. Dataset comparison

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