Multidimensional Systems and Signal Processing

, Volume 27, Issue 4, pp 989–1005 | Cite as

Fusion of block and keypoints based approaches for effective copy-move image forgery detection

  • Jiangbin Zheng
  • Yanan Liu
  • Jinchang Ren
  • Tingge Zhu
  • Yijun Yan
  • Heng Yang


Keypoint-based and block-based methods are two main categories of techniques for detecting copy-move forged images, one of the most common digital image forgery schemes. In general, block-based methods suffer from high computational cost due to the large number of image blocks used and fail to handle geometric transformations. On the contrary, keypoint-based approaches can overcome these two drawbacks yet are found difficult to deal with smooth regions. As a result, fusion of these two approaches is proposed for effective copy-move forgery detection. First, our scheme adaptively determines an appropriate initial size of regions to segment the image into non-overlapped regions. Feature points are extracted as keypoints using the scale invariant feature transform (SIFT) from the image. The ratio between the number of keypoints and the total number of pixels in that region is used to classify the region into smooth or non-smooth (keypoints) regions. Accordingly, block based approach using Zernike moments and keypoint based approach using SIFT along with filtering and post-processing are respectively applied to these two kinds of regions for effective forgery detection. Experimental results show that the proposed fusion scheme outperforms the keypoint-based method in reliability of detection and the block-based method in efficiency.


Image forensics Copy-move image forgery detection  Adaptive fusion SIFT Zernike moments 



This study was supported by the Science and Technology Innovation Project of Shaanxi Province (Nos. 2015KTTSGY04-05 and 2015KTZDGY04-01), Pre-research Project: target detection project. The authors also wish to greatly thank the editors and anonymous reviewers for their constructive comments to further improve the clarity and quality of this paper. We also thank Li et al. (2015) for providing their source codes to enable us for more accurate performance assessment in term of not only precision, recall and F1 but also the running time.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Jiangbin Zheng
    • 1
  • Yanan Liu
    • 1
  • Jinchang Ren
    • 2
  • Tingge Zhu
    • 1
  • Yijun Yan
    • 2
  • Heng Yang
    • 3
  1. 1.Department of Computer Science and Engineering, School of ComputersNorthwestern Polytechnical UniversityXi’anChina
  2. 2.Department of Electronic and Electrical EngineeringUniversity of StrathclydeGlasgowUK
  3. 3.Xi’an Communications InstituteXi’anChina

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