Enhanced block-based copy-move forgery detection using k-means clustering

  • Osamah M. Al-Qershi
  • Bee Ee KhooEmail author


The goal of copy-move forgery is to manipulate the semantics of an image. In fact, this can be performed by cloning a region of an image and subsequently pasting it onto a different region within the same image. As such, this paper proposes an improved matching technique based on enhanced CMFD pipeline via k-means clustering technique. By deploying the k-means clustering to group the overlapping blocks, the matching step was independently carried out within each cluster to speed up the matching process. In addition, the clustering step of the feature vectors allowed the matching process to identify the matches accurately. Thus, in order to test the enhanced pipeline, it was combined with Zernike moments and locality sensitive hashing (LSH). The experimental results proved that the proposed method can enhance the detection accuracy in a significant manner. On top of that, the proposed pipeline can reduce the processing time with LSH-based matching.


Image forgery detection Copy-move forgery Clustering 



The authors would like to acknowledge the financial assistance provided by the Malaysian Ministry of Education through FRGS Grant No. 203/PELECT/6071305.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electrical and Electronic EngineeringUniversiti Sains Malaysia (USM)PenangMalaysia

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