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

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Abstract

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.

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Notes

  1. “Proposed method” in this subsection refers to the new combination PCT-LS with the proposed pipeline.

  2. “Proposed method” in this subsection refers to the new combination PCT-LS with the proposed pipeline.

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Acknowledgements

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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  1. School of Electrical and Electronic Engineering, Universiti Sains Malaysia (USM), 14300, Penang, Malaysia

    Osamah M. Al-Qershi & Bee Ee Khoo

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  1. Osamah M. Al-Qershi
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  2. Bee Ee Khoo
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Correspondence to Bee Ee Khoo.

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Al-Qershi, O.M., Khoo, B.E. Enhanced block-based copy-move forgery detection using k-means clustering. Multidim Syst Sign Process 30, 1671–1695 (2019). https://doi.org/10.1007/s11045-018-0624-y

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