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Enhanced Matching Method for Copy-Move Forgery Detection by Means of Zernike Moments

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Digital-Forensics and Watermarking (IWDW 2014)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 9023))

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Abstract

Copy-move is one of the most popular and efficient operations to create image forgery. Many passive detection techniques have been proposed to detect such a forgery in digital images. The performance of the detection algorithms depends mainly on the features used for matching image blocks or keypoints and the matching method as well. Among the existing detection algorithms, those which employ Zernike moments as features provide remarkable detection accuracy. The robustness of Zernike moments comes from the fact that they are invariant to rotation and scaling. However, Zernike moments-based algorithms can be improved further by adopting more efficient matching methods. In this paper, we propose a new matching method in order to enhance the detection accuracy. Compared to the lexicographical sorting-based matching method, the proposed method improved the detection accuracy by 40 %.

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Acknowledgment

The authors would like to acknowledge the financial assistance provided by Universiti Sains Malaysia via TPLN USM. They would also like to thank S.J. Ryu et al. [17] for sharing their code.

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Authors and Affiliations

  1. Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia

    Osamah M. Al-Qershi & Bee Ee Khoo

Authors
  1. Osamah M. Al-Qershi
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  2. Bee Ee Khoo
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Corresponding author

Correspondence to Bee Ee Khoo .

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Editors and Affiliations

  1. NJIT, Newark, New Jersey, USA

    Yun-Qing Shi

  2. Korea University, Seoul, Korea, Republic of (South Korea)

    Hyoung Joong Kim

  3. Universidad de La Laguna, La Laguna, Spain

    Fernando Pérez-González

  4. Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan

    Ching-Nung Yang

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Al-Qershi, O.M., Khoo, B.E. (2015). Enhanced Matching Method for Copy-Move Forgery Detection by Means of Zernike Moments. In: Shi, YQ., Kim, H., Pérez-González, F., Yang, CN. (eds) Digital-Forensics and Watermarking. IWDW 2014. Lecture Notes in Computer Science(), vol 9023. Springer, Cham. https://doi.org/10.1007/978-3-319-19321-2_37

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  • DOI: https://doi.org/10.1007/978-3-319-19321-2_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19320-5

  • Online ISBN: 978-3-319-19321-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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