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Rotation Invariant Local Binary Pattern for Blind Detection of Copy-Move Forgery with Affine Transform

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Cloud Computing and Security (ICCCS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10040))

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Abstract

For copy-move forgery, the copied region may be rotated or flipped to fit the scene better. A blind image forensics approach is proposed for copy-move forgery detection using rotation invariant uniform local binary patterns (\(LBP_{P, R}^{riu2}\)). The image is first filtered and divided into overlapped blocks with fixed size. The features are extracted from each block using \(LBP_{P, R}^{riu2}\). Then, the feature vectors are sorted and block pairs are identified by estimating the Euclidean distances of these feature vectors. Specifically, a shift-vector counter C is exploited to detect and locate tampering region. Experimental results show that the proposed approach can deal with multiple copy-move forgeries, and is robust to JPEG compression, noise, blurring region rotation and flipping.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (61379143, 61232016, U1405254), the Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP) under grant 20120161110014 and the PAPD fund.

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Authors and Affiliations

  1. School of Information Science and Engineering, Hunan University, Changsha, 410082, China

    Pei Yang, Gaobo Yang & Dengyong Zhang

Authors
  1. Pei Yang
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  2. Gaobo Yang
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  3. Dengyong Zhang
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Corresponding author

Correspondence to Gaobo Yang .

Editor information

Editors and Affiliations

  1. University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Michigan State University , EAST LANSING, Michigan, USA

    Alex Liu

  3. National Dong Hwa University , Shoufeng, Taiwan

    Han-Chieh Chao

  4. Purdue University , West Lafayette, Indiana, USA

    Elisa Bertino

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© 2016 Springer International Publishing AG

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Yang, P., Yang, G., Zhang, D. (2016). Rotation Invariant Local Binary Pattern for Blind Detection of Copy-Move Forgery with Affine Transform. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10040. Springer, Cham. https://doi.org/10.1007/978-3-319-48674-1_36

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  • DOI: https://doi.org/10.1007/978-3-319-48674-1_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48673-4

  • Online ISBN: 978-3-319-48674-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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