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Detection of Copy-Move Forgery in Flat Region Based on Feature Enhancement

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Digital Forensics and Watermarking (IWDW 2016)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10082))

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Abstract

A new Feature Enhancement method based on SURF is proposed for Copy-Move Forgery Detection. The main difference from the traditional methods is that Contrast Limited Adaptive Histogram Equalization is proposed as a preprocessing stage in images. SURF is used to extract keypoints from the preprocessed image. Even in flat regions, the method can also extract enough keypoints. In the matching stage, g2NN matching skill is used which can also detect multiple forgeries. The experimental results show that the proposed method performs better than the state-of-the-art algorithms on the public database.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 61370195, U1536121).

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Authors and Affiliations

  1. School of Science, China Agricultural University, Beijing, 100083, China

    Weiwei Zhang, Zhenghong Yang & Junbin Wang

  2. Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Shaozhang Niu

Authors
  1. Weiwei Zhang
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  2. Zhenghong Yang
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  3. Shaozhang Niu
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  4. Junbin Wang
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Corresponding author

Correspondence to Shaozhang Niu .

Editor information

Editors and Affiliations

  1. New Jersey Institute of Technology, Newark, New Jersey, USA

    Yun Qing Shi

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

    Hyoung Joong Kim

  3. University of Vigo, Vigo, Spain

    Fernando Perez-Gonzalez

  4. Chinese Academy of Sciences, Beijing, China

    Feng Liu

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Zhang, W., Yang, Z., Niu, S., Wang, J. (2017). Detection of Copy-Move Forgery in Flat Region Based on Feature Enhancement. In: Shi, Y., Kim, H., Perez-Gonzalez, F., Liu, F. (eds) Digital Forensics and Watermarking. IWDW 2016. Lecture Notes in Computer Science(), vol 10082. Springer, Cham. https://doi.org/10.1007/978-3-319-53465-7_12

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  • DOI: https://doi.org/10.1007/978-3-319-53465-7_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53464-0

  • Online ISBN: 978-3-319-53465-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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