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A Local Derivative Pattern Based Image Forensic Framework for Seam Carving Detection

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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

Seam carving is one of the most popular image scaling algorithms which can effectively manipulate the image size while preserving the important image content. In this paper, we present a local derivative pattern (LDP) based forensic framework to detect if a digital image has been processed by seam carving or not. Each image is firstly encoded by applying four LDP encoders. Afterward, 96-D features are extracted from the encoded LDP images, and the support vector machine (SVM) classifier with linear kernel is utilized. The experimental results thus obtained have demonstrated that the proposed framework outperforms the state of the art. Specifically, the proposed scheme has achieved 73%, 88% and 97% average detection accuracies in detecting the low carving rate cases, i.e., 5%, 10% and 20%, respectively; while the prior state-of-the-arts has achieved 66%, 75% and 87% average detection accuracy on these cases.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, New Jersey, 07102, USA

    Jingyu Ye & Yun-Qing Shi

Authors
  1. Jingyu Ye
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  2. Yun-Qing Shi
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Corresponding author

Correspondence to Jingyu Ye .

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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Ye, J., Shi, YQ. (2017). A Local Derivative Pattern Based Image Forensic Framework for Seam Carving Detection. 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_13

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  • DOI: https://doi.org/10.1007/978-3-319-53465-7_13

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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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