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Measuring the Statistical Correlation Inconsistencies in Mobile Images for Tamper Detection

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Book cover Transactions on Data Hiding and Multimedia Security VII

Part of the book series: Lecture Notes in Computer Science ((TDHMS,volume 7110))

Abstract

In this paper, we propose a novel framework to statistically measure the correlation inconsistency in mobile images for tamper detection. By first sampling a number of blocks at different image locations, we extract a set of derivative weights as features from each block using partial derivative correlation models. Through regularizing the within-image covariance eigenspectrum and performing eigenfeature transformation, we derive a compact set of eigen weights, which are sensitive to image signal mixing from different source models. A metric is then proposed to quantify the inconsistency among the sampled blocks at different image locations. Through comparison, our eigen weights features show better performance than the eigenfeatures from several other types of forensics features in detecting the presence of tampering. Experimentally, our method shows good tamper detection performance especially when a small percentage of sampled blocks are from a different camera model or brand with different demosaicing processing.

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Author information

Authors and Affiliations

  1. Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore, 138632

    Hong Cao

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798

    Hong Cao & Alex C. Kot

Authors
  1. Hong Cao
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  2. Alex C. Kot
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Editor information

Editors and Affiliations

  1. New Jersey Institute of Technology, University Heights, 07102-1982, Newark, NJ, USA

    Yun Q. Shi

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Cao, H., Kot, A.C. (2012). Measuring the Statistical Correlation Inconsistencies in Mobile Images for Tamper Detection. In: Shi, Y.Q. (eds) Transactions on Data Hiding and Multimedia Security VII. Lecture Notes in Computer Science, vol 7110. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28693-3_5

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  • DOI: https://doi.org/10.1007/978-3-642-28693-3_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28692-6

  • Online ISBN: 978-3-642-28693-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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