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Detection of Tampering Inconsistencies on Mobile Photos

  • Conference paper
Digital Watermarking (IWDW 2010)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 6526))

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Abstract

Fast proliferation of mobile cameras and the deteriorating trust on digital images have created needs in determining the integrity of photos captured by mobile devices. As tampering often creates some inconsistencies, we propose in this paper a novel framework to statistically detect the image tampering inconsistency using accurately detected demosaicing weights features. By first cropping four non-overlapping blocks, each from one of the four quadrants in the mobile photo, we extract a set of demosaicing weights features from each block based on a partial derivative correlation model. Through regularizing the eigenspectrum of the within-photo covariance matrix and performing eigenfeature transformation, we further derive a compact set of eigen demosaicing weights features, which are sensitive to image signal mixing from different photo sources. A metric is then proposed to quantify the inconsistency based on the eigen weights features among the blocks cropped from different regions of the mobile photo. Through comparison, we show our eigen weights features perform better than the eigen features extracted from several other conventional sets of statistical forensics features in detecting the presence of tampering. Experimentally, our method shows a good confidence in tampering detection especially when one of the four cropped blocks is from a different camera model or brand with different demosaicing process.

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Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Jurong West, Singapore

    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. Graduate School of Information Management and Security, CIST, Korea University, 136-701, Seoul, Korea

    Hyoung-Joong Kim

  2. New Jersey Institute of Technology, 07102, Newark, NJ, USA

    Yun Qing Shi

  3. Department of Information Engineering, University of Siena, Via Roma 56, 53100, Siena, Italy

    Mauro Barni

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Cao, H., Kot, A.C. (2011). Detection of Tampering Inconsistencies on Mobile Photos. In: Kim, HJ., Shi, Y.Q., Barni, M. (eds) Digital Watermarking. IWDW 2010. Lecture Notes in Computer Science, vol 6526. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18405-5_9

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  • DOI: https://doi.org/10.1007/978-3-642-18405-5_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-18404-8

  • Online ISBN: 978-3-642-18405-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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