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Multimedia Data Mining and Analytics pp 327–343Cite as

Exposing Image Tampering with the Same Quantization Matrix

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Abstract

Image tampering, being readily facilitated and proliferated by today’s digital techniques, is increasingly causing problems regarding the authenticity of images. As the most popular multimedia data, JPEG images can be easily tampered without leaving any clues; therefore, JPEG-based forensics , including the detection of double compression, interpolation, rotation, etc., has become an active research topic in multimedia forensics. Nevertheless, the interesting issue of detecting image tampering and its related operations by using the same quantization matrix has not been fully investigated. Aiming to detect such forgery manipulations under the same quantization matrix, we propose a detection method by using shift-recompression -based reshuffle characteristic features. Learning classifiers are applied to evaluating the efficacy. Our experimental results indicate that the approach is indeed highly effective in detecting image tampering and relevant manipulations with the same quantization matrix.

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Acknowledgments

The study was supported from the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice, under the Award No. 2010-DN-BX-K223 and from the Sam Houston State University under two enhanced research grants (ERG, 2011–2013).

Author information

Authors and Affiliations

  1. Department of Computer Science, Sam Houston State University, Huntsville, TX, 77341, USA

    Qingzhong Liu & Lei Chen

  2. School of Computing, The University of Southern Mississippi, Hattiesburg, MS, 39406, USA

    Andrew H. Sung

  3. Department of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, IN, 47405, USA

    Zhongxue Chen

Authors
  1. Qingzhong Liu
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  2. Andrew H. Sung
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  3. Zhongxue Chen
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  4. Lei Chen
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Corresponding author

Correspondence to Qingzhong Liu .

Editor information

Editors and Affiliations

  1. IBM Corp., Durham, North Carolina, USA

    Aaron K. Baughman

  2. Nokia Inc., Sunnyvale, California, USA

    Jiang Gao

  3. Google Inc., Mountain View, California, USA

    Jia-Yu Pan

  4. 4i, Inc., Carlsbad, California, USA

    Valery A. Petrushin

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Liu, Q., Sung, A.H., Chen, Z., Chen, L. (2015). Exposing Image Tampering with the Same Quantization Matrix. In: Baughman, A., Gao, J., Pan, JY., Petrushin, V. (eds) Multimedia Data Mining and Analytics. Springer, Cham. https://doi.org/10.1007/978-3-319-14998-1_15

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  • DOI: https://doi.org/10.1007/978-3-319-14998-1_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14997-4

  • Online ISBN: 978-3-319-14998-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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