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Steganalysis of LSB Replacement Using Parity-Aware Features

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Information Hiding (IH 2012)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 7692))

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Abstract

Detection of LSB replacement in digital images has received quite a bit of attention in the past ten years. In particular, structural detectors together with variants of Weighted Stego-image (WS) analysis have materialized as the most accurate. In this paper, we show that further surprisingly significant improvement is possible with machine–learning based detectors utilizing co-occurrences of neighboring noise residuals as features. Such features can leverage dependencies among adjacent residual samples in contrast to the WS detector, which implicitly assumes that the residuals are mutually independent. Further improvement is achieved by adapting the features for detection of LSB replacement by making them aware of pixel parity. To this end, we introduce two key novel concepts – calibration by parity and parity-aware residuals. It is shown that, at least for a known cover source when a binary classifier can be built, its accuracy is markedly better in comparison with the best structural and WS detectors in both uncompressed images and in decompressed JPEGs. This improvement is especially significant for very small change rates. A simple feature selection algorithm is used to obtain interesting insight that reveals potentially novel directions in structural steganalysis.

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Author information

Authors and Affiliations

  1. Department of ECE, Binghamton University, NY, USA

    Jessica Fridrich & Jan Kodovský

Authors
  1. Jessica Fridrich
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  2. Jan Kodovský
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Editor information

Editors and Affiliations

  1. International Computer Science Institute, 1947 Center Street, Suite 600, 94704, Berkeley, CA, USA

    Matthias Kirchner

  2. Department of Computer Science, University of California Davis, 95616, Davis, CA, USA

    Dipak Ghosal

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Fridrich, J., Kodovský, J. (2013). Steganalysis of LSB Replacement Using Parity-Aware Features. In: Kirchner, M., Ghosal, D. (eds) Information Hiding. IH 2012. Lecture Notes in Computer Science, vol 7692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36373-3_3

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  • DOI: https://doi.org/10.1007/978-3-642-36373-3_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36372-6

  • Online ISBN: 978-3-642-36373-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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