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Towards Multi-class Blind Steganalyzer for JPEG Images

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Digital Watermarking (IWDW 2005)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 3710))

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Abstract

In this paper, we use the previously proposed calibrated DCT features [9] to construct a Support Vector Machine classifier for JPEG images capable of recognizing which steganographic algorithm was used for embedding. This work also constitutes a more detailed evaluation of the performance of DCT features as in [9] only a linear classifier was used. The DCT features transformed using Principal Component Analysis enable an interesting visualization of different stego programs in a three-dimensional space. This paper demonstrates that, at least under some simplifying assumptions in which the effects of double compression are ignored, it is possible to reliably classify stego images to their embedding techniques. The classifier is capable of generalizing to previously unseen techniques.

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Author information

Authors and Affiliations

  1. Department of Computer Science,  

    Tomáš Pevný

  2. Department of Electrical and Computer Engineering, SUNY Binghamton, Binghamton, NY, 13902–6000

    Jessica Fridrich

Authors
  1. Tomáš Pevný
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  2. Jessica Fridrich
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Editor information

Editors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Siena, Italy

    Mauro Barni

  2. University College London, Torrington Place, UK

    Ingemar Cox

  3. Hewlett-Packard Laboratories, Palo Alto, CA, USA

    Ton Kalker

  4. Multimedia Security Lab, Korea University, Science Campus, 136-701, Seoul, Korea

    Hyoung-Joong Kim

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© 2005 Springer-Verlag Berlin Heidelberg

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Pevný, T., Fridrich, J. (2005). Towards Multi-class Blind Steganalyzer for JPEG Images. In: Barni, M., Cox, I., Kalker, T., Kim, HJ. (eds) Digital Watermarking. IWDW 2005. Lecture Notes in Computer Science, vol 3710. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551492_4

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  • DOI: https://doi.org/10.1007/11551492_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28768-1

  • Online ISBN: 978-3-540-32052-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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