Algorithmic Learning for Steganography: Proper Learning of k-term DNF Formulas from Positive Samples

  • Matthias Ernst
  • Maciej Liśkiewicz
  • Rüdiger ReischukEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9472)


Proper learning from positive samples is a basic ingredient for designing secure steganographic systems for unknown covertext channels. In addition, security requirements imply that the hypothesis should not contain false positives. We present such a learner for k-term DNF formulas for the uniform distribution and a generalization to q-bounded distributions. We briefly also describe how these results can be used to design a secure stegosystem.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Matthias Ernst
    • 1
    • 2
  • Maciej Liśkiewicz
    • 1
  • Rüdiger Reischuk
    • 1
    Email author
  1. 1.Institut für Theoretische InformatikUniversität zu LübeckLübeckGermany
  2. 2.Graduate School for Computing in Medicine and Life SciencesUniversität zu LübeckLübeckGermany

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