Adaptive Steganography and Steganalysis with Fixed-Size Embedding

  • Benjamin Johnson
  • Pascal Schöttle
  • Aron Laszka
  • Jens Grossklags
  • Rainer Böhme
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8948)

Abstract

We analyze a two-player zero-sum game between a steganographer, Alice, and a steganalyst, Eve. In this game, Alice wants to hide a secret message of length \(k\) in a binary sequence, and Eve wants to detect whether a secret message is present. The individual positions of all binary sequences are independently distributed, but have different levels of predictability. Using knowledge of this distribution, Alice randomizes over all possible size-\(k\) subsets of embedding positions. Eve uses an optimal (possibly randomized) decision rule that considers all positions, and incorporates knowledge of both the sequence distribution and Alice’s embedding strategy.

Our model extends prior work by removing restrictions on Eve’s detection power. We give defining formulas for each player’s best response strategy and minimax strategy; and we present additional structural constraints on the game’s equilibria. For the special case of length-two binary sequences, we compute explicit equilibria and provide numerical illustrations.

Keywords

Game theory Content-adaptive steganography Security 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Benjamin Johnson
    • 1
    • 5
  • Pascal Schöttle
    • 2
  • Aron Laszka
    • 3
  • Jens Grossklags
    • 4
  • Rainer Böhme
    • 2
  1. 1.CylabCarnegie Mellon UniversityPittsburghUSA
  2. 2.Department of Information SystemsUniversity of MünsterMünsterGermany
  3. 3.Institute for Software Integrated SystemsVanderbilt UniversityNashvilleUSA
  4. 4.College of Information Sciences and TechnologyPennsylvania State UniversityUniversity ParkUSA
  5. 5.School of InformationUniversity of California, BerkeleyBerkeleyUSA

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