Transactions on Data Hiding and Multimedia Security X

Volume 8948 of the series Lecture Notes in Computer Science pp 69-91


Adaptive Steganography and Steganalysis with Fixed-Size Embedding

  • Benjamin JohnsonAffiliated withCylab, Carnegie Mellon UniversitySchool of Information, University of California, Berkeley
  • , Pascal SchöttleAffiliated withDepartment of Information Systems, University of Münster
  • , Aron LaszkaAffiliated withInstitute for Software Integrated Systems, Vanderbilt University Email author 
  • , Jens GrossklagsAffiliated withCollege of Information Sciences and Technology, Pennsylvania State University
  • , Rainer BöhmeAffiliated withDepartment of Information Systems, University of Münster

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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.


Game theory Content-adaptive steganography Security