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Steganography Based on Pattern Languages

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9618)


In order to transmit secret messages such that the information exchange itself cannot be detected, steganography needs a channel, a set of strings with some distribution that occur in an ordinary communication. The elements of such a language or concept are called coverdocuments. The question how to design secure stegosystems for natural classes of languages is investigated for pattern languages. We present a randomized modification scheme for strings of a pattern language that can reliably encode arbitrary messages and is almost undetectable.


  • Language-based cryptography
  • Steganography
  • Pattern

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  1. Angluin, D.: Finding patterns common to a set of strings. JCSS 21(1), 46–62 (1980)

    MathSciNet  MATH  Google Scholar 

  2. Bellare, M., Desai, A., Jokipii, E., Rogaway, P.: A concrete security treatment of symmetric encryption. In: Proceedings of 38th Annual Symposium on Foundations of Computer Science, FOCS, pp. 394–403. IEEE (1997)

    Google Scholar 

  3. Case, J., Jain, S., Le, T.D., Ong, Y.S., Semukhin, P., Stephan, F.: Automatic learning of subclasses of pattern languages. Inf. Comput. 218, 17–35 (2012)

    CrossRef  MathSciNet  MATH  Google Scholar 

  4. Case, J., Jain, S., Reischuk, R., Stephan, F., Zeugmann, T.: Learning a subclass of regular patterns in polynomial time. TCS 364(1), 115–131 (2006)

    CrossRef  MathSciNet  MATH  Google Scholar 

  5. Dedić, N., Itkis, G., Reyzin, L., Russell, S.: Upper and lower bounds on black-box steganography. J. Cryptol. 22(3), 365–394 (2009)

    CrossRef  MATH  Google Scholar 

  6. Ernst, M., Liśkiewicz, M., Reischuk, R.: Algorithmic learning for steganography: proper learning of k-term DNF formulas from positive samples. In: Elbassioni, K., Makino, K. (eds.) ISAAC 2015. LNCS, vol. 9472, pp. 151–162. Springer, Heidelberg (2015)

    CrossRef  Google Scholar 

  7. Hopper, N., von Ahn, L., Langford, J.: Provably secure steganography. IEEE Trans. Comput. 58(5), 662–676 (2009)

    CrossRef  MathSciNet  Google Scholar 

  8. Jerrum, M., Valiant, L.G., Vazirani, V.V.: Random generation of combinatorial structures from a uniform distribution. TCS 43, 169–188 (1986)

    CrossRef  MathSciNet  MATH  Google Scholar 

  9. Katzenbeisser, S., Petitcolas, F.A.: Defining security in steganographic systems. In: Electronic Imaging 2002, SPIE, pp. 50–56 (2002)

    Google Scholar 

  10. Ker, A.D., Bas, P., Böhme, R., Cogranne, R., Craver, S., Filler, T., Fridrich, J., Pevnỳ, T.: Moving steganography and steganalysis from the laboratory into the real world. In: Proceedings 1st ACM WS on Information Hiding and Multimedia Security, pp. 45–58. ACM (2013)

    Google Scholar 

  11. Ker, A.D., Pevný, T., Kodovský, J., Fridrich, J.J.: The square root law of steganographic capacity. In: Proceedings of 10th WS Multimedia & Security, pp. 107–116 (2008)

    Google Scholar 

  12. Lange, S., Wiehagen, R.: Polynomial-time inference of arbitrary pattern languages. New Gener. Comput. 8(4), 361–370 (1991)

    CrossRef  MATH  Google Scholar 

  13. Liśkiewicz, M., Reischuk, R., Wölfel, U.: Grey-box steganography. TCS 505, 27–41 (2013)

    CrossRef  MATH  Google Scholar 

  14. Reidenbach, D.: A negative result on inductive inference of extended pattern languages. In: Cesa-Bianchi, N., Numao, M., Reischuk, R. (eds.) ALT 2002. LNCS (LNAI), vol. 2533, pp. 308–320. Springer, Heidelberg (2002)

    CrossRef  Google Scholar 

  15. Reischuk, R., Zeugmann, T.: An average-case optimal one-variable pattern language learner. JCSS 60(2), 302–335 (2000)

    MathSciNet  MATH  Google Scholar 

  16. Salomaa, A.: Patterns. EATCS Bull. 54, 46–62 (1994)

    Google Scholar 

  17. Shinohara, T.: Polynomial time inference of extended regular pattern languages. In: Goto, E., Furukawa, K., Nakajima, R., Nakata, I., Yonezawa, A. (eds.) RIMS Symposia on Software Science and Engineering. LNCS, pp. 115–127. Springer, Heidelberg (1983)

    CrossRef  Google Scholar 

  18. Shinohara, T., Arikawa, S.: Pattern inference. In: Lange, S., Jantke, K.P. (eds.) GOSLER 1994. LNCS, vol. 961, pp. 259–291. Springer, Heidelberg (1995)

    CrossRef  Google Scholar 

  19. Stephan, F., Yoshinaka, R., Zeugmann, T.: On the parameterised complexity of learning patterns. In: Proceedings of 26th Computer and Information Sciences, pp. 277–281 (2011)

    Google Scholar 

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Correspondence to Sebastian Berndt .

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Berndt, S., Reischuk, R. (2016). Steganography Based on Pattern Languages. In: Dediu, AH., Janoušek, J., Martín-Vide, C., Truthe, B. (eds) Language and Automata Theory and Applications. LATA 2016. Lecture Notes in Computer Science(), vol 9618. Springer, Cham.

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  • Print ISBN: 978-3-319-29999-0

  • Online ISBN: 978-3-319-30000-9

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