Advertisement

Steganography Based on Pattern Languages

Conference paper
First Online:
  • 1.2k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9618)

Abstract

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.

Keywords

Language-based cryptography Steganography Pattern 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Angluin, D.: Finding patterns common to a set of strings. JCSS 21(1), 46–62 (1980)MathSciNetzbMATHGoogle Scholar
  2. 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. 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)CrossRefMathSciNetzbMATHGoogle Scholar
  4. 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)CrossRefMathSciNetzbMATHGoogle Scholar
  5. 5.
    Dedić, N., Itkis, G., Reyzin, L., Russell, S.: Upper and lower bounds on black-box steganography. J. Cryptol. 22(3), 365–394 (2009)CrossRefzbMATHGoogle Scholar
  6. 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)CrossRefGoogle Scholar
  7. 7.
    Hopper, N., von Ahn, L., Langford, J.: Provably secure steganography. IEEE Trans. Comput. 58(5), 662–676 (2009)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Jerrum, M., Valiant, L.G., Vazirani, V.V.: Random generation of combinatorial structures from a uniform distribution. TCS 43, 169–188 (1986)CrossRefMathSciNetzbMATHGoogle Scholar
  9. 9.
    Katzenbeisser, S., Petitcolas, F.A.: Defining security in steganographic systems. In: Electronic Imaging 2002, SPIE, pp. 50–56 (2002)Google Scholar
  10. 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. 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. 12.
    Lange, S., Wiehagen, R.: Polynomial-time inference of arbitrary pattern languages. New Gener. Comput. 8(4), 361–370 (1991)CrossRefzbMATHGoogle Scholar
  13. 13.
    Liśkiewicz, M., Reischuk, R., Wölfel, U.: Grey-box steganography. TCS 505, 27–41 (2013)CrossRefzbMATHGoogle Scholar
  14. 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)CrossRefGoogle Scholar
  15. 15.
    Reischuk, R., Zeugmann, T.: An average-case optimal one-variable pattern language learner. JCSS 60(2), 302–335 (2000)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Salomaa, A.: Patterns. EATCS Bull. 54, 46–62 (1994)Google Scholar
  17. 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)CrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 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

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Theoretical Computer ScienceUniversity of LübeckLübeckGermany

Personalised recommendations

Cite paper

Buy options