[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

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

  • Conference paper
  • First Online:
Algorithms and Computation (ISAAC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9472))

Included in the following conference series:

Abstract

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.

M. Ernst—This work was supported by the Graduate School for Computing in Medicine and Life Sciences funded by Germany’s Excellence Initiative [DFG GSC 235/2].

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 59.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 74.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Alekhnovich, M., Braverman, M., Feldman, V., Klivans, A.R., Pitassi, T.: The complexity of properly learning simple concept classes. J. Comput. Syst. Sci. 74(1), 16–34 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  2. Blumer, A., Ehrenfeucht, A., Haussler, D., Warmuth, M.K.: Learnability and the Vapnik-Chervonenkis dimension. J. ACM 36(4), 929–965 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  3. De, A., Diakonikolas, I., Servedio, R.A.: Learning from satisfying assignments. In: Indyk, P. (ed.) Proc. SODA, pp. 478–497. SIAM, Philadelphia (2015)

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  5. Flammini, M., Marchetti-Spaccamela, A., Kučera, L.: Learning DNF formulae under classes of probability distributions. In: Proc. COLT, pp. 85–92. ACM, New York (1992)

    Google Scholar 

  6. Fridrich, J.: Steganography in digital media: principles, algorithms, and applications. Cambridge University Press, New York (2009)

    Book  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  9. Ker, A.D., Bas, P., Böhme, R., Cogranne, R., Craver, S., Filler, T., Fridrich, J., Pevný, T.: Moving steganography and steganalysis from the laboratory into the real world. In: Proc. IH&MMSec, pp. 45–58. ACM, New York (2013)

    Google Scholar 

  10. Kodovsky, J., Fridrich, J., Holub, V.: Ensemble classifiers for steganalysis of digital media. IEEE T. Inform. Forensics and Sec. 7(2), 432–444 (2012)

    Article  Google Scholar 

  11. Kucera, L., Marchetti-Spaccamela, A., Protasi, M.: On learning monotone DNF formulae under uniform distributions. Inform. Comput. 110(1), 84–95 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  12. Liśkiewicz, M., Reischuk, R., Wölfel, U.: Grey-box steganography. Theor. Comput. Sc. 505, 27–41 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  13. Natarajan, B.K.: Probably approximate learning of sets and functions. SIAM J. Comput. 20(2), 328–351 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  14. Pitt, L., Valiant, L.G.: Computational limitations on learning from examples. J. ACM 35(4), 965–984 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  15. Sakai, Y., Maruoka, A.: Learning monotone log-term DNF formulas under the uniform distribution. Theory of Comput. Syst. 33(1), 17–33 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  16. Sakai, Y., Maruoka, A.: Learning \(k\)-term monotone boolean formulae. In: Doshita, S., Furukawa, K., Jantke, K.P., Nishida, T. (eds.) ALT 1992. LNCS, vol. 743, pp. 195–207. Springer, Heidelberg (1993)

    Chapter  Google Scholar 

  17. Schaathun, H.G.: Machine Learning in Image Steganalysis. Wiley-IEEE Press, Chichester (2012)

    Book  Google Scholar 

  18. Valiant, L.G.: A theory of the learnable. CACM 27(11), 1134–1142 (1984)

    Article  MATH  Google Scholar 

  19. Verbeurgt, K.: Learning DNF under the uniform distribution in quasi-polynomial time. In: Proc. COLT, pp. 314–326. Morgan Kaufmann Publishers Inc., San Francisco (1990)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rüdiger Reischuk .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ernst, M., Liśkiewicz, M., Reischuk, R. (2015). Algorithmic Learning for Steganography: Proper Learning of k-term DNF Formulas from Positive Samples. In: Elbassioni, K., Makino, K. (eds) Algorithms and Computation. ISAAC 2015. Lecture Notes in Computer Science(), vol 9472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48971-0_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-48971-0_14

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-48970-3

  • Online ISBN: 978-3-662-48971-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics