Advertisement

Advanced Methods of Detection of the Steganography Content

  • Jakub HendrychEmail author
  • Lačezar Ličev
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 554)

Abstract

In this paper, we deal with the classification of the steganography content. Some illegal activities can perform steganography for stealing the secret information from a company internal network. Therefore, we must be prepared to protect our data. To detect steganography content, we have counter-technique known as steganalysis. There are different types of steganalysis, based on the existence of the original artifact (cover work) or if we know which algorithm embed a secret message. For practical use, most important are methods of blind steganalysis, that can be applied to the most compact and ordinary cover work - JPEG image files. This paper describes the methodology to the issues of JPEG image steganalysis. It is crucial to understand the behavior of the targeted steganography algorithm. Then we can use it is weaknesses to increase the detection capability and success of classification. We are primarily focusing on breaking the DCT steganography algorithm OutGuess2.0 and secondary on breaking the F5 algorithm. We are analyzing the ability of the detector, which utilizes the calibration process, blockiness calculation, and shallow neural network to identify the presence of steganography message in the suspected image. This approach is an improvement over our previous researches. Contribution and new results are discussed.

Keywords

Steganography Steganalysis Shallow neural network ANN JPEG DCT Calibration Blockiness OutGuess2.0 F5 

Notes

Acknowledgements

The following grants are acknowledged for the financial support provided for this research by Grant of SGS No. 2018/177, VSB-Technical University of Ostrava and under the support of NAVY and MERLIN research lab.

References

  1. 1.
    Johnson, N.F., Jajodia, S.: Exploring steganography: seeing the unseen. Computer 31(2), 26–34 (1998)CrossRefGoogle Scholar
  2. 2.
    Conway, M.: Code wars: steganography, signals intelligence, and terrorism. Knowl. Technol. Policy 16(2), 45–62 (2003)CrossRefGoogle Scholar
  3. 3.
    Fridrich, J., Goljan, M., Hogea, D.: Attacking the outguess. In: Proceedings of the ACM Workshop on Multimedia and Security. Juan-les-Pins, France (2002)Google Scholar
  4. 4.
    Chen, C.L.P., et al.: A pattern recognition system for JPEG steganography detection. Opt. Commun. 285(21), 4252–4261 (2012)CrossRefGoogle Scholar
  5. 5.
    Liu, Q., et al.: An improved approach to steganalysis of JPEG images. Inf. Sci. 180(9), 1643–1655 (2010)CrossRefGoogle Scholar
  6. 6.
    Provos, N.: Outguess 0.2 (2001). http://www.outguess.org/S
  7. 7.
    Westfeld, A.: F5—a steganographic algorithm. In: Information Hiding, pp. 289–302. Springer, Heidelberg (2001)Google Scholar
  8. 8.
    Gul, G., Kurugollu, F.: A new methodology in steganalysis: breaking highly undetectable steganograpy (HUGO). In: Information Hiding, pp. 71–84. Springer, Heidelberg (2011)Google Scholar
  9. 9.
    Zeng, J., et al.: Large-scale JPEG steganalysis using hybrid deep-learning framework. arXiv preprint arXiv:1611.03233 (2016)
  10. 10.
    Liu, Q., Chen, Z.: Z.: Improved approaches with calibrated neighboring joint density to steganalysis and seam-carved forgery detection in JPEG images. ACM Trans. Intell. Syst. Technol. (TIST) 5(4), 63 (2015)Google Scholar
  11. 11.
    Provos, N., Honeyman, P.: Hide and seek: an introduction to steganography. IEEE Secur. Priv. 99(3), 32–44 (2003)CrossRefGoogle Scholar
  12. 12.
    Upham, D.: Steganographic algorithm JSteg. Software (1993). http://zooid.org/~paul/crypto/jsteg
  13. 13.
    Provos, N.: Defending Against Statistical Steganalysis. In: Usenix Security Symposium, pp. 323–336 (2001)Google Scholar
  14. 14.
    Hendrych, J., Kunčický, R., Ličev, L.: New approach to steganography detection via steganalysis framework. In: International Conference on Intelligent Information Technologies for Industry, pp. 496–503. Springer, Cham (2017)Google Scholar
  15. 15.
    Hendrych, J., Ličev, L., Kunčický, R.: Detector of the steganography images with the application of artificial neural network. In: 17th International Multidisciplinary Scientific GeoConference SGEM2017, Informatics, Geoinformatics and Remote Sensing (2017).  https://doi.org/10.5593/sgem2017/21/s07.033
  16. 16.
    Fu, D., et al.: JPEG steganalysis using empirical transition matrix in block DCT domain. In: 2006 IEEE 8th Workshop on Multimedia Signal Processing, pp. 310–313. IEEE (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.VŠB-TU OstravaOstrava-PorubaCzech Republic

Personalised recommendations