Konuşma Sesleri Steganalizine Gecikmeli Vektör Varyans Metodunu Kullanan Yeni Bir Yaklaşım

Öz

We investigate the use of delay vector variance-based features for recorded speech steganalysis. Considering that data hiding within a speech signal distorts the properties of the original speech signal, we design a steganalyzer that uses surrogate data based delay vector variance (DVV) features to detect the existence of a stego-signal. We evaluate the performance of the proposed DVV features as steganalyzer with numerical results

Anahtar Kelimeler

• Steganography, steganalysis, speech, chaos, false-neighbors, Lyapunov exponent, surrogate data, delay vector variance

A New Approach For Speech Audio Steganalysis Using Delay Vector Variance Method

Öz

Bu çalışmada gecikmeli vektör varyans metodunun, kayıt edilmiş konuşma seslerinin steganalizinde kullanımı araştırılmıştır. Konuşma sinyali içerisine veri gizlemenin orijinal ses verisinin özelliklerini bozacağı gerçeğinden göz önünde bulundurarak, vekil verileri kullanan gecikmeli vektör varyans (DVV) özelliklerindeki değişimi kontrol eden ve böylece stego-sinyalin varlığını tespit etmeye çalışan yeni bir steganalizör tasarlanmıştır. DVV özelliklerini baz alan önerilen steganalizörün performans başarımı ise nümerik değerlerle verilmiştir

Anahtar Kelimeler

• Steganografi, steganaliz, konuşma, kaos, hatalı-komşular, Lyapunov üsteli, vekil veri, gecikmeli vektör varyans

Kaynakça

1. Westfeld, A.P. (2003). Detecting Low Embedding Rates, Fabien A. P. Petitcolas (Ed.):
2. Information Hiding. 5th International Workshop, IH 2002 Noordwijkerhout, pp. 324–339,
3. Springer-Verlag Berlin Heidelberg..
4. Fridrich, J. and Goljan, M. (2002). Practical Steganalysis of Digital Images - State of the Art,
5. Proc. SPIE Photonics West, Vol. 4675, pp. 1—13.
6. Johnson, M.K., Lyu, S. and Farid, H. (2005). Steganalysis of Recorded Speech, Proc. SPIE
7. Symposium on Electronic Imaging, San Jose, CA.
8. Westfeld, A.P. and Pfitzmann, A. (1999). Attacks on steganographic systems, Information

9. Hiding, LNCS 1768, pp. 61-66, Springer-Verlag Heidelberg.
10. Koçal, O.H., Yürüklü, E. and Avcıbaş, İ. (2008). Chaotic-type features for Speech
11. Steganalysis, IEEE Transactions on Information Forensics and Security, Vol. 3, Issue 4, pp. 651- 661.
12. Altun, O., Sharma, G., Celik, M., Sterling, M., Titlebaum, E. and Bocko, M. (2005).
13. Morphological Steganalysis of Audio Signals and the Principle of Diminishing Marginal
14. Distortions”, IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, Proceedings. (ICASSP
15. '05), Vol. 2, pp. 21- 24.
16. Ozer, H., Avcibas, I.,, Sankur, B. and Memon, N. (2003). Steganalysis of Audio Based on
17. Audio Quality Metrics, Proc. SPIE, Security and Watermarking of Multimedia Contents V, Vol. 5020, pp. 55-66.
18. Kokkinos, I. and Maragos, P. (2005). Nonlinear Speech Analysis Using Models for Chaotic
19. Systems, IEEE Transactions on Speech and Audio Processing, Vol. 13, No. 6, pp. 1098-1109.
20. Banbrook, M. and McLaughlin, S., (1999). Speech Characterization and Synthesis by
21. Nonlinear Methods”, IEEE Transactions on Speech and Audio Processing, Vol. 7, No. 1, pp. 1- 17.
22. Theiler, J., Eubank, S., Longtin, A., Galdrikian, B. and Farmer, J. D. (1992). Testing for
23. Nonlinearity in Time Series: The Method of Surrogate Data, Physica D 58. pp. 77 - 94.
24. Gautama, T., Mandic, D.P. and Van Hulle, M.M. (2003). Indications of nonlinear structures
25. in brain electrical activity, Physical Review E, Vol. 67, No. 046204.
26. Schreiber, T. and Schmitz, A. (1997). Discrimination power of measures for nonlinearity in
27. a time series, Phys. Rev. E, Vol:55. pp. 5443-5447.
28. Gautama, T., Mandic, D.P. and Van Hulle, M.M. (2004). A Novel Method for Determining
29. the Nature of Time Series, IEEE Transactions On Bıomedical Engineering, Vol. 51, No. 5.
30. Simmons, G. J. (1984). Prisoners' Problem and the Subliminal Channel (The), CRYPTO83
31. Advances in Cryptology, pp. 51-67.
32. Bender, W., Gruhl, D., Morimoto, N. and Lu, A. (1996). Techniques for data hiding, IBM
33. Systems Journal, vol. 35, no: 3&4, pp. 313-336.
34. Fridrich, J. and Goljan, M. (2003). Digital Image Steganography Using Stochastic
35. Modulation, Proc. SPIE Electronic Imaging, Santa Clara, CA, pp. 191-202.
36. Cox., I. J., Kilian, J., Leighton, F.T. and Shamoon, T. (1997). Secure spread spectrum
37. watermarking for multimedia, IEEE Tr. Image Proc., vol 6, pp. 1673 – 1687.
38. www.steganos.com,. Access Date: 23.Nov.2011, Subject: Steganos.
39. www.petitcolas.net/fabien/ steganography/mp3stego,Access Date: 12.Sep.2011, Subject: Mp3stego.
40. http://steghide.sourceforge.net, Access Date: 13.Sep.2011, Subject: S. Hetzl.
41. www.heinz-repp.onlinehome.de/Hide4PGP.htm, Access Date: 12.Sep.2011, Subject: Hide4PGP
42. http://www.mpi.nl/world/tg/corpora/timit/timit.html, Access Date: 05.Jul.2011, Subject: TIMIT Speech Database
43. Kennel, M. B. and Abarbanel, H. D. I. (2002). False neighbors and false strands: A reliable minimum embedding dimension algorithm”, Physical Review E, Vol. 66, 026209.
44. Hilborn, R. (2000). Chaos and Nonlinear Dynamics, Oxford University Press, 2. ed.
45. Hegger, R., Kantz, H. and Schreiber, T. (1999). Practical Implementation of Nonlinear Time Series Methods: The TISEAN Package, Chaos, Vol. 9, pp. 413-435.
46. Pudil, P., Novovicova, J. and Kittler, J. (1994). Floating Search Methods in Feature Selection, Pattern Recognition Lett. Vol 15, pp. 1119-1125.
47. http://eewww.eng.ohio-state.edu/~maj/osu_svm/, Access Date: 24.Sep.2011, Subject: OSU- SVM Matlab Toolbox.
48. Özer, H., Sankur, B., Memon, N. and Avcıbaş, İ. (2006). Detection of Audio Covert Channels Using Statistical Footprints of Hidden Messages, Digital Signal Processing, Vol.16, pp. 389-401.
49. Makale 19.11.2013 tarihinde alınmış, 07.12.2013 tarihinde düzeltilmiş, 09.12.2013 tarihinde kabul edilmiştir.