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Journals / Uludağ Üniversitesi Mühendislik Fakültesi Dergisi / 2026 / Cilt: 19 Sayı: 1

Konuşma Sesleri Steganalizine Gecikmeli Vektör Varyans Metodunu Kullanan Yeni Bir Yaklaşım

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

Emrah Yürüklü
Osman Hilmi Koçal
Erdoğan Dilaveroğlu
Pages
27–36
Publication Date
2014-04-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

We evaluate the performance of the proposed DVV features as steganalyzer with numerical results

📌 3 Key Takeaways

🎯
Objective & Hypothesis:We investigate the use of delay vector variance-based features for recorded speech steganalysis.
📊
Key Findings & Data: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.
💡
Conclusion & Impact:We evaluate the performance of the proposed DVV features as steganalyzer with numerical results
🔬Design:Makale
📍Scope:Comprehensive Sample
⚙️Variables:Steganografi · steganaliz · konuşma

Abstract

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

Özet

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

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Cite This Article

Yürüklü, E., Koçal, O. H., Dilaveroğlu, E. (2014). Konuşma Sesleri Steganalizine Gecikmeli Vektör Varyans Metodunu Kullanan Yeni Bir Yaklaşım. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, Cilt: 19 Sayı: 1, 27–36. https://doi.org/10.17482/uujfe.19395

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References (50)

  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.
  50. Springer-Verlag Berlin Heidelberg.