Table of Contents Author Guidelines Submit a Manuscript
Mobile Information Systems
Volume 2017, Article ID 5418978, 18 pages
https://doi.org/10.1155/2017/5418978
Research Article

Detecting Steganography of Adaptive Multirate Speech with Unknown Embedding Rate

1College of Computer Science and Technology, National Huaqiao University, Xiamen 361021, China
2Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

Correspondence should be addressed to Hui Tian; nc.ude.uqh@naith and Tian Wang; nc.ude.uqh@naitgnaw

Received 9 December 2016; Accepted 23 April 2017; Published 18 May 2017

Academic Editor: Elio Masciari

Copyright © 2017 Hui Tian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. N. Provos and P. Honeyman, “Hide and seek: an introduction to steganography,” IEEE Security and Privacy, vol. 99, no. 3, pp. 32–44, 2003. View at Publisher · View at Google Scholar · View at Scopus
  2. E. Zielińska, W. Mazurczyk, and K. Szczypiorski, “Trends in steganography,” Communications of the ACM, vol. 57, no. 3, pp. 86–95, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Cheddad, J. Condell, K. Curran, and P. Mc Kevitt, “Digital image steganography: survey and analysis of current methods,” Signal Processing, vol. 90, no. 3, pp. 727–752, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. B. Li, M. Wang, X. Li, S. Tan, and J. Huang, “A strategy of clustering modification directions in spatial image steganography,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 9, pp. 1905–1917, 2015. View at Publisher · View at Google Scholar · View at Scopus
  5. M. M. Sadek, A. S. Khalifa, and M. G. M. Mostafa, “Video steganography: a comprehensive review,” Multimedia Tools and Applications, vol. 74, no. 17, pp. 7063–7094, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Ramalingam and N. A. M. Isa, “A data-hiding technique using scene-change detection for video steganography,” Computers & Electrical Engineering, vol. 54, pp. 423–434, 2016. View at Publisher · View at Google Scholar
  7. F. Djebbar, B. Ayad, K. A. Meraim, and H. Hamam, “Comparative study of digital audio steganography techniques,” Eurasip Journal on Audio, Speech, and Music Processing, vol. 2012, no. 1, article 25, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. G. Hua, J. Huang, Y. Q. Shi, J. Goh, and V. L. L. Thing, “Twenty years of digital audio watermarking - A comprehensive review,” Signal Processing, vol. 128, pp. 222–242, 2016. View at Publisher · View at Google Scholar · View at Scopus
  9. E. Satir and H. Isik, “A compression-based text steganography method,” Journal of Systems and Software, vol. 85, no. 10, pp. 2385–2394, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. C.-Y. Chang and S. Clark, “Practical linguistic steganography using contextual synonym substitution and a novel vertex coding method,” Computational Linguistics, vol. 40, no. 2, pp. 403–448, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Lubacz, W. Mazurczyk, and K. Szczypiorski, “Principles and overview of network steganography,” IEEE Communications Magazine, vol. 52, no. 5, pp. 225–229, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. W. Mazurczyk, S. Wendzel, S. Zander, A. Houmansadr, and K. Szczypiorski, Information Hiding in Communication Networks: Fundamentals, Mechanisms, Applications, and Countermeasures, John Wiley & Sons, Inc., Hoboken, New Jersey, 2016. View at Publisher · View at Google Scholar
  13. W. Mazurczyk, “VoIP steganography and its detection-a survey,” ACM Computing Surveys, vol. 46, no. 2, article 20, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. H. Tian, J. Qin, S. Guo et al., “Improved adaptive partial-matching steganography for Voice over IP,” Computer Communications, vol. 70, pp. 95–108, 2015. View at Publisher · View at Google Scholar · View at Scopus
  15. H. Tian, J. Qin, Y. Huang et al., “Optimal matrix embedding for Voice-over-IP steganography,” Signal Processing, vol. 117, pp. 33–43, 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. Y. Jiang, S. Tang, L. Zhang, M. Xiong, and Y. J. Yip, “Covert voice over internet protocol communications with packet loss based on fractal interpolation,” ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 12, no. 4, article 54, pp. 1–20, 2016. View at Publisher · View at Google Scholar
  17. A. Janicki, W. Mazurczyk, and K. Szczypiorski, “Steganalysis of transcoding steganography,” Annals of Telecommunications/Annales des Télécommunications, vol. 69, no. 7-8, pp. 449–460, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. Z. Xia, X. Wang, X. Sun, and B. Wang, “Steganalysis of least significant bit matching using multi-order differences,” Security and Communication Networks, vol. 7, no. 8, pp. 1283–1291, 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. V. Holub and J. Fridrich, “Low-complexity features for JPEG steganalysis using undecimated DCT,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 2, pp. 219–228, 2015. View at Publisher · View at Google Scholar · View at Scopus
  20. Z. Xia, X. Wang, X. Sun, Q. Liu, and N. Xiong, “Steganalysis of LSB matching using differences between nonadjacent pixels,” Multimedia Tools and Applications, vol. 75, no. 4, pp. 1947–1962, 2016. View at Publisher · View at Google Scholar · View at Scopus
  21. W. Tang, H. Li, W. Luo, and J. Huang, “Adaptive steganalysis based on embedding probabilities of pixels,” IEEE Transactions on Information Forensics and Security, vol. 11, no. 4, pp. 734–744, 2016. View at Publisher · View at Google Scholar · View at Scopus
  22. J. Yu, F. Li, H. Cheng, and X. Zhang, “Spatial steganalysis using contrast of residuals,” IEEE Signal Processing Letters, vol. 23, no. 7, pp. 989–992, 2016. View at Publisher · View at Google Scholar · View at Scopus
  23. T. Denemark, M. Boroumand, and J. Fridrich, “Steganalysis features for content-adaptive JPEG steganography,” IEEE Transactions on Information Forensics and Security, vol. 11, no. 8, pp. 1736–1746, 2016. View at Publisher · View at Google Scholar · View at Scopus
  24. H. Tian, Y. Wu, Y. Cai et al., “Distributed steganalysis of compressed speech,” Soft Computing, vol. 21, no. 3, pp. 795–804, 2017. View at Google Scholar
  25. H. Tian, Y. Wu, C. C. Chang et al., “Steganalysis of analysis-by-synthesis speech exploiting pulse-position distribution characteristics,” Security and Communication Networks, vol. 9, no. 15, pp. 2934–2944, 2016. View at Publisher · View at Google Scholar
  26. 3GPP/ETSI, “AMR speech codec: general description, version 10.0.0,” Technical Report TS 26 071, Sophia Antipolis Cedex, France, April 2011. View at Google Scholar
  27. 3GPP/ETSI., “Performance characterization of the adaptive multi-rate (AMR) speech codec,” Technical Report TR 126 975, Sophia Antipolis Cedex, France, January 2009. View at Google Scholar
  28. 3GPP/ETSI, “Digital cellular telecommunications system (phase 2+); Universal mobile telecommunications system (UMTS); LTE: mandatory speech codec speech processing functions; Adaptive multi-rate (AMR) speech codec; Transcoding functions (3GPP TS 26.090 version 13.0.0 Release 13),” Technical Report TR 126 090, Sophia Antipolis Cedex, France, January 2016. View at Google Scholar
  29. B. Geiser and P. Vary, “High rate data hiding in ACELP speech codecs,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '08), pp. 4005–4008, Las Vegas, Nev, USA, April 2008. View at Publisher · View at Google Scholar · View at Scopus
  30. H. Miao, L. Huang, Z. Chen, W. Yang, and A. Al-Hawbani, “A new scheme for covert communication via 3G encoded speech,” Computers & Electrical Engineering, vol. 38, no. 6, pp. 1490–1501, 2012. View at Publisher · View at Google Scholar · View at Scopus
  31. H. Miao, L. Huang, Y. Shen, X. Lu, and Z. Chen, “Steganalysis of compressed speech based on Markov and entropy,” in Proceedings of the 12th International Workshop on Digital-Forensics and Watermarking (IWDW), pp. 63–76, Auckland, New Zealand, Oct. 2013. View at Publisher · View at Google Scholar
  32. Y. Ren, T. Cai, M. Tang, and L. Wang, “AMR steganalysis based on the probability of same pulse position,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 9, pp. 1801–1811, 2015. View at Publisher · View at Google Scholar · View at Scopus
  33. H. Tian, Y. Wu, Y. Huang et al., “Steganalysis of adaptive multi-Rate speech using statistical characteristics of pulse pairs,” Signal Processing, vol. 134, pp. 9–22, 2017. View at Publisher · View at Google Scholar
  34. Y. Freund, R. Schapire, and N. Abe, “A short introduction to boosting,” Journal of Japanese Society for Artificial Intelligence, vol. 14, pp. 771–780, 1999. View at Google Scholar · View at MathSciNet
  35. X. Wen, L. Shao, Y. Xue, and W. Fang, “A rapid learning algorithm for vehicle classification,” Information Sciences, vol. 295, pp. 395–406, 2015. View at Publisher · View at Google Scholar
  36. D. D. Le and S. Satoh, “Feature selection by adaboost for SVM-based face detection,” Information Technology Letters, vol. 3, pp. 183–186, 2004. View at Google Scholar
  37. Y.-J. Yeh and C.-T. Hsu, “Online selection of tracking features using AdaBoost,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 19, no. 3, pp. 442–446, 2009. View at Publisher · View at Google Scholar · View at Scopus
  38. L. Guo, P.-S. Ge, M.-H. Zhang, L.-H. Li, and Y.-B. Zhao, “Pedestrian detection for intelligent transportation systems combining AdaBoost algorithm and support vector machine,” Expert Systems with Applications, vol. 39, no. 4, pp. 4274–4286, 2012. View at Publisher · View at Google Scholar · View at Scopus
  39. A. D. Ker, P. Bas, R. Böhme et al., “Moving steganography and steganalysis from the laboratory into the real world,” in Proceedings of the 1st ACM Workshop on Information Hiding and Multimedia Security, IH and MMSec 2013, pp. 45–58, France, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  40. T. Pevny, “Detecting messages of unknown length,” in Proceedings of the Media Watermarking, Security, and Forensics III, vol. 7880, pp. 1–12, San Francisco Airport, California, USA, 2011. View at Publisher · View at Google Scholar
  41. L. Marvel, B. Henz, and C. Boncelet, “A performance study of ±1 steganalysis employing a realistic operating scenario,” in Proceedings of the 2007 IEEE Military Communications Conference, pp. 1–7, USA, October 2007. View at Publisher · View at Google Scholar · View at Scopus
  42. A. P. Dempster, “Upper and lower probabilities induced by a multivalued mapping,” Annals of Mathematical Statistics, vol. 38, pp. 325–339, 1967. View at Publisher · View at Google Scholar · View at MathSciNet
  43. G. Shafer, A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ, USA, 1976. View at MathSciNet
  44. R. R. Murphy, “Dempster-Shafer theory for sensor fusion in autonomous mobile robots,” IEEE Transactions on Robotics and Automation, vol. 14, no. 2, pp. 197–206, 1998. View at Publisher · View at Google Scholar · View at Scopus
  45. N. R. Pal and S. Ghosh, “Some classification algorithms integrating Dempster-Shafer theory of evidence with the rank nearest neighbor rules,” IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans., vol. 31, no. 1, pp. 59–66, 2001. View at Publisher · View at Google Scholar · View at Scopus
  46. T. M. Chen and V. Venkataramanan, “Dempster-Shafer theory for intrusion detection in ad hoc networks,” IEEE Internet Computing, vol. 9, no. 6, pp. 35–41, 2005. View at Publisher · View at Google Scholar · View at Scopus
  47. J. S. Perkell and D. H. Klatt, Invariance and Variability in Speech Processes, Lawrence Erlbaum Associates, Mahwah, New Jersey, USA, 1986.
  48. A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Statistics and Computing, vol. 14, no. 3, pp. 199–222, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  49. C. Chang and C. Lin, “LIBSVM: a Library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, article 27, 2011. View at Publisher · View at Google Scholar · View at Scopus
  50. B. Gu, V. S. Sheng, Z. Wang, D. Ho, S. Osman, and S. Li, “Incremental learning for ν-support vector regression,” Neural Networks, vol. 67, pp. 140–150, 2015. View at Publisher · View at Google Scholar
  51. B. Gu, V. S. Sheng, K. Y. Tay, W. Romano, and S. Li, “Incremental support vector learning for ordinal regression,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 7, pp. 1403–1416, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  52. B. Gu, V. S. Sheng, and S. Li, “Bi-parameter space partition for cost-sensitive SVM,” in Proceedings of the 24th International Joint Conference on Artificial Intelligence, pp. 3532–3539, Buenos Aires, Argentina, July 2015.
  53. B. Gu and V. S. Sheng, “A robust regularization path algorithm for v-support vector classification,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 5, pp. 1241–1248, 2017. View at Publisher · View at Google Scholar