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A new audio steganalysis method based on linear prediction

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Abstract

Steganography and Steganalysis have attracted a lot of attention in decades. Recently, voice communication has been more and more popular, which provides ways to covert communication. However, the existing audio steganalysis methods can only gain good detection accuracies when the hidden ratio is high. Besides, majority of the audio steganalysis methods can not provide a general evaluation, only provide the detection accuracies according to several high hidden ratios. In this paper, we proposed a new method for audio steganalysis by introducing linear prediction method, a technique from signal coding and speaker identification filed, into audio steganalysis, which can bring significant differences between covers and stegos. The linear prediction based features are utilized as the classification features loaded in a support vector machine for detection. In our work we used hidden message to cover ratio to replace the concept of hidden ratio, providing a uniform criterion to compare the performance among steganalysis methods. Furthermore, we exploited a general dataset, in which the hidden message size ranges from several bits to the maximum hiding capacity for a general evaluation on steganalysis methods. Experiment results show that our method delivers a better performance than previous two prestigious methods and brings above 96% accuracy. In general evaluation, our method gains a higher score than the other two methods. Steganalysis is a challenging work, this linear prediction based method maybe an approach to bring improvement to this filed and provide inspiration for other form of media steganalysis.

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References

  1. Avci E (2009) Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithmCsupport vector machines: HGASVM. Expert Syst Appl 36(2):1391–1402

    Article  Google Scholar 

  2. Couellan N, Jan S, Jorquera T (2015) Self-adaptive support vector machine: A multi-agent optimization perspective. Expert Syst Appl 42(9):4284–4298

    Article  Google Scholar 

  3. Desai MB, Patel SV, Prajapati B (2016) ANOVA and fisher criterion based feature selection for lower dimensional universal image steganalysis. Int J Image Process (IJIP) 10(3):145

    Google Scholar 

  4. Djebbar F, Ayad B (2012) Audio steganalysis based on lossless data-compression techniques. In: International Conference on Information and Communications Security. Springer, Berlin Heidelberg, pp 1–9

  5. Du SC, Huang DL, Wang H (2015) An adaptive support vector machine-based workpiece surface classification system using high-definition metrology. IEEE Trans Instrum Measur 64(10):2590–2604

    Article  Google Scholar 

  6. Geiser B, Vary P (2008) High rate data hiding in ACELP speech codecs. In: IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE Xplore, pp 4005–4008

  7. Ghasemzadeh H, Arjmandi MK (2014) Reversed-Mel cepstrum based audio steganalysis. In: 4th International eConference on Computer and Knowledge Engineering (ICCKE). IEEE, pp 679–684

  8. Ghasemzadeh H, Khass MT, Arjmandi MK (2016) Audio steganalysis based on reversed psychoacoustic model of human hearing. Digit signal process 51:133–141

    Article  MathSciNet  Google Scholar 

  9. Kraetzer C, Dittmann J (6505) Mel-cepstrum based steganalysis for VoIP steganography. Proc SPIE - Int Soc Opt Eng 6505:650505–650505-12

    Google Scholar 

  10. Liao X, Shu C (2015) Reversible data hiding in encrypted images based on absolute mean difference of multiple neighboring pixels. J Vis Commun Image Represent 28:21–27

    Article  Google Scholar 

  11. Liu Q, Sung AH, Qiao M (2009) Temporal derivative-based spectrum and mel-cepstrum audio steganalysis. IEEE Trans Inf Forensic Secur 4(3):359–368

    Article  Google Scholar 

  12. Liu Q, Sung AH, Qiao M (2011) Derivative-based audio steganalysis. ACM Trans Multimed Comput Commun Applicat (TOMM) 7(3):18

    Google Scholar 

  13. Liu D, Niu D, Wang H (2014) Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm. Renew Energy 62:592–597

    Article  Google Scholar 

  14. Makhoul J (1975) Linear prediction: A tutorial review. Proc IEEE 63(4):561–580

    Article  Google Scholar 

  15. Miao H, Huang L, Chen Z et al (2012) A new scheme for covert communication via 3G encoded speech. Comput Electr Eng 38(6):1490–1501

    Article  Google Scholar 

  16. Molau S, Pitz M, Schluter R et al (2001) Computing mel-frequency cepstral coefficients on the power spectrum. In: 2001. Proceedings. (ICASSP’01). 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol 1. IEEE, pp 73–76

  17. Nouri A, Nazari A (2016) Improving Image steganalysis performance using a graph-based feature selection method. Adv Comput Sci: Int J 5(3):33–39

    Google Scholar 

  18. Ozer H, Avcibas I, Sankur B et al (2003) Steganalysis of audio based on audio quality metrics. Int Soc Opt Photonics Electron Imaging 2003 2003:55–66

    Google Scholar 

  19. Qian Y, Dong J, Wang W et al (2016) Learning and transferring representations for image steganalysis using convolutional neural network. In: IEEE International Conference on Image Processing (ICIP). IEEE, pp 2752–2756

  20. Qiao M, Sung AH, Liu Q (2013) MP3 audio steganalysis. Inf Sci 231 (9):123–134

    Article  Google Scholar 

  21. Ren Y, Cai T, Tang M et al (2015) AMR steganalysis based on the probability of same pulse position. IEEE Trans Inf Forensic Secur 10(9):1–1

    Article  Google Scholar 

  22. Ren Y, Xiong Q, Wang L (2016) Steganalysis of AAC using calibrated Markov model of adjacent codebook. In: IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, pp 2139–2143

  23. Ru XM, Zhang HJ, Huang X (2005) Steganalysis of audio: Attacking the steghide. In: Proceedings of 2005 International Conference on Machine Learning and Cybernetics, vol 7. IEEE, pp 3937–3942

  24. Srivastava S, Nandi P, Sahoo G et al (2014) Formant based linear prediction coefficients for speaker identification. In: 2014 International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, pp 685–688

  25. Tang BT, Guo L, Liu ZH (2008) An information hiding method in advanced audio coding (AAC). Tech Acoust 27(4):533–538

    Google Scholar 

  26. Tian H, Wu Y, Chang CC et al (2016) Steganalysis of adaptive multi-rate speech using statistical characteristics of pulse pairs. Signal Process 134:9–22

    Article  Google Scholar 

  27. Tint Y, Mya KT (2012) Audio steganalysis using features extraction and classification. International Journal of Research and Reviews in Computer Science (IJRRCS) 3(2):1593–1595

    Google Scholar 

  28. Wu Q (2010) Power load forecasts based on hybrid PSO with Gaussian and adaptive mutation and Wv-SVM. Expert Syst Appl 37(1):194–201

    Article  Google Scholar 

  29. Wu CH, Tzeng GH, Goo YJ (2007) A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy. Expert Syst Appl 32(2):397–408

    Article  Google Scholar 

  30. Wu A, Feng G, Zhang X, et al. (2016) Unbalanced JPEG image steganalysis via multiview data match. J Vis Commun Image Represent 34:103–107

    Article  Google Scholar 

  31. Xia Z, Wang X, Sun X, Liu Q, Xiong N (2016) Steganalysis of LSB matching using differences between nonadjacent pixels. Multimed Tools Appl 75 (4):1947–1962

    Article  Google Scholar 

  32. Yan D, Wang R, Yu X et al (2013) Steganalysis for MP3Stego using differential statistics of quantization step. Digit Signal Process 23(4):1181–1185

    Article  MathSciNet  Google Scholar 

  33. Yavanoglu U, Ozcakmak B, Milletsever O (2012) A new intelligent steganalysis method for waveform audio files. In: 11th International Conference on Machine Learning and Applications (ICMLA), vol 2. IEEE, pp 233–239

  34. Yu X, Wang R, Yan D et al (2012) MP3 audio steganalysis using calibrated side information feature. J Comput Inf Syst 8(10):4241–4248

    Google Scholar 

  35. Yuan C, Xia Z, Sun X (2017) Coverless image steganography based on SIFT and BOF. J Internet Technol 18(2):435–442

    Google Scholar 

  36. Zhai S, Jiang T (2015) A new sense-through-foliage target recognition method based on hybrid differential evolution and self-adaptive particle swarm optimization-based support vector machine. Neurocomputing 149:573–584

    Article  Google Scholar 

  37. Zhu J, Wang RD, Li J et al (2011) A huffman coding section-based steganography for AAC audio. Inf Technol J 10(10):1983–1988

    Article  Google Scholar 

Download references

Acknowledgements

The authors are supported by National Natural Science Foundation of China (No.61402471, 61472414). We wish to thank Professor Tang and Professor Zuo for their substantial support, insightful comments and suggestions, Dr. Chen Gong, Dr. Hailong Zhang and Professor Li for their discussion and guidance. Special thanks goes to Mr and Mrs Han for their understanding and support.

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Authors and Affiliations

  1. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Chunling Han, Rui Xue, Rui Zhang & Xueqing Wang

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  1. Chunling Han
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  2. Rui Xue
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  3. Rui Zhang
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  4. Xueqing Wang
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Correspondence to Rui Xue.

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Han, C., Xue, R., Zhang, R. et al. A new audio steganalysis method based on linear prediction. Multimed Tools Appl 77, 15431–15455 (2018). https://doi.org/10.1007/s11042-017-5123-x

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  • DOI: https://doi.org/10.1007/s11042-017-5123-x

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