Multimedia Tools and Applications

, Volume 77, Issue 23, pp 31487–31516 | Cite as

High capacity, transparent and secure audio steganography model based on fractal coding and chaotic map in temporal domain

  • Ahmed Hussain AliEmail author
  • Loay Edwar George
  • A. A. Zaidan
  • Mohd Rosmadi Mokhtar


Information hiding researchers have been exploring techniques to improve the security of transmitting sensitive data through an unsecured channel. This paper proposes an audio steganography model for secure audio transmission during communication based on fractal coding and a chaotic least significant bit or also known as HASFC. This model contributes to enhancing the hiding capacity and preserving the statistical transparency and security. The HASFC model manages to embed secret audio into a cover audio with the same size. In order to achieve this result, fractal coding is adopted which produces high compression ratio with the acceptable reconstructed signal. The chaotic map is used to randomly select the cover samples for embedding and its initial parameters are utilized as a secret key to enhancing the security of the proposed model. Unlike the existing audio steganography schemes, The HASFC model outperforms related studies by improving the hiding capacity up to 30% and maintaining the transparency of stego audio with average values of SNR at 70.4, PRD at 0.0002 and SDG at 4.7. Moreover, the model also shows resistance against brute-force attack and statistical analysis.


Fractal coding Least significant bit Steganography Information hiding Logistic map Statistical steganalysis 



This research is supported by the Ministry of Higher Education and Scientific Research, Studies Planning and Follow-up Directorate, Republic of Iraq and the Research Center for Software Technology & Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (DPP-2015-018).

h. References

  1. 1.
    Al-Haj A, An i, algorithm r a w (2014) EURASIP Journal on Audio. Speech, and Music Processing 2014(1):37CrossRefGoogle Scholar
  2. 2.
    Al-Hilo E, George LE (2008) Speeding-up fractal colored image compression using moments features. In: Digital Image Computing: Techniques and Applications (DICTA), 2008. IEEE. 486–490Google Scholar
  3. 3.
    Ali SM, George LE, Taher HB (2013) Speeding up Audio Fractal Compression. International Journal of Advanced Research in Computer Science and Software Engineering 3(6):7Google Scholar
  4. 4.
    Ali AH, Mokhtar MR, George LE (2016) Recent Approaches for VoIP Steganography. Indian Journal of Science and Technology 9(38):6Google Scholar
  5. 5.
    Ali AH, Mokhtar MR, George LE (2016) A Review on Audio Steganography Techniques. Res J Appl Sci Eng Technol 12(2):154–162Google Scholar
  6. 6.
    Ali AH, Mokhtar MR, George LE (2017) Enhancing the hiding capacity of audio steganography based on block mapping. Journal of Theoretical & Applied Information Technology:95(7), 1441–1448Google Scholar
  7. 7.
    Atawneh S, et al (2016) Secure and imperceptible digital image Steganographic algorithm based on diamond encoding in DWT domain. Multimedia Tools and Applications 76(18):18451–18472CrossRefGoogle Scholar
  8. 8.
    Ballesteros L (2012) D.M. and J.M. Moreno A, Highly transparent steganography model of speech signals using Efficient Wavelet Masking. Expert Syst Appl 39(10):9141–9149CrossRefGoogle Scholar
  9. 9.
    Barnsley MF, Sloan AD (1988) A better way to compress images. Byte 13(1):215–223Google Scholar
  10. 10.
    Bazyar M, Sudirman R (2015) A New Method to Increase the Capacity of Audio Steganography Based on the LSB Algorithm. Jurnal Teknologi 74(6):49–53CrossRefGoogle Scholar
  11. 11.
    Bedan AK, George LE (2013) Speeding-up Fractal Audio Compression Using Moment Descriptors. International Journal of Scientific & Engineering Research 4(7):5Google Scholar
  12. 12.
    Bhat V, Sengupta I, Das A (2010) An adaptive audio watermarking based on the singular value decomposition in the wavelet domain. Digital Signal Processing 20(6):1547–1558CrossRefGoogle Scholar
  13. 13.
    Cheddad A et al (2010) Digital image steganography: Survey and analysis of current methods. Signal Process 90(3):727–752CrossRefGoogle Scholar
  14. 14.
    Djebbar F et al (2012) Comparative study of digital audio steganography techniques. EURASIP Journal on Audio, Speech, and Music Processing 2012(1):1–16CrossRefGoogle Scholar
  15. 15.
    El-Khamy SE, Korany NO, El-Sherif MH (2016) A security enhanced robust audio steganography algorithm for image hiding using sample comparison in discrete wavelet transform domain and RSA encryption. Multimedia Tools and Applications 76(22):24091–24106CrossRefGoogle Scholar
  16. 16.
    El-Khamy SE, Korany N, El-Sherif MH (2017) Robust image hiding in audio based on integer wavelet transform and Chaotic maps hopping. in Radio Science Conference (NRSC), 2017 34th National. IEEEGoogle Scholar
  17. 17.
    George LE, Al-Hilo E (2011) Speeding-up Fractal Color Image Compression Using Moments Features Based on Symmetry Predictor. in Information Technology: New Generations (ITNG), 2011 Eighth International Conference on. IEEE. 508–513Google Scholar
  18. 18.
    Ghasemzadeh H, Khass MT, Arjmandi MK (2016) Audio steganalysis based on reversed psychoacoustic model of human hearing. Digital signal processing 51:133–141MathSciNetCrossRefGoogle Scholar
  19. 19.
    Ghebleh M, Kanso A (2014) A robust chaotic algorithm for digital image steganography. Commun Nonlinear Sci Numer Simul 19(6):1898–1907CrossRefGoogle Scholar
  20. 20.
    Hemalatha S, Acharya UD, Renuka A (2016) Audio data hiding technique using integer wavelet transform. Int J Electron Secur Digit Forensics 8(2):131–147CrossRefGoogle Scholar
  21. 21.
    Ibaida A, Al-Shammary D, Khalil I (2014) Cloud enabled fractal based ECG compression in wireless body sensor networks. Futur Gener Comput Syst 35:91–101CrossRefGoogle Scholar
  22. 22.
    Jacquin AE (1992) Image coding based on a fractal theory of iterated contractive image transformations. IEEE Trans Image Process 1(1):18–30CrossRefGoogle Scholar
  23. 23.
    Jaferzadeh K, Moon I, Gholami S (2016) Enhancing fractal image compression speed using local features for reducing search space. Pattern Analysis and Applications, 1–10Google Scholar
  24. 24.
    Kamble SD, et al (2015) Color video compression based on fractal coding using quadtree weighted finite automata. In Information Systems Design and Intelligent Applications. Springer, 340:649–658Google Scholar
  25. 25.
    Kar DC, Mulkey CJ (2015) A multi-threshold based audio steganography scheme. Journal of Information Security and Applications 23:54–67CrossRefGoogle Scholar
  26. 26.
    Kaur A, et al (2017) Localized & self adaptive audio watermarking algorithm in the wavelet domain. Journal of Information Security and Applications 33:1–15CrossRefGoogle Scholar
  27. 27.
    Kekre HB, et al (2010) Increasing the capacity of the cover audio signal by using multiple LSBs for information hiding. in Emerging Trends in Engineering and Technology (ICETET), 2010 3rd International Conference on. IEEE 196–201Google Scholar
  28. 28.
    Kodituwakku S, Amarasinghe U (2010) Comparison of lossless data compression algorithms for text data. Indian journal of computer science and engineering 1(4):416–425Google Scholar
  29. 29.
    Rahim LBA, Bhattacharjee S, Aziz IB (2014) An Audio Steganography Technique to Maximize Data Hiding Capacity along with Least Modification of Host. In: Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013). Springer 277–289Google Scholar
  30. 30.
    Lee J-D, Chiou Y-H, Guo J-M (2013) Lossless data hiding for VQ indices based on neighboring correlation. Inf Sci 221:419–438CrossRefGoogle Scholar
  31. 31.
    Lei B et al (2012) A robust audio watermarking scheme based on lifting wavelet transform and singular value decomposition. Signal Process 92(9):1985–2001CrossRefGoogle Scholar
  32. 32.
    Lin G-S, Chang Y-T, Lie W-N (2010) A framework of enhancing image steganography with picture quality optimization and anti-steganalysis based on simulated annealing algorithm. IEEE Trans Multimedia 12(5):345–357CrossRefGoogle Scholar
  33. 33.
    Lin C-C et al (2015) A novel reversible data hiding scheme based on AMBTC compression technique. Multimedia Tools and Applications 74(11):3823–3842CrossRefGoogle Scholar
  34. 34.
    Liu Q, Sung AH, Qiao M (2011) Derivative-based audio steganalysis. ACM Transactions on Multimedia Computing. Communications, and Applications (TOMM) 7(3):18Google Scholar
  35. 35.
    Lou D-C (2012) C.-H. Hu, LSB steganographic method based on reversible histogram transformation function for resisting statistical steganalysis. Inf Sci 188:346–358CrossRefGoogle Scholar
  36. 36.
    Ma X et al (2015) Reversible data hiding scheme for VQ indices based on modified locally adaptive coding and double-layer embedding strategy. J Vis Commun Image Represent 28:60–70CrossRefGoogle Scholar
  37. 37.
    Malik A, Sikka G, Verma HK (2016) A high capacity text steganography scheme based on LZW compression and color coding. Engineering Science and Technology, an International Journal 20(1):72–79CrossRefGoogle Scholar
  38. 38.
    Mammeri A, Hadjou B, Khoumsi A (2012) A survey of image compression algorithms for visual sensor networks. ISRN Sensor Networks 2012 1–19CrossRefGoogle Scholar
  39. 39.
    Mohd BJ et al (2013) Hierarchical steganography using novel optimum quantization technique. Signal. Image and Video Processing 7(6):1029–1040MathSciNetCrossRefGoogle Scholar
  40. 40.
    Pan J-S, Huang H-C, Jain LCC (2009) Information Hiding and Applications, Springer, EditorGoogle Scholar
  41. 41.
    Petitcolas FA, Anderson RJ, Kuhn MG (1999) Information hiding-a survey. Proc IEEE 87(7):1062–1078CrossRefGoogle Scholar
  42. 42.
    Renza D, Lemus C (2018) Authenticity verification of audio signals based on fragile watermarking for audio forensics. Expert Syst Appl 91:211–222CrossRefGoogle Scholar
  43. 43.
    Renza D, Lemus C, Ballesteros DML (2017) Highly Transparent and Secure Scheme for Concealing Text Within Audio. in Iberoamerican Congress on Pattern Recognition. Springer 27–35Google Scholar
  44. 44.
    Sayood K (2012) Introduction to data compression, Fourth edn. Elsevier, New YorkCrossRefGoogle Scholar
  45. 45.
    Shahadi H, Jidin R (2011) High Capacity and Resistance to Additive Noise Audio Steganography Algorithm. International Journal of Computer Science Issues 8(2):176–184Google Scholar
  46. 46.
    Shahadi HI, Jidin R (2011) High capacity and inaudibility audio steganography scheme. in 7th International Conference on Information Assurance and Security (IAS), 2011. IEEE 104–109Google Scholar
  47. 47.
    Shahadi HI, Jidin R, Way WH (2014) Lossless audio steganography based on lifting wavelet transform and dynamic Stego Key. Indian Journal of Science and Technology 7(3):323–334Google Scholar
  48. 48.
    Shahadi HI, Jidin R, Way WH (2014) A novel and high capacity audio steganography algorithm based on adaptive data embedding positions. Res J Appl Sci Eng Technol 7(11):2311–2323Google Scholar
  49. 49.
    Sheikhan M, Assadollahi K, Hemmati E (2010) High quality audio steganography by Floating substitution of LSBS in wavelet Domain. World Applied Sciences Journal 10(12):1501–1507Google Scholar
  50. 50.
    Sheikhan M, Asadollahi K, Shahnazi R (2011) Improvement of Embedding Capacity and Quality of DWT-Based Audio Steganography Systems. World Applied Sciences Journal 13(3):507–516Google Scholar
  51. 51.
    Sheltami T, Musaddiq M, Shakshuki E (2016) Data compression techniques in Wireless Sensor Networks. Futur Gener Comput Syst 64:151–162CrossRefGoogle Scholar
  52. 52.
    Sheltami T, Musaddiq M, Shakshuki E (2016) Data compression techniques in Wireless Sensor Networks. Future Generation Computer Systems 64:151–162CrossRefGoogle Scholar
  53. 53.
    Shivdas DA (2014) High Capacity and Inaudibility Audio Steganography Technique. International Journal of Scientific Progress and Research 6(2):61–65Google Scholar
  54. 54.
    Tang M, Hu J, Song W (2014) A high capacity image steganography using multi-layer embedding. Optik 125(15):3972–3976CrossRefGoogle Scholar
  55. 55.
    Tang M et al (2016) An adaptive image steganography using AMBTC compression and interpolation technique. Optik-International Journal for Light and Electron Optics 127(1):471–477CrossRefGoogle Scholar
  56. 56.
    Tsai Y-S, Tsai P (2011) Adaptive data hiding for vector quantization images based on overlapping codeword clustering. Inf Sci 181(15):3188–3198CrossRefGoogle Scholar
  57. 57.
    Tzanetakis G (2009) Music Analysis, Retrieval and Synthesis for Audio Signals (Marsyas). 22–11-2015; Available from: Accessed 22 Nov 2015
  58. 58.
    Tzanetakis G, Cook P (2002) Musical genre classification of audio signals. IEEE Transactions on speech and audio processing 10(5):293–302CrossRefGoogle Scholar
  59. 59.
    Valarmathi M, Sobia M, Devi RB (2015) Iteration-Free Fractal Image Compression Using Pearson’s Correlation Coefficient-Based Classification. In: Informatics and Communication Technologies for Societal Development. Springer, pp. 157–166Google Scholar
  60. 60.
    Wu Q et al (2016) A magic cube based information hiding scheme of large payload. Journal of Information Security and Applications 26:1–7CrossRefGoogle Scholar
  61. 61.
    Yu L et al (2010) Improved adaptive LSB steganography based on chaos and genetic algorithm. EURASIP Journal on Advances in Signal Processing 2010(1):1Google Scholar
  62. 62.
    Zaidan B et al (2016) A new digital watermarking evaluation and benchmarking methodology using an external group of evaluators and multi-criteria analysis based on ‘large-scale data’. Software: Practice and Experience 47(10):1365–1392Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Universiti Kebangsaan MalaysiaBangiMalaysia
  2. 2.University of BaghdadBaghdadIraq
  3. 3.Universiti Pendidikan Sultan IdrisTanjung MalimMalaysia

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