Skip to main content
SpringerLink
Log in

Audio scrambling technique based on cellular automata

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Scrambling is a process that has proved to be very effective in increasing the quality of data hiding, watermarking, and encryption applications. Cellular automata are used in diverse and numerous applications because of their ability to obtain complex global behavior from simple and localized rules. In this paper we apply cellular automata in the field of audio scrambling because of the potential it holds in breaking the correlation between audio samples effectively. We also analyze the effect of using different cellular automata types on audio scrambling and we test different cellular automata rules with different Lambda values. The scrambling degree is measured and the relation between the robustness and the scrambling degree obtained is studied. Experimental results show that the proposed technique is robust to data loss attack where 1/3 of the data is lost and that the algorithm can be applied to music and speech files of different sizes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Abu Dalhoum A, Mahafzah B, Awwad A, Al-Dhamari I, Ortega A, Alfonseca M (2011) Digital image scrambling method based on two dimensional cellular automata: a test of the lambda value. In: IEEE multimedia. doi:10.1109/MMUL.2011.54

  2. Aleksić Z (2000) Artificial life: growing complex systems. In: Bossomaier T, Green D (eds) Complex systems. Cambridge University Press, pp 91–126

  3. Aponte A, Moreno J (2006) Cellular automata and its application to the modeling of vehicular traffic in the city of Caracas. In: El Yacoubi S, Chopard B, Bandini S (eds) Cellular automata. Springer, Lect Notes Comput Sci 4173:502–511

  4. Chang FC, Huang HC, Hang HM (2007) Layered access control schemes on watermarked scalable media. J VLSI Signal Process Syst 49(3):443–455

    Article  Google Scholar 

  5. Chen G, Hu Q (2010) An audio scrambling method based on combination strategy. In: Proc. international conference on computer science and information technology (ICCSIT), Chengdu, China, pp 62–66

  6. Fu W, Yan W, Kankanhalli MS (2005) Progressive scrambling for MP3 audio. In: Proc. IEEE international symposium on circuits and systems (ISCAS), vol 6. Kobe, Japan, pp 5525–5528

  7. Gardner M (1970) Mathematical games—the fantastic combinations of John Conway’s new solitaire game “life”. Sci Am 223:120–123

    Article  Google Scholar 

  8. Huang HC, Chen YH (2009) Genetic fingerprinting for copyright protection of multicast media. Soft Comput 13(4):383–391

    Article  Google Scholar 

  9. Jiping N, Yongchuan Z, Zhihua H, Zuqiao Y (2008) A digital image scrambling method based on AES and error correcting code. In: Proc. international conference on computer xcience and software engineering, Wuhan, Hubei, China, pp 677–680

  10. Kier L, Witten T (2005) Cellular automata models of complex biochemical systems. In: Bonchev D, Rouvray D (eds) Complexity in chemistry, biology, and ecology. Springer, pp 237–301

  11. Langton C (1990) Computation at the edge of chaos: phase transitions and emergent computation. Physica D 42(1–3):12–37

    Article  MathSciNet  Google Scholar 

  12. Li H, Qin Z (2009) Audio scrambling algorithm based on variable dimension Space. In: Proc. international conference on industrial and information systems, Haikou, China, pp 316–319

  13. Li H, Qin Z, Shao L (2009) Audio watermarking pre-process algorithm. In: Proc. IEEE international conference on e-business engineering, Macau, China, pp 165–170

  14. Li H, Qin Z, Shao L, Zhang S, Wang B (2009) Variable dimension space audio scrambling algorithm against MP3 compression. In: Hua A, Chang S (eds) Algorithms and architectures for parallel processing. Springer, Lect Notes Comput Sci 5574:866–876

  15. Martínez-Noriega R, Nakano M, Kurkoski B, Yamaguchi K (2011) High payload audio watermarking: toward channel characterization of MP3 compression. Journal of Information Hiding and Multimedia Signal Processing (JIHMSP) 2(2):91–107

    Google Scholar 

  16. Nishio H (2006) How does the neighborhood affect the global behavior of cellular automata. In: El Yacoubi S, Chopard B, Bandini S (eds) Cellular automata. Springer, Lect Notes Comput Sci 4173:122–130

  17. Sarkar P (2000) A Brief History of Cellular Automata. ACM Comput Surv (CSUR) 32(1):80–107

    Article  Google Scholar 

  18. Shang Z, Ren H, Zhang J (2008) A block location scrambling algorithm of digital image based on Arnold transformation. In: Proc. 9th international conference for young computer scientists, Hunan, China, pp 2942–2947

  19. Shin S, Yoo K (2009) Analysis of 2-state, 3-neighborhood cellular automata rules for cryptographic pseudorandom number generation. In: Proc. international conference on computational science and engineering (CSE’09), Vancouver, BC, Canada, pp 399–404

  20. Wolfram S (2002) A new kind of science. Wolfram Media, USA

    MATH  Google Scholar 

  21. Xiangdong L, Junxing Z, Jinhai Z, Xiqin H (2008) A new chaotic image scrambling algorithm based on dynamic twice interval-division. In: Proc. international conference on computer science and software engineering, Wuhan, Hubei, China, pp 818–821

  22. Yan W, Fu W, Kankanhalli MS (2008) Progressive audio scrambling in compressed domain. IEEE Trans Multimedia 10(6):960–968

    Article  Google Scholar 

  23. Ye R, Li H (2008) A novel image scrambling and watermarking scheme based on cellular automata. In: Proc. international symposium on electronic commerce and security, Guangzhou City, China, pp 938–941

  24. Zhu L, Li W, Liao L, Li H (2006) A novel algorithm for scrambling digital image based on cat chaotic mapping. In: Proc. international conference on intelligent information hiding and multimedia signal processing (IIH-MSP’06), Pasadena, CA, USA, pp 601–604

Download references

Acknowledgements

This work is partially supported by the Spanish Ministry of Science and Innovation under coordinated research projects TIN2011-28260-C03-00 and TIN2011-28260-C03-02 and by the Comunidad Autónoma de Madrid under research project e-madrid S2009/TIC-1650

Author information

Authors and Affiliations

  1. University of Jordan, Amman, 11942, Jordan

    Alia Madain, Abdel Latif Abu Dalhoum & Hazem Hiary

  2. Universidad Autónoma de Madrid, Madrid, Spain

    Alfonso Ortega & Manuel Alfonseca

Authors
  1. Alia Madain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdel Latif Abu Dalhoum
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hazem Hiary
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alfonso Ortega
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Manuel Alfonseca
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hazem Hiary.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Madain, A., Abu Dalhoum, A.L., Hiary, H. et al. Audio scrambling technique based on cellular automata. Multimed Tools Appl 71, 1803–1822 (2014). https://doi.org/10.1007/s11042-012-1306-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-012-1306-7

Keywords

Search

Navigation