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Digital image scrambling based on elementary cellular automata

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Abstract

Image scrambling is the process of converting an image to an unintelligible format, mainly for security reasons. The scrambling is considered as a pre-process or a post-process of security related applications such as watermarking, information hiding, fingerprinting, and encryption. Cellular automata are parallel models of computation that prove an interesting concept where a simple configuration can lead to a complex behavior. Since there are a lot of parameters to configure, cellular automata have many types and these types differ in terms of complexity and behavior. Cellular automata were previously used in scrambling different types of multimedia, but only complex two-dimensional automata were explored. We propose a scheme where the simplest type of cellular automata is used that is the elementary type. We test the scrambling degree for different cellular automata rules that belong to classes three and four of Wolfram’s classification which correspond to complex and chaotic behavior; we also check the effect of other parameters such as the number of generations and the boundary condition. Experimental results show that our proposed scheme outperforms other schemes based on cellular automata in terms of scrambling degree.

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References

  1. Abu Dalhoum A, Mahafzah B, Ayyal Awwad A, Aldhamari I, Ortega A, Alfonseca M (2012) Digital image scrambling using 2D cellular automata. IEEE MultiMedia 19(4):28–36

  2. Augustine N., George S., Deepthi P. (2014) Sparse representation based audio scrambling using cellular automata. In: IEEE International Conference on Electronics, Computing and Communication Technologies (IEEE CONECCT). IEEE, Bangalore, pp 1–5

  3. Desheng X, Yueshan X (2005) Digital image scrambling based on josephus traversing. Computer Eng and Applications 10:44–46

    Google Scholar 

  4. George SN, Augustine N, Pattathil DP (2014) Audio security through compressive sampling and cellular automata. Multimedia Tools and Applications:1–25

  5. Jiping N, Yongchuan Z, Zhihua H, Zuqiao Y (2008) A digital image scrambling method based on AES and error-correcting code. In: International Conference on Computer Science and Software Engineering (CSSE ’08), vol 3. IEEE, Wuhan, pp 677–680

  6. Li H, Qin Z (2009) Audio scrambling algorithm based on variable dimension space. In: International Conference on Industrial and Information Systems (IIS ’09). IEEE, Haikou, pp 316–319

  7. Li H, Qin Z, Shao L (2009) Audio watermarking pre-process algorithm. In: IEEE International Conference on e-Business Engineering (ICEBE ’09). IEEE, Macau, pp 165–170

  8. 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 L (eds) Algorithms and Architectures for Parallel Processing, Lecture Notes in Computer Science, vol 5574. Springer, Berlin, pp 866–876

  9. Li H, Wang Y, Yan H, Li L, Li Q, Zhao X (2013) Double-image encryption by using chaos-based local pixel scrambling technique and gyrator transform. Opt Lasers Eng 51(12):1327–1331

  10. Li XW, Cho SJ, Kim ST (2014) A 3D image encryption technique using computer-generated integral imaging and cellular automata transform. Optik - International Journal for Light and Electron Optics 125(13):2983–2990

  11. Liu S, Sheridan JT (2013) Optical encryption by combining image scrambling techniques in fractional fourier domains. Opt Commun 287:73–80

    Article  Google Scholar 

  12. Liu Z, Gong M, Dou Y, Liu F, Lin S, Ahmad MA, Dai J, Liu S (2012) Double image encryption by using arnold transform and discrete fractional angular transform. Opt Lasers Eng 50(2):248–255

    Article  Google Scholar 

  13. Liu Z, Zhang Y, Liu W, Meng F, Wu Q, Liu S (2013) A mixed scrambling operation for hiding image. Optik - International Journal for Light and Electron Optics 124(22):5391–5396

  14. Madain A, Abu Dalhoum A, Hiary H, Ortega A, Alfonseca M (2014) Audio scrambling technique based on cellular automata. Multimed Tools Appl 71(3):1803–1822

  15. Maleki F, Mohades A, Hashemi S, Shiri M (2008) An image encryption system by cellular automata with memory. In: Third International Conference on Availability, Reliability and Security (ARES ’08). IEEE, Barcelona, pp 1266–1271

  16. Parah SA, Sheikh JA, Hafiz AM, Bhat G (2014) Data hiding in scrambled images: A new double layer security data hiding technique. Comput Electr Eng 40(1):70–82

    Article  Google Scholar 

  17. Shang Z, Ren H, Zhang J (2008) A block location scrambling algorithm of digital image based on arnold transformation. In: The 9th International Conference for Young Computer Scientists (ICYCS ’08). IEEE, Hunan, pp 2942–2947

  18. Shin SH, Yoo KY (2009) Analysis of 2-state, 3-neighborhood cellular automata rules for cryptographic pseudorandom number generation. In: International Conference on Computational Science and Engineering (CSE ’09), vol 1. IEEE, Vancouver, pp 399–404

  19. (2015) The University of Waterloo–Image repository: http://links.uwaterloo.ca/Repository.html. Accessed 12 Oct

  20. (2015) USC-SIPI Image Database: http://sipi.usc.edu/database/. Accessed 12 Oct

  21. Wang T, Li H (2014) A novel scrambling digital image watermarking algorithm based on contourlet transform. Wuhan University Journal of Natural Sciences 19(4):315–322

  22. Wolfram S (2002) A New Kind of Science. Wolfram Media

  23. Wu J, Guo F, Liang Y, Zhou N (2014) Triple color images encryption algorithm based on scrambling and the reality-preserving fractional discrete cosine transform. Optik - International Journal for Light and Electron Optics 125(16):4474–4479

  24. Xiangdong L, Junxing Z, Jinhai Z, Xiqin H (2008) A new chaotic image scrambling algorithm based on dynamic twice interval-division. In: International Conference on Computer Science and Software Engineering, vol 3, Wuhan, pp 818–821

  25. Yan W, Weir J (2010) Fundamentals of Media Security. Ventus Publishing Aps

  26. Ye G, Huang X, Zhu C (2007) Image encryption algorithm of double scrambling based on ascii code of matrix element. In: International Conference on Computational Intelligence and Security. IEEE, Harbin, pp 843–847

  27. Ye R, Li H (2008) A novel image scrambling and watermarking scheme based on cellular automata. In: International Symposium on Electronic Commerce and Security. IEEE, Guangzhou City, pp 938–941

  28. Zefreh E, Rajaee S, Farivary M (2011) Image security system using recursive cellular automata substitution and its parallelization. In: CSI International Symposium on Computer Science and Software Engineering (CSSE). IEEE, Tehran, pp 77–86

  29. Zhang L, Ji S, Xie Y, Yuan Q, Wan Y, Bao G (2005) Principle of image encrypting algorithm based on magic cube transformation. In: Hao Y, Li J, Wang YP, Cheung YM, Yin H, Jiao L, Ma J, Jiao YC (eds) Computational Intelligence and Security, Lecture Notes in Computer Science, vol 3802. Springer, Berlin, pp 977–982

  30. Zhong Z, Chang J, Shan M, Hao B (2012) Double image encryption using double pixel scrambling and random phase encoding. Optics Communications 285(5):584–588

  31. Zhou Y, Bao L, Chen CP (2014) A new 1D chaotic system for image encryption. Signal Process 97:172–182

    Article  Google Scholar 

  32. Zhou Y, Panetta K, Agaian S (2008) An image scrambling algorithm using parameter bases M-sequences. In: International Conference on Machine Learning and Cybernetics, vol 7. IEEE, Kunming, pp 3695–3698

  33. Zhu L, Li W, Liao L, Li H (2006) A novel algorithm for scrambling digital image based on cat chaotic mapping. In: International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP ’06). IEEE, Pasadena, pp 601–604

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Authors and Affiliations

  1. The University of Jordan, Amman, 11942, Jordan

    Abdel Latif Abu Dalhoum, Alia Madain & Hazem Hiary

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  1. Abdel Latif Abu Dalhoum
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  2. Alia Madain
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  3. Hazem Hiary
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Correspondence to Hazem Hiary.

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Abu Dalhoum, A.L., Madain, A. & Hiary, H. Digital image scrambling based on elementary cellular automata. Multimed Tools Appl 75, 17019–17034 (2016). https://doi.org/10.1007/s11042-015-2972-z

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  • DOI: https://doi.org/10.1007/s11042-015-2972-z

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