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An optimized image encryption framework with chaos theory and EMO approach

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Abstract

Security of digital data is one of the prime concerns of advanced data communication technologies. Images possess a major share of the overall data communication. Image data contains several sensitive pieces of information that are to be protected during transmission, storage, and other stages. There are several algorithms that exist in the literature that addresses this issue. The ever-increasing need for image communication demands sophisticated and robust image encryption approaches that can be effectively applied in real-life scenarios. In this work, a local binary pattern is used to produce the binary image, and the electromagnetism-like optimization approach is used to maximize the transition of 0 and 1 and generate the shuffled image. The shuffled image is decomposed into the constituting bitplanes using any one of three bitplane decomposition techniques. Chaos theory is used to generate some synthetic bitplanes for substitution purposes where the electromagnetism-like optimization process optimizes the uniformity in the histogram. This approach is further secured using a final layer scrambling approach. The proposed approach is tested using both qualitative and quantitative metrics. Moreover, the proposed approach is tested against different types of attacks that prove the practical applicability and robustness of the proposed approach.

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References

  1. Agaian S, Astola J, Egiazarian K, Kuosmanen P (1995) Decompositional methods for stack filtering using Fibonacci p-codes. Signal Process 41:101–110. https://doi.org/10.1016/0165-1684(94)00093-F

    Article  Google Scholar 

  2. Amari S (1993) Backpropagation and stochastic gradient descent method. Neurocomputing 5:185–196. https://doi.org/10.1016/0925-2312(93)90006-O

    Article  MATH  Google Scholar 

  3. Auli-Llinas F, Marcellin MW (2012) Scanning order strategies for bitplane image coding. IEEE Trans Image Process 21:1920–1933. https://doi.org/10.1109/TIP.2011.2176953

    Article  MathSciNet  MATH  Google Scholar 

  4. Bao L, Zhou Y (2015) Image encryption: generating visually meaningful encrypted images. Inf Sci (Ny) 324:197–207. https://doi.org/10.1016/J.INS.2015.06.049

    Article  MathSciNet  MATH  Google Scholar 

  5. Birbil ŞI, Fang SC (2003) An electromagnetism-like mechanism for global optimization. J Glob Optim 25:263–282. https://doi.org/10.1023/A:1022452626305

    Article  MathSciNet  MATH  Google Scholar 

  6. Chakraborty S, Chatterjee S, Dey N et al (2017) Modified cuckoo search algorithm in microscopic image segmentation of hippocampus. Microsc Res Tech 1–22. https://doi.org/10.1002/jemt.22900

  7. Chakraborty S, Mali K (2020) SuFMoFPA: a superpixel and meta-heuristic based fuzzy image segmentation approach to explicate COVID-19 radiological images. Expert Syst Appl 114142. https://doi.org/10.1016/j.eswa.2020.114142

  8. Chakraborty S, Mali K (2020) Fuzzy Electromagnetism Optimization (FEMO) and its application in biomedical image segmentation. Appl Soft Comput J 97. https://doi.org/10.1016/j.asoc.2020.106800

  9. Chakraborty S, Seal A, Roy M, Mali K (2016) A novel lossless image encryption method using DNA substitution and chaotic logistic map. Int J Secur Appl 10:205–216. https://doi.org/10.14257/ijsia.2016.10.2.19

    Article  Google Scholar 

  10. Computer Security Division N FIPS 46–3, Data Encryption Standard (DES) (withdrawn May 19, 2005)

  11. Cui G, Qin L, Wang Y, Zhang X (2008) An encryption scheme using DNA technology. In: 2008 3rd International Conference on Bio-Inspired Computing: Theories and Applications. IEEE, pp 37–42

  12. CVG - UGR - Image database. http://decsai.ugr.es/cvg/dbimagenes/g512.php. Accessed 15 Aug 2019

  13. Daemen J (AeS1) VR-F candidate conference, 1999 undefined The Rijndael block cipher: AES proposal. cs.technion.ac.il

  14. Gálvez J, Cuevas E, Avalos O et al (2018) Electromagnetism-like mechanism with collective animal behavior for multimodal optimization. Appl Intell 48:2580–2612. https://doi.org/10.1007/s10489-017-1090-1

    Article  Google Scholar 

  15. Gehani A, LaBean T, Reif J (2003) DNA-based cryptography. Springer, Berlin, pp 167–188

    MATH  Google Scholar 

  16. Gehani A, LaBean TH, Reif JH DNA-based cryptography. DIMACS series in discrete mathematics. Theor Comput Sci 54:233–249

  17. Gevorkian DZ, Egiazarian KO, Agaian SS et al (1995) Parallel algorithms and VLSI architectures for stack filtering using Fibonacci p-codes. IEEE Trans Signal Process 43:286–295. https://doi.org/10.1109/78.365308

    Article  Google Scholar 

  18. Gonzalez RC, Woods RE (Richard E (2008) Digital image processing. Prentice Hall

  19. Grangetto M, Magli E, Olmo G (2006) Multimedia selective encryption by means of randomized arithmetic coding. IEEE Trans Multimed 8:905–917. https://doi.org/10.1109/TMM.2006.879919

    Article  Google Scholar 

  20. Gupta S, Sarkar J, Kundu M et al (2020) Automatic recognition of SEM microstructure and phases of steel using LBP and random decision forest operator. Meas J Int Meas Confed 151:107224. https://doi.org/10.1016/j.measurement.2019.107224

    Article  Google Scholar 

  21. Horé A, Ziou D (2010) Image quality metrics: PSNR vs. SSIM. Proc - Int Conf Pattern Recognit 2366–2369. https://doi.org/10.1109/ICPR.2010.579

  22. Hu H, Liu L, Ding N (2013) Pseudorandom sequence generator based on the Chen chaotic system. Comput Phys Commun 184:765–768. https://doi.org/10.1016/j.cpc.2012.11.017

    Article  MathSciNet  Google Scholar 

  23. Hua Z, Zhou Y, Huang H (2019) Cosine-transform-based chaotic system for image encryption. Inf Sci (Ny) 480:403–419. https://doi.org/10.1016/J.INS.2018.12.048

    Article  Google Scholar 

  24. Hua Z, Zhou Y, Pun C-M, Chen CLP (2015) 2D sine logistic modulation map for image encryption. Inf Sci (Ny) 297:80–94. https://doi.org/10.1016/J.INS.2014.11.018

    Article  Google Scholar 

  25. Hua Z, Zhu Z, Yi S et al (2021) Cross-plane colour image encryption using a two-dimensional logistic tent modular map. Inf Sci (Ny) 546:1063–1083. https://doi.org/10.1016/j.ins.2020.09.032

    Article  MathSciNet  Google Scholar 

  26. Kamal FM, Elsonbaty A, Elsaid A (2021) A novel fractional nonautonomous chaotic circuit model and its application to image encryption. Chaos Solit Fractals 144:110686. https://doi.org/10.1016/j.chaos.2021.110686

    Article  MathSciNet  Google Scholar 

  27. Kamali SH, Hedayati M, Shakerian R, Rahmani M (2010) A new modified version of Advanced Encryption Standard based algorithm for image encryption. In: ICEIE 2010–2010 International Conference on Electronics and Information Engineering, Proceedings

  28. Ko S-J, Lee S-H, Jeon S-W, Kang E-S (1999) Fast digital image stabilizer based on gray-coded bit-plane matching. IEEE Trans Consum Electron 45:598–603. https://doi.org/10.1109/30.793546

    Article  Google Scholar 

  29. Kumar M, Saxena A, Vuppala SS (2020) A survey on chaos based image encryption techniques. In: Studies in Computational Intelligence. Springer, pp 1–26

  30. Kumar S, Singh BK, Akshita et al (2020) A survey on symmetric and asymmetric key based image encryption. In: 2nd International Conference on Data, Engineering and Applications, IDEA 2020. Institute of Electrical and Electronics Engineers Inc.

  31. Leong MP, Cheung OYH, Tsoi KH, Leong PHW A bit-serial implementation of the international data encryption algorithm IDEA. In: Proceedings 2000 IEEE Symposium on Field-Programmable Custom Computing Machines (Cat. No.PR00871). IEEE Comput Soc pp 122–131

  32. Li S, Chen G, Zheng X (2019) Chaos-Based Encryption for Digital Images and Videos. In: Multimedia Security Handbook. CRC Press, pp 133–167

  33. Li P, Lo K (2020) Survey on JPEG compatible joint image compression and encryption algorithms. IET Signal Process 14:475–488. https://doi.org/10.1049/iet-spr.2019.0276

    Article  Google Scholar 

  34. Li Y, Yu H, Song B, Chen J (2021) Image encryption based on a single-round dictionary and chaotic sequences in cloud computing. Concurr Comput Pract Exp 33:1–1. https://doi.org/10.1002/cpe.5182

    Article  Google Scholar 

  35. Liao X, Li K, Zhu X, Liu KJR (2020) Robust detection of image operator chain with two-stream convolutional neural network. IEEE J Sel Top Signal Process 14:955–968. https://doi.org/10.1109/JSTSP.2020.3002391

    Article  Google Scholar 

  36. Lindholm FA, Fossum JG, Burgess EL (1979) Application of the superposition principle to solar-cell analysis. IEEE Trans Electron Devices 26:165–171. https://doi.org/10.1109/T-ED.1979.19400

    Article  Google Scholar 

  37. Mali K, Chakraborty S, Roy M (2015) A study on statistical analysis and security evaluation parameters in image encryption. IJSRD-Int J Sci Res Dev 3:2321–0613

    Google Scholar 

  38. Mali K, Chakraborty S, Seal A, Roy M (2015) An efficient image cryptographic algorithm based on frequency domain using haar wavelet transform. Int J Secur Its Appl 9:279–288. https://doi.org/10.14257/ijsia.2015.9.12.26

    Article  Google Scholar 

  39. Malik A, Gupta S, Dhall S (2020) Analysis of traditional and modern image encryption algorithms under realistic ambience. Multimed Tools Appl 79:27941–27993. https://doi.org/10.1007/s11042-020-09279-6

    Article  Google Scholar 

  40. Mozaffari S (2018) Parallel image encryption with bitplane decomposition and genetic algorithm. Multimed Tools Appl 77:25799–25819. https://doi.org/10.1007/s11042-018-5817-8

    Article  Google Scholar 

  41. Niyishaka P, Bhagvati C (2020) Image splicing detection technique based on Illumination-Reflectance model and LBP. Multimed Tools Appl 1–15. https://doi.org/10.1007/s11042-020-09707-7

  42. Pareek N, Patidar V, Sud K (2003) Discrete chaotic cryptography using external key. Phys Lett A 309:75–82. https://doi.org/10.1016/S0375-9601(03)00122-1

    Article  MathSciNet  MATH  Google Scholar 

  43. Patidar V, Sud K, Informatica NP (2009) undefined A pseudo random bit generator based on chaotic logistic map and its statistical testing. informatica.si

  44. Ramanathan TT, Hossen J, Sayeed S, Emerson Raja J (2020) Survey on computational intelligence based image encryption techniques. Indones J Electr Eng Comput Sci 19:1428–1435. https://doi.org/10.11591/ijeecs.v19.i3.pp1428-1435

    Article  Google Scholar 

  45. Roy M, Chakraborty S, Mali K (2020) A robust image encryption method using chaotic skew-tent map. In: Chakraborty S, Mali K (eds) Applications of advanced machine intelligence in computer vision and object recognition: emerging research and opportunities

  46. Roy M, Chakraborty S, Mali K (2021) The MSK: a simple and robust image encryption method. Multimed Tools Appl 1–31. https://doi.org/10.1007/s11042-021-10761-y

  47. Roy M, Chakraborty S, Mali K (2021) A chaotic framework and its application in image encryption. Multimed Tools Appl 1–42. https://doi.org/10.1007/s11042-021-10839-7

  48. Roy M, Chakraborty S, Mali K, Roy D (2021) Utilization of hyperchaotic environment and DNA sequences for digital image security. Springer, Singapore, pp 289–297

    Google Scholar 

  49. Roy M, Chakraborty S, Mali K et al (2019) A dual layer image encryption using polymerase chain reaction amplification and dna encryption. In: 2019 International Conference on Opto-Electronics and Applied Optics, Optronix 2019. Institute of Electrical and Electronics Engineers Inc.

  50. Roy M, Chakraborty S, Mali K et al (2020) Biomedical image security using matrix manipulation and DNA encryption

  51. Roy M, Chakraborty S, Mali K et al (2020) Data security techniques based on DNA encryption

  52. Roy M, Chakraborty S, Mali K et al (2021) An image security method based on low dimensional chaotic environment and DNA encoding. Springer, Singapore, pp 267–277

    Google Scholar 

  53. Roy M, Chakraborty S, Mali K et al (2021) A robust image encryption framework based on DNA computing and chaotic environment. Microsyst Technol 1–11. https://doi.org/10.1007/s00542-020-05120-0

  54. Roy M, Mali K, Chatterjee S et al (2019) A Study on the Applications of the Biomedical Image Encryption Methods for Secured Computer Aided Diagnostics. In: 2019 Amity International Conference on Artificial Intelligence (AICAI). IEEE, pp 881–886

  55. Seal A, Chakraborty S, Mali K (2017) A new and resilient image encryption technique based on pixel manipulation, value transformation and visual transformation utilizing single–Level haar wavelet transform

  56. Serelkhetm S, Heshmat S (2020) A Survey study on Joint Image Compression-Encryption Methods. In: Proceedings of 2020 International Conference on Innovative Trends in Communication and Computer Engineering, ITCE 2020. Institute of Electrical and Electronics Engineers Inc., pp 222–226

  57. Shujun L, Xuanqin M, Yuanlong C (2001) Pseudo-random bit generator based on couple chaotic systems and its applications in stream-cipher cryptography. Springer, Berlin, pp 316–329

    MATH  Google Scholar 

  58. Sun F, Lu Z, Liu S (2010) A new cryptosystem based on spatial chaotic system. Opt Commun 283:2066–2073. https://doi.org/10.1016/j.optcom.2010.01.028

    Article  Google Scholar 

  59. Talhaoui MZ, Wang X (2021) A new fractional one dimensional chaotic map and its application in high-speed image encryption. Inf Sci (Ny) 550:13–26. https://doi.org/10.1016/j.ins.2020.10.048

    Article  MathSciNet  MATH  Google Scholar 

  60. Tamang J, De Dieu Nkapkop J, Ijaz MF et al (2021) Dynamical properties of ion-acoustic waves in space plasma and its application to image encryption. IEEE Access 9:18762–18782. https://doi.org/10.1109/ACCESS.2021.3054250

    Article  Google Scholar 

  61. Tsamardinos I, Brown LE, Aliferis CF (2006) The max-min hill-climbing Bayesian network structure learning algorithm. Mach Learn 65:31–78. https://doi.org/10.1007/s10994-006-6889-7

    Article  MATH  Google Scholar 

  62. Wadi SM, Zainal N (2014) High definition image encryption algorithm based on AES modification. Wirel Pers Commun 79:811–829. https://doi.org/10.1007/s11277-014-1888-7

    Article  Google Scholar 

  63. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13:600–612. https://doi.org/10.1109/TIP.2003.819861

    Article  Google Scholar 

  64. Wang X, Liu L, Zhang Y (2015) A novel chaotic block image encryption algorithm based on dynamic random growth technique. Opt Lasers Eng 66:10–18. https://doi.org/10.1016/J.OPTLASENG.2014.08.005

    Article  Google Scholar 

  65. Wang Y, See J, Phan RC-W, Oh Y-H (2015) Efficient spatio-temporal local binary patterns for spontaneous facial micro-expression recognition. PLoS One 10:e0124674. https://doi.org/10.1371/journal.pone.0124674

    Article  Google Scholar 

  66. Wang X, Zhang Q (2009) DNA computing-based cryptography. In: 2009 Fourth International on Conference on Bio-Inspired Computing. IEEE, pp 1–3

  67. Wang B, Zhang BF, Liu XW (2021) An image encryption approach on the basis of a time delay chaotic system. Optik (Stuttg) 225:165737. https://doi.org/10.1016/j.ijleo.2020.165737

    Article  Google Scholar 

  68. Wu X, Wang D, Kurths J, Kan H (2016) A novel lossless color image encryption scheme using 2D DWT and 6D hyperchaotic system. Inf Sci (Ny) 349–350:137–153. https://doi.org/10.1016/J.INS.2016.02.041

    Article  Google Scholar 

  69. Wu Y, Zhang L, Qian T et al (2021) Content-adaptive image encryption with partial unwinding decomposition. Signal Process 181:107911. https://doi.org/10.1016/j.sigpro.2020.107911

    Article  Google Scholar 

  70. Wu Y, Zhou Y, Noonan JP, Agaian S (2014) Design of image cipher using latin squares. Inf Sci (Ny) 264:317–339. https://doi.org/10.1016/J.INS.2013.11.027

    Article  MathSciNet  MATH  Google Scholar 

  71. Xian Y, Wang X (2021) Fractal sorting matrix and its application on chaotic image encryption. Inf Sci (Ny) 547:1154–1169. https://doi.org/10.1016/j.ins.2020.09.055

    Article  MathSciNet  MATH  Google Scholar 

  72. Xu M, Tian Z (2019) A novel image cipher based on 3D bit matrix and latin cubes. Inf Sci (Ny) 478:1–14. https://doi.org/10.1016/J.INS.2018.11.010

    Article  Google Scholar 

  73. Ye R (2011) A novel chaos-based image encryption scheme with an efficient permutation-diffusion mechanism. Opt Commun 284:5290–5298. https://doi.org/10.1016/j.optcom.2011.07.070

    Article  Google Scholar 

  74. Zahmoul R, Ejbali R, Zaied M (2017) Image encryption based on new Beta chaotic maps. Opt Lasers Eng 96:39–49. https://doi.org/10.1016/J.OPTLASENG.2017.04.009

    Article  Google Scholar 

  75. Zhang Y (2018) The unified image encryption algorithm based on chaos and cubic S-box. Inf Sci (Ny) 450:361–377. https://doi.org/10.1016/J.INS.2018.03.055

    Article  MathSciNet  MATH  Google Scholar 

  76. Zhang W, Yu H, Zhao Y, Zhu Z (2016) Image encryption based on three-dimensional bit matrix permutation. Signal Process 118:36–50. https://doi.org/10.1016/J.SIGPRO.2015.06.008

    Article  Google Scholar 

  77. Zhou Y, Cao W, Philip Chen CL (2014) Image encryption using binary bitplane. Signal Process 100:197–207. https://doi.org/10.1016/J.SIGPRO.2014.01.020

    Article  Google Scholar 

  78. Zhou Y, Panetta K, Agaian S, Chen CLP (2012) Image encryption using P-Fibonacci transform and decomposition. Opt Commun 285:594–608. https://doi.org/10.1016/J.OPTCOM.2011.11.044

    Article  Google Scholar 

  79. Zhou Y, Panetta K, Agaian S, Chen CLP (2013) (n, k, p)-gray code for image systems. IEEE Trans Cybern 43:515–529. https://doi.org/10.1109/TSMCB.2012.2210706

    Article  Google Scholar 

  80. Zhu J, Kaplan R, Johnson J, Fei-Fei L (2018) HiDDeN: Hiding Data With Deep Networks

  81. Zhu Z, Zhang W, Wong K, Yu H (2011) A chaos-based symmetric image encryption scheme using a bit-level permutation. Inf Sci (Ny) 181:1171–1186. https://doi.org/10.1016/J.INS.2010.11.009

    Article  Google Scholar 

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  1. Department of Computer Science & Engineering, University of Kalyani, Kalyani, India

    Mousomi Roy, Shouvik Chakraborty & Kalyani Mali

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Appendix

Appendix

Table 7 Advantages and real-time uses of the mentioned algorithms
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Table 8 NIST test results for the PRBG
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Roy, M., Chakraborty, S. & Mali, K. An optimized image encryption framework with chaos theory and EMO approach. Multimed Tools Appl 82, 30309–30343 (2023). https://doi.org/10.1007/s11042-023-14438-6

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