GSAPSO-MQC:medical image encryption based on genetic simulated annealing particle swarm optimization and modified quantum chaos system

Abstract

Due to the large amount of image information data, high redundancy and high pixel correlation, the traditional medical image encryption algorithm is easy to be attacked by chosen plaintext. Therefore, a new medical image encryption algorithm combining genetic simulated annealing particle swarm optimization and modified quantum chaos system is proposed to obtain better security performance. Firstly, an improved quantum chaotic system is used to generate the key stream. Then the selection and cross operation of genetic algorithm are used to process the plaintext image. The optimal sequence generated by simulated annealing algorithm is used to scramble the image. Meanwhile, the particle swarm optimization (PSO) algorithm is introduced into the simulated annealing mechanism. The initial temperature is set according to the optimal fitness value of the initial population. Metropolis is used to optimize the generation of individual optimal position and global optimal position, and the inertial weight parameters of PSO algorithm are optimized to avoid particles falling into local optimal in the optimization process and improve the convergence speed of the algorithm. Through these three operations, the histogram of the scrambled image can be equalized to resist statistical attack. Experimental results and performance analysis show that the encryption system proposed in this paper can resist many typical attacks such as histogram analysis, correlation analysis, differential attack and violent attack, and has high security and encryption efficiency. Compared with other encryption methods, the encryption efficiency of our proposed method has improved by approximately 10%.

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References

  1. 1.

    Yin S, Liu J (2016) A K-means approach for map-reduce model and social network privacy protection. J Inf Hiding Multimed Signal Process 7(6):1215–1221

    Google Scholar 

  2. 2.

    Sun Y, Yin S, Liu J, Teng L (2019) A certificateless group authenticated key agreement protocol based on dynamic binary tree. Int J Netw Secur 21(5):843–849

    Google Scholar 

  3. 3.

    Moschos A, Papadimitriou G, Nicopolitidis P (2018) Proactive encryption of personal area networks and small office-home office networks under advanced encryption standard application. Secur Privacy 1(1):e10

    Google Scholar 

  4. 4.

    Thakur A, Singh H, Sharda S (2015) Secure video steganography based on discrete wavelet transform and Arnold transform. Int J Comput Appl 123(11):25–29

    Google Scholar 

  5. 5.

    Teng L, Li H, Yin S, Sun Y (2020) A modified advanced encryption standard for data security. Int J Netw Secur 22(1):112–117

    Google Scholar 

  6. 6.

    Liu X, Cao Y, Lu P et al (2013) Optical image encryption technique based on compressed sensing and Arnold transformation. Opt Int J Light Electron Opt 124(24):6590–6593

    Google Scholar 

  7. 7.

    Tu L, Hu J, Zhang C et al (2012) Image encryption algorithm based on arnold transformation and logistic mapping. Adv Inf Sci Serv Sci 4(23):282–289

    Google Scholar 

  8. 8.

    Liang X, Tan X, Tao L et al (2019) Image hybrid encryption based on matrix nonlinear operation and generalized Arnold transformation. Int J Pattern Recognit Artif Intell 33(6):1954022.1–1954022.17

    Google Scholar 

  9. 9.

    Hussain I, Shah T, Gondal MA et al (2013) A novel image encryption algorithm based on chaotic maps and GF(28) exponent transformation. Nonlinear Dyn 72(1–2):399–406

    Google Scholar 

  10. 10.

    Beheri MH, Amin M, Song X et al (2016) Quantum image encryption based on scrambling–diffusion (SD) approach. In: 2016 2nd International conference on frontiers of signal processing (ICFSP). IEEE

  11. 11.

    Liang Y, Liu G, Zhou N et al (2015) Image encryption combining multiple generating sequences controlled fractional DCT with dependent scrambling and diffusion. J Mod Opt 62(4):251–264

    Google Scholar 

  12. 12.

    Kumar GAS, Bagan KB, Vivekanand V (2011) A Novel algorithm for image encryption by integrated pixel scrambling plus diffusion [IISPD] utilizing duo chaos mapping applicability in wireless systems. Procedia Comput Sci 3:378–387

    Google Scholar 

  13. 13.

    Hua Z, Yi S, Zhou Y (2018) Medical image encryption using high-speed scrambling and pixel adaptive diffusion. Signal Process 144:134–144

    Google Scholar 

  14. 14.

    Saleem A, Noor A (2015) Analysis of S-box image encryption based on generalized fuzzy soft expert set. Nonlinear Dyn 79(3):1679–1692

    Google Scholar 

  15. 15.

    Fan H, Li M, Liu D et al (2018) Cryptanalysis of a colour image encryption using chaotic APFM nonlinear adaptive filter. Signal Processing 143:28–41

    Google Scholar 

  16. 16.

    Yin S, Li H, Teng L (2019) A novel proxy re-encryption scheme based on identity property and stateless broadcast encryption under cloud environment. Int J Netw Secur 21(5):797–803

    Google Scholar 

  17. 17.

    Yin S, Bi J (2019) Medical image annotation based on deep transfer learning. J Appl Sci Eng 22(2):385–390

    Google Scholar 

  18. 18.

    Huang H, Yang S (2017) Colour image encryption based on logistic mapping and double random-phase encoding. IET Image Process 11(4):211–216

    Google Scholar 

  19. 19.

    Lin T, Hang L (2019) CSDK: a chi-square distribution-kernel method for image de-noising under the IoT big data environment. Int J Distrib Sens Netw. https://doi.org/10.1177/1550147719847133

    Article  Google Scholar 

  20. 20.

    Teng L, Li H, Karim S (2019) DMCNN: a deep multiscale convolutional neural network model for medical image segmentation. J Healthcare Eng. https://doi.org/10.1155/2019/8597606

    Article  Google Scholar 

  21. 21.

    Yin S, Liu J, Teng L (2020) Improved elliptic curve cryptography with homomorphic encryption for medical image encryption. Int J Netw Secur 22(3):419–424

    Google Scholar 

  22. 22.

    Li P, Chen Z, Yang LT et al (2019) An incremental deep convolutional computation model for feature learning on industrial big data. IEEE Trans Ind Inform 15(3):1341–1349

    Google Scholar 

  23. 23.

    He C, Ming K, Wang Y et al (2019) A deep learning based attack for the chaos-based image encryption. arXiv:1907.12245

  24. 24.

    Ding Y, Wu G, Chen D et al (2020) DeepEDN: a deep learning-based image encryption and decryption network for internet of medical things. arXiv:2004.05523

  25. 25.

    Li X, Jiang Y, Chen M et al (2018) Research on iris image encryption based on deep learning. J Image Video Proc 2018:126. https://doi.org/10.1186/s13640-018-0358-7

    Article  Google Scholar 

  26. 26.

    Yanyan Q, Chennan Z, Rui L, Mingrui C (2019) Research on face image encryption based on deep learning. In: IOP conference series: earth and environmental science, vol 252(5). pp 052007. https://doi.org/10.1088/1755-1315/252/5/052007

  27. 27.

    Muhammad K, Sajjad M, Baik SW (2016) Dual-level security based cyclic18 steganographic method and its application for secure transmission of keyframes during wireless capsule endoscopy. J Med Syst 40:114. https://doi.org/10.1007/s10916-016-0473-x

    Article  Google Scholar 

  28. 28.

    Tran L, Yin X, Liu X (2017) Disentangled representation learning GAN for pose-invariant face recognition. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI. pp 1283–1292. https://doi.org/10.1109/CVPR.2017.141

  29. 29.

    Liang Y, Ouyang K, Jing L, Ruan S, Liu Y, Zhang J, Rosenblum DS, Zheng Y (2019) UrbanFM: inferring fine-grained urban flows. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining (KDD19). Association for Computing Machinery, New York, pp 3132–3142. https://doi.org/10.1145/3292500.3330646

  30. 30.

    Hitaj B, Ateniese G, Perez-Cruz F (2017) Deep models under the GAN: information leakage from collaborative deep learning. In: Proceedings of the 2017 ACM SIGSAC conference on computer and communications security (CCS17). Association for Computing Machinery, New York, pp 603–618. https://doi.org/10.1145/3133956.3134012

  31. 31.

    Ouyang K, Liang Y, Liu Y, Tong Z, Ruan S, Zheng Y, Rosenblum DS (2020) Fine-grained urban flow inference. arXiv:2002.02318

  32. 32.

    Zieba M, Wang L (2017) Training triplet networks with GAN . arXiv:1704.02227

  33. 33.

    Chen Y, Pi DC, Wang B (2019) Enhanced global flower pollination algorithm for parameter identification of chaotic and hyper-chaotic system. Nonlinear Dyn 9:1343–1358

    MATH  Google Scholar 

  34. 34.

    Gonchenko AS, Gonchenko SV et al (2016) Variety of strange pseudohyperbolic attractors in three-dimensional generalized Hnon maps. Physica D Nonlinear Phenom 337:43–57

    MATH  Google Scholar 

  35. 35.

    Ramos JGGS, Barbosa ALR, Macdo AMS (2011) Tunable crossovers for the quantum interference correction to conductance and shot-noise power in chaotic quantum dots with nonideal contacts. Phys Rev B Condens Matter 84(3):035453

    Google Scholar 

  36. 36.

    Liu T, Yin S (2017) An improved particle swarm optimization algorithm used for BP neural network and multimedia course-ware evaluation. Multimed Tools Appl 76(9):11961–11974

    Google Scholar 

  37. 37.

    Chai X, Zhang J, Gan Z et al (2019) Medical image encryption algorithm based on Latin square and memristive chaotic system. Multimed Tools Appl 21:35419–35453

    Google Scholar 

  38. 38.

    Naik K, Pal AK (2018) A cryptosystem for lossless/lossy grayscale images in IWT domain using chaotic map based Generated key matrices. Int J Wavelets Multiresolut Inf Process 16(7):1850024

    MathSciNet  MATH  Google Scholar 

  39. 39.

    Yin Q, Wang C (2018) A new chaotic image encryption scheme using breadth-first search and dynamic diffusion. Int J Bifurc Chaos 28(4):1850047

    MathSciNet  MATH  Google Scholar 

  40. 40.

    Abu Zaid O, El-Fishaw N, Nigm E (2016) Encryption quality measurement of a proposed cryptosystem algorithm for the colored images Compared with Another Algorithm. Int Arab J Inf Technol 13(1):20–29

    Google Scholar 

  41. 41.

    Fu C, Chen JJ, Zou H et al (2012) A chaos-based digital image encryption scheme with an improved diffusion strategy. Opt Express 20(3):2363

    Google Scholar 

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Correspondence to Hang Li.

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Yin, S., Li, H. GSAPSO-MQC:medical image encryption based on genetic simulated annealing particle swarm optimization and modified quantum chaos system. Evol. Intel. (2020). https://doi.org/10.1007/s12065-020-00440-6

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Keywords

  • Medical image encryption
  • Genetic simulated annealing particle swarm optimization
  • Modified quantum chaos system
  • Histogram analysis