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Texture maps and chaotic maps framework for secure medical image transmission

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Abstract

Telemedicine has evolved significantly for detection and diagnosis of diseases remotely, which means more frequent transmission of medical images over the network. The security of any medical image can be characterized as its integrity, confidentiality and authentication. It is these security vulnerabilities that limit the development of mobile healthcare applications, which intend to improve the efficiency of medical image communication. To address the vulnerabilities associated with medical images, we propose a texture edge map and multilevel chaotic map driven encryption framework for medical images. This technique utilizes texture maps generated by utilizing a bank of gabor filters along with multiple chaotic maps viz.: Sine, Cubic and Logistic maps for enhancing the key space, robustness and security of medical images over an insecure channel. The security, speed and reliability of the proposed technique for medical images are illustrated via experiments for key sensitivity, statistical and performance analysis. The proposed technique offers a large key space, pixel diffusion at an acceptable speed. Security analysis shows a high sensitive dependence of the encryption and decryption techniques to any subtle change in the secret key, the plain medical image and the encrypted image. Also, the proposed technique has a large enough key space to see off brute force attacks. Therefore, the proposed technique is a potential candidate for addressing security vulnerabilities of medical images over the communication networks.

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References

  1. Abdmouleh AKMK, Bouhlel, MS (2013) Dynamic chaotic look-up table for mri medical image encryption. Proceedings of the 2013 International Conference on Systems, Control, Signal Processing and Informatics, pp 241–246

  2. Alassaf N, Gutub A (2019) Simulating light-weight-cryptography implementation for IoT healthcare data security applications. Int J E-Health Med Commun (IJEHMC) 10(4):1–15

    Article  Google Scholar 

  3. Alassaf N, Gutub A, Parah S, Al Ghamdi M (2019) Enhancing speed of SIMON: a light-weight-cryptographic algorithm for IoT applications. Multimed Tools Appl 78:32633–32657

    Article  Google Scholar 

  4. Ali S, Hafeez Y, Jhanjhi NZ, Humayun M, Imran M, Nayyar A, Singh S, Ra IH (2020) Towards pattern-based change verification framework for cloud-enabled healthcare component-based. IEEE Access 8:148007–148020

    Article  Google Scholar 

  5. Alvarez E, Fernandez A, Jimenez GP, Marcano A (1999) New approach to chaotic encryption. Phys Lett A 263:373–375

    Article  Google Scholar 

  6. Amin Banday S, Shakeel I, Hamid M, Matto W (2020) Fused Iris Biometric Template Formation Using Logistic Maps and LFSR. 5th International Conference on Next Generation Computing Technologies (NGCT-2019)

  7. Ashtiyani M, Birgani PM, Hosseini HM (2008) “Chaosbased Medical Image Encryption using Symmetric Cryptography.” Information and Communication Technologies: From Theory to Applications, 2008. ICTTA 2008. 3rd international conference on. IEEE, pp 1–5

  8. Behnia S, Akhshani A, Mahmodi H (2008) A novel algorithm for image encryption based on mixture of chaotic maps. Chaos, Solitons Fractals 35(2):408–419

    Article  MathSciNet  Google Scholar 

  9. Belkhouche F, Gokcen I, Qidwai U (2005) Chaotic gray-level image transformation. J Electron Imaging 14(4):043001

    Article  Google Scholar 

  10. Brahimi Z, Bessalah H, Tarabet A, Kholladi MK (2008) Selective encryption techniques of JPEG2000 code stream for medical images transmission. WSEAS Trans Circ Syst 7(7):718–727

    Google Scholar 

  11. Chen GR, Mao YB, Chui CK (2004) Asymmetric image encryption scheme based on 3D chaotic cat maps. Chaos, Solitons Fractals 21(3):749–761

    Article  MathSciNet  Google Scholar 

  12. Chen Y, He F, Li H, Zhang D, Wu Y (2020) A full migration BBO algorithm with enhanced population quality bounds for multimodal biomedical image registration. Appl Soft Comput 93:106335

    Article  Google Scholar 

  13. Daugman JG (1985) Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by 2D visual cortical filters. J Opt Soc A2(7):1160–1169

    Article  Google Scholar 

  14. Diaconu AV (2016) Circular inter–intra pixels bit-level permutation and Chaos-based image encryption. Inf Sci 355:314–327

    Article  Google Scholar 

  15. Farooqi N, Gutub A, Khozium O (2019) Smart Community Challenges: Enabling IoT/M2M Technology Case Study. Life Sci J 16(7):11–17

    Google Scholar 

  16. Fridrich J (1998) Symmetric ciphers based on two-dimensional chaotic maps. Int J Bifurcation Chaos 8(6):1259–1284

    Article  MathSciNet  Google Scholar 

  17. Fu C, Meng WH, Zhan YF, Zhu ZL, Lau FCM, Tse CK, Ma HF (2013) An efficient and secure medical image protection scheme based on chaotic maps. Comput Biol Med 43(8):1000–1010

    Article  Google Scholar 

  18. Gao TG, Chen ZQ (2008) A new image encryption algorithm based on hyper-chaos. Phys Lett A 372(4):394–400

    Article  Google Scholar 

  19. Gao TG, Chen ZQ (2008) Image encryption based on a new total shuffling algorithm. Chaos, Solitons Fractals 38(1):213–220

    Article  MathSciNet  Google Scholar 

  20. Gutub A, Al-Ghamdi M (2020) Hiding shares by multimedia image steganography for optimized counting-based secret sharing. Multimed Tools Appl 79:7951–7985

    Article  Google Scholar 

  21. Hongjun K, Abdurahman LY (2016) Asymmetric color pathological image encryption scheme based on complex hyper chaotic system. Optik 127:5812–5819

    Article  Google Scholar 

  22. Hu JK, Han FL (2009) A pixel-based scrambling scheme for digital medical images protection. J Netw Comput Appl 32(4):788–794

    Article  Google Scholar 

  23. Ji Z, Xia Y, Sun Q, Cao G, Chen Q (2015) Active contours driven by local likelihood image fitting energy for image segmentation. Inf Sci 301:285–304

    Article  Google Scholar 

  24. Jung H, Sung K, Nayak KS, Kim EY, Ye JC (2009) k-t focus: a general compressed sensing framework for high resolution dynamic MRI. Magn Reson 61:103–116

    Article  Google Scholar 

  25. Khan MK, Zhang J (2008) Multimodal face and fingerprint biometrics authentication on space-limited tokens. Neurocomputing 71(13–15):3026–3031

    Article  Google Scholar 

  26. Lee TS (1996) Image representation using 2D gabor wavelets. IEEE Trans Pattern Anal Mach Intell 18(10):959–971

    Article  Google Scholar 

  27. Liehuang Z, Wenzhuo L, Liao L, Hong L (2006) A Novel Algorithm for Scrambling Digital Image Based on Cat Chaotic Mapping. Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP'06), 0–7695–2745-0/2006© IEEE

  28. Lin CF, Chung CH, Lin JH (2009) A chaos-based visual encryption mechanism for clinical EEG signals. Med Biol Eng Comput 47(7):757–762

    Article  Google Scholar 

  29. Liu JL (2006) Efficient selective encryption for JPEG 2000 images using private initial table. Pattern Recogn 39(8):1509–1517

    Article  Google Scholar 

  30. Lou LDC, Hua MC, Liua JL (2009) Multiple layer data hiding scheme for medical images. Comput Stand Interfaces 31(2):329–335

    Article  Google Scholar 

  31. Mao YB, Chen GR, Lian SG (2004) A novel fast image encryption scheme based on 3D chaotic baker maps. Int J Bifurcation Chaos 14(10):3613–3624

    Article  MathSciNet  Google Scholar 

  32. Materka A, Strzelecki M (1998) Texture analysis methods– a review. Technical University of Lodz, Institute of Electronics, COST B11 report, Brussels 9–11

  33. Menezes AJ, van Oorschot PC, Vanstone SA (1996) Handbook of applied cryptography. CRC Press

  34. Mitra S, Uma Shankar B (2015) Medical image analysis for cancer management in natural computing framework. Inf Sci 306:111–131

    Article  Google Scholar 

  35. Ou Y, Sur C, Rhee KH (2007) Region-based selective encryption for medical imaging, Proceedings of the 1st annual international conference on frontiers in algorithmics. Springer, Berlin Heidelberg, pp 62–73

  36. Panduranga HT, NaveenKumar SK (2013) Selective image encryption for medical and satellite images. Int J Eng Sci Tech 5(2). Retrieved from: http://www.enggjournals.com/ijet/abstract.html?file=13-05-01-048. Accessed  09 02 2021

  37. Panduranga HT, Naveenkumar SK (2013) Partial image encryption using block wise shuffling and chaotic maps, 2013 International conference on optical imaging sensor and security (ICOSS). IEEE 1–5. https://doi.org/10.1109/ICOISS.2013.6678417

  38. Pareek NK, Patidar V, Sud KK (2003) Discrete chaotic cryptography using external key. Phys Lett A 309:75–82

    Article  MathSciNet  Google Scholar 

  39. Patidar V, Pareek NK, Sud KK (2009) A new substitution–diffusion based image cipher using chaotic standard and logistic maps. Commun Nonlinear Sci Numer Simul 14(7):3056–3075

    Article  Google Scholar 

  40. Phophalia A, Rajwade A, Mitra SK (2014) Rough set based image denoising for brain MR images. Signal Process 103:24–35

    Article  Google Scholar 

  41. Puech W, Rodrigues JM (2005) “Crypto-compression of medical images by selective encryption of DCT”, EUSIPCO’05: European signal processing conference. Antalya, Turkey

  42. Rao YS, Mitra A, Prasanna SM (2006) “A partial image encryption method with pseudo random sequences”, information systems security. Berlin Heidelberg: Springer; p. 315–25

  43. Sarker MZH, Parvez MS (2005) A cost effective symmetric key crypto-graphic algorithm for small amount of data. Proceedings of the 9th IEEE International Multi topic Conference Karachi, pp 1–6

  44. Scharinger J (1998) Fast encryption of image data using chaotic Kolmogorov flows. J Electron Imaging 7(2):318–325

    Article  Google Scholar 

  45. Shang Z, Ren H, Zhang J (2008) A Block Location Scrambling Algorithm of Digital Image Based on Arnold Transformation. 9th IEEE International Conference for Young Computer Scientists, 978–0-7695-3398-8/08/$25.00

  46. Singh S, Sharma PK, Moon SY, Park JH (2017) Advanced lightweight encryption algorithms for IoT devices: survey, challenges and solutions. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-017-0494-4

  47. Som S, Sen S (2013) A non-adaptive partial encryption of grayscale images based on chaos. Procedia Tech 10:663–671

    Article  Google Scholar 

  48. Sun FY, Liu ST, Li ZQ (2008) A novel image encryption scheme based on spatial chaos map. Chaos, Solitons Fractals 38(3):631–640

    Article  MathSciNet  Google Scholar 

  49. Wang L, Qian X, Zhang Y, Shen J, Cao X (2020) Enhancing sketch-based image retrieval by CNN semantic re-ranking. IEEE Trans Cybern 50(7):3330–3342. https://doi.org/10.1109/TCYB.2019.2894498

    Article  Google Scholar 

  50. Wong KW (2002) A fast chaotic cryptography scheme with dynamic look-up table. Phys Lett A 298:238–242

    Article  MathSciNet  Google Scholar 

  51. Wong WK, Lee LP, Wong KW (2000) A modified chaotic cryptographic method. Comput Phys Commun 138:23–236

    MATH  Google Scholar 

  52. Wu H, Huang J, Shi Y (2015) A reversible data hiding method with contrast enhancement for medical images. J Vis Commun Image Represent 31:146–153

    Article  Google Scholar 

  53. Xiang T, Wong KW, Liao X (2007) Selective image encryption using a spatiotemporal chaotic system. Chaos Interdiscip J Nonlinear Sci. https://doi.org/10.1063/1.2728112

  54. Yu H, He F, Pan Y (2020) A scalable region-based level set method using adaptive bilateral filter for noisy image segmentation. Multimed Tools Appl 79:5743–5765. https://doi.org/10.1007/s11042-019-08493-1

    Article  Google Scholar 

  55. Zhang S, He F (2019) DRCDN: learning deep residual convolutional dehazing networks. Vis Comput 36:1797–1808

    Article  Google Scholar 

  56. Zhao T, Zhang B, He M, Zhang W, Zhou N, Yu J, Fan J (2018) Embedding visual hierarchy with deep networks for large-scale visual recognition. IEEE Trans Image Process 27(10):4740–4755. https://doi.org/10.1109/TIP.2018.2845118

    Article  MathSciNet  MATH  Google Scholar 

  57. Zhou Y, Panetta K, Agaian S (2009) “A lossless encryption method for medical images using edge maps”, Engineering in medicine and biology society. Annual international conference of the IEEE. IEEE. p. 3707–10

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Authors and Affiliations

  1. Machine Learning Lab, School of Engineering & Technology IUST J&K India, Awantipora, India

    Shoaib Amin Banday & Mohammad Khalid Pandit

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  1. Shoaib Amin Banday
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Correspondence to Shoaib Amin Banday.

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Banday, S.A., Pandit, M.K. Texture maps and chaotic maps framework for secure medical image transmission. Multimed Tools Appl 80, 17667–17683 (2021). https://doi.org/10.1007/s11042-021-10564-1

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