Skip to main content
Log in

A secure image encryption algorithm based on fractional transforms and scrambling in combination with multimodal biometric keys

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

Abstract

In today’s digital world, security is a preeminent element in the transmission of digital images. In this paper, image encryption algorithm is proposed using fractional transform and scrambling along with multimodal biometric keys. For unauthorized persons, it is very difficult to retrieve the biometric keys. Firstly, both iris and fingerprint binary codes are XORed and given to the original image. This randomized image is secured using fractional order as a key. The significant feature of fractional transforms benefits from its extra degree of freedom that is provided by its fractional orders. The fractional order is calculated from the iris key. To make the encryption more confusing, scrambling is used to shuffle the position of pixels. Experimental results like histogram analysis, correlation analysis, peak signal-to-noise ratio, mean square error, structural similarity index measure, spectral distortion, information entropy, key sensitivity analysis, differential attacks and spoofing attacks verify the efficacy of proposed algorithm.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Acharya B, Patra SK, Panda G (2008) Image Encryption by Novel Cryptosystem Using Matrix Transformation. First International Conference on Emerging Trends in Engineering and Technology, IEEE, 77–81

  2. Bhatnagar G, Wu QJ (2014) Biometric inspired multimedia encryption based on Dual Parameter Fractional Fourier Transform. IEEE transactions on systems, Man, And Cybernetics: Systems 44(9):1234–1247

    Article  Google Scholar 

  3. Bhatnagar G, Wu QJ, Raman B (2013) Discrete fractional wavelet transform and its application to multiple encryption. Inf Sci 223:297–316

    Article  MathSciNet  Google Scholar 

  4. Chandra S, Paira S, Alam SS, Sanyal G (2014) A comparitive survey of symmetric and asymmetric key cryptography. International Conference on Electronics, Communication and Computational Engineering (ICECCE), 83–93

  5. Daugman J (2003) The importance of being random: statistical principles of iris recognition. Pattern Recogn 36(2):279–291

    Article  Google Scholar 

  6. Galbally J, Marcel S, Fierrez J (2014) Image quality assessment for fake biometric detection: Application to iris, fingerprint, and face recognition. IEEE Trans Image Process 23(2):710–724

    Article  MathSciNet  Google Scholar 

  7. Huijuan X, Shuisheng Q, Yong-Zhong DCH, Ying C (2007) A Composite Image Encryption Scheme Using AES and Chaotic Series. The First International Symposium in Data, Privacy, and E-Commerce, ISDPE, IEEE, 277279–277279

  8. Jain A, Flynn P, Ross AA (2007) Handbook of biometrics. Springer Science & Business Media, Berlin Heidelberg

    Google Scholar 

  9. Jindal N, Singh K (2013) Image Retrieval Algorithm Based on Discrete Fractional Transforms. J Electr Eng 64(4):250–255

    Google Scholar 

  10. Jindal N, Singh K (2014) Image and video processing using discrete fractional transforms. SIViP 8(8):1543–1553

    Article  Google Scholar 

  11. Khashan OA, Zin AM, Sundararajan EA (2014) Performance study of selective encryption in comparison to full encryption for still visual images. Journal of Zhejiang University SCIENCE C 15(6):435–444

    Article  Google Scholar 

  12. Lima JB, Novaes LFG (2014) Image encryption based on the fractional Fourier transform over finite fields. Signal Process 94:521–530

    Article  Google Scholar 

  13. Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2Activity: Recognizing Complex Activities from Sensor Data. 24th International Joint Conference on Artificial Intelligence, 1617–1623

  14. Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing, 108–115

  15. Liu Y, Zhang L, Nie L, Yan Y, Rosenblum DS (2016) Fortune teller: predicting your career path. Thirtieth AAAI Conference on Artificial Intelligence, pp. 201–207

  16. Liu Y, Zheng Y, Liang Y, Liu S, Rosenblum DS (2016) Urban water quality prediction based on multi-task multi-view learning. 25th International Joint Conference on Artificial Intelligence, pp. 2576–2582

  17. Naik K, Pal AK (2015) Design of a cryptosystem for DCT compressed image using Arnold transform and fractional Fourier transform. International Journal of Computational Vision and Robotics 5(3):335–346

    Article  Google Scholar 

  18. Narayanan VA, Prabhu KMM (2003) The fractional Fourier transforms: theory, implementation and error analysis. Microprocess Microsyst 27(10):511–521

    Article  Google Scholar 

  19. Pan SM, Wen RH, Zhou ZH, Zhou NR (2017) Optical multi-image encryption scheme based on discrete cosine transform and nonlinear fractional Mellin transform. Multimedia Tools and Applications 76(2):2933–2953

    Article  Google Scholar 

  20. Parvin Z, Seyedarabi H, Shamsi M (2016) A new secure and sensitive image encryption scheme based on new substitution with chaotic function. Multimedia Tools and Applications 75(17):10631–10648

    Article  Google Scholar 

  21. Rao BR, Rao EK, Rao SR (2012) Finger Print Parameter Based Cryptographic Key Generation. International Journal of Engineering Research and Applications (IJERA) ISSN, 2248–9622

  22. Singh H (2016) Cryptosystem for Securing Image Encryption Using Structured Phase Masks in Fresnel Wavelet Transform Domain. 3D Res 7(4):34

    Article  Google Scholar 

  23. Sinha A, Singh K (2013) Image encryption using fractional Fourier transform and 3D Jigsaw transform. Opt Eng

  24. Sui L, Duan K, Liang J (2015) Double-image encryption based on discrete multiple-parameter fractional angular transform and two-coupled logistic maps. Opt Commun 343:140–149

    Article  Google Scholar 

  25. Sun Q, Guan P, Qiu Y, Xue Y (2012) A Novel Digital Image Encryption Method Based on One-dimensional Random Scrambling. 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), IEEE, 1669–1672

  26. Takeda M, Nakano K, Suzuki H, Yamaguchi M (2012) Encoding plaintext by Fourier transform hologram in double random phase encoding using fingerprint keys. J Opt 14(9):094003

    Article  Google Scholar 

  27. Vilardy JM, Useche J, Torres CO, Mattos L (2011) Image encryption using the fractional wavelet transform. J Phys Conf Ser 274(1):012047

    Article  Google Scholar 

  28. Wang XY, Yang L, Liu R, Kadir A (2010) A chaotic image encryption algorithm based on perceptron model. Nonlinear Dynamics 62(3):615–621

    Article  MathSciNet  Google Scholar 

  29. Wu J, Guo F, Zhou N (2013) Single-Channel Color Image Encryption using the Reality-Preserving Fractional Discrete Cosine Transform in YCbCr Space. JCP 8(11):2816–2822

    Google Scholar 

  30. Xu L, Li Z, Li J, Hua W (2016) A novel bit-level image encryption algorithm based on chaotic maps. Opt Lasers Eng 78:17–25

    Article  Google Scholar 

  31. Yu C, Li J, Li X, Ren X, Gupta BB (2018) Four-image encryption scheme based on quaternion Fresnel transform, chaos and computer generated hologram. Multimed Tools Appl 77:4585–4608

    Article  Google Scholar 

  32. Zhao D, Li X, Chen L (2008) Optical image encryption with redefined fractional Hartley transform. Opt Commun 281(21):5326–5329

    Article  Google Scholar 

  33. Zhao Q, Zhang D, Zhang L, Luo N (2010) High resolution partial fingerprint alignment using pore–valley descriptors. Pattern Recogn 43(3):1050–1061

    Article  Google Scholar 

  34. Zhou N, Wang Y, Gong L (2011) Novel optical image encryption scheme based on fractional Mellin transform. Opt Commun 284(13):3234–3242

    Article  Google Scholar 

  35. Zhou N, Zhang A, Wu J, Pei D, Yang Y (2014) Novel hybrid image compression-encryption algorithm based on compressive sensing. Optik-International Journal for Light and Electron Optics 125(18):5075–5080

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Neeru Jindal.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaur, J., Jindal, N. A secure image encryption algorithm based on fractional transforms and scrambling in combination with multimodal biometric keys. Multimed Tools Appl 78, 11585–11606 (2019). https://doi.org/10.1007/s11042-018-6701-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6701-2

Keywords

Navigation