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A robust cryptosystem to enhance the security in speech based person authentication

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Abstract

The developments in technology have made us utilizing speech as a biometric to authenticate persons. In this paper, speech encryption and decryption algorithm are presented for enhancing the security in speech-based person authentication systems. The implementation of the authentication system contains the feature extraction, modeling techniques and testing procedures for authenticating the person. Firstly, the Mel frequency cepstral coefficient (MFCC) features are extracted from the training speech utterances and models are developed for each speaker. The speech encryption system encrypts the test speech utterances. Multiple chaotic mapping techniques and Deoxyribonucleic acid (DNA) addition based speech cryptosystem is developed to secure test speech against attacks. The speech encryption system deals with sampled test speech signal given as input, which is subjected to intra level and inter level bit substitution. These resultant samples are encoded into the DNA sequence denoted by P(n). The DNA sequence P(n) and DNA sequences {A(n), B(n), C(n), D(n)} obtained using different techniques based on chaos, such as tent mapping, henon mapping, sine mapping, and logistic mapping and summed up together using DNA addition operation. Finally, the encrypted test speech is obtained using DNA decoding. The speaker authentication system in the receiving side decrypts the encrypted signal and identifies the speakers from the decrypted speech. The correlation coefficient test, Signal to noise ratio test, Peak Signal to Noise Ratio test, key sensitivity test, NSCR and UACI test, key space analysis, and histogram analysis are the techniques used as metrics to prove the efficiency of the proposed cryptosystem. Overall individual accuracy is 97% for the text dependent person authentication with the original test speech set and decrypted test speech set. Overall individual accuracy is 66% for the text independent person authentication with the original test speech set and decrypted test speech set. In our work, the speech utterances are taken from AVSpoof database for authenticating 44 speakers. Our work highlights the efficiency of the encryption system, to provide security for test speech and person authentication using speech as a biometric.

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References

  1. Das RK, Jelil S, Prasanna SRM (2017) Development of Multi level Speech based Person Authentication System. Journal of Signal Processing Systems 88(3):259–271

    Article  Google Scholar 

  2. Dellwo V, French P, He L (2018) Voice biometrics for forensic speaker recognition applications. In: Frühholz S, Belin P (eds) The Oxford handbook of voice perception. Oxford University Press, Oxford, pp 777–798

    Google Scholar 

  3. Subhadeep Dey, Sujit Barman, Ramesh K. Bhukya, Rohan K. Das, Haris B. C, S. R. M Prasanna, R. Sinha, “Speech Biometric Based Attendance System”, Twentieth National conference on communications, pp - 1-6, 2014.

  4. Enayatifar R, Abdullah AH, FauziIsnin I (2014) Chaos based image encryption using hybrid genetic algorithm and a DNA sequence. optics and lasers in engineering 56:83–93

    Article  Google Scholar 

  5. Ergünay S. K, Khoury E, Lazaridis A,Marcel S, “On the vulnerability of speaker verification to realistic voice spoofing” Int Proc. Int. Conf. on Biometrics: Theory, Applications and Systems (BTAS), 2015.

  6. Eshwarappa MN, Latte MV (2011) Multimodal Biometric Person Authentication using Speech, Signature and Handwriting Features. International Journal of Advanced Computer Science and Applications, Special Issue on Artificial Intelligence 1(3):77–86

    Google Scholar 

  7. Farsana FJ, Gopakumar K (2016) A novel approach for speech encryption: Zaslavsky map as Pseudo random number generator. Procedia computer science 93:816–823

    Article  Google Scholar 

  8. F. J. Farsana and K. Gopakumar, “Speech encryption algorithm based on nonorthogonal quantum state with Hyperchaotic Keystreams”, Advances in Mathematical Physics, Volume 2020.

  9. Hamza R, Titouna F (2016) A novel sensitive image encryption algorithm based on the Zaslavsky chaotic map. Information Security Journal: A Global Perspective 25(4–6):162–179

    Google Scholar 

  10. Rafik Hamza, Zheng Yan, Khan Muhammad, Paolo Bellavista, Faiza Titouna, “A privacy-preserving cryptosystem for IoT E-healthcare”, Information Sciences, 2019.

  11. Hamza R., Hassan A., Patil A. S. (2019) “A Lightweight Secure IoT Surveillance Framework Based on DCT-DFRT Algorithms” In: Chen X., Huang X., Zhang J. (eds) Machine Learning for Cyber Security. ML4CS 2019. Lecture notes in computer science, vol 11806. Springer, Cham

  12. Kar B, Karthik B (2006) Pranab Kumar Dutta, “speech and face biometrics for person authentication”. International Conference on Industrial Technology:391–396

  13. Kocarev L (2001) Chaos-based cryptography: a brief overview. IEEE circuits and system magazine 1(3):6–21

    Article  Google Scholar 

  14. A. Kounoudes, V. Kekatos and S. Mavromoustakos, "Voice Biometric Authentication for Enhancing Internet Service Security," 2006 2nd international conference on Information & Communication Technologies, Damascus, 2006, pp. 1020–1025.

  15. Lim YH, Yook D (2015) Formant Based Robust Voice Activity Detection. IEEE/ ACM Transactions on Audio, Speech, and Language Processing 23(12):2238–2224

    Google Scholar 

  16. Mosa E, Nagy W, Messiha, Zahran O, Fathi E, El-Samie A (2011) chaotic encryption of speech signals. international journal of speech technology 14:285–296

    Article  Google Scholar 

  17. Nagakrishnan R, Revathi A, “A robust speech encryption system based on DNA addition and chaotic maps”, 18th international conference on intelligent systems design and applications, volume 1, pp - 1070-1080, 2018.

  18. Pareek NK, Patidar V, Sud KK (2005) Cryptography using multiple one-dimensional chaotic maps. Communication in Nonlinear science and Numerical Simulation 10(7):715–723

    Article  MathSciNet  Google Scholar 

  19. R. D. Peacocke and D. H. Graf, "An introduction to speech and speaker recognition," in Computer, vol. 23, no. 8, pp. 26–33, 1990.

  20. Rabiner L, Juang B. H, “fundamentals of speech recognition”, Prentice Hall, New Jersey, 1993.

  21. Ravichandran D, Praveenkumar P, Rayappan JBB, Amirtharajan R (2017) DNA Chaos Blend to Secure Medical Privacy. IEEE transactions on Nano bioscience 16(8):850–858

    Article  Google Scholar 

  22. Revathi A, Venkataramani Y (2011) Speaker independent continuous speech and isolated digit recognition using VQ and HMM. proceedings of IEEE sponsored international conference on communication and signal processing:198–202

  23. Revathi A, Jeyalakshmi C, Thenmozhi K (2018) Digital Speech watermarking to enhance the security using speech as a biometric for person authentication. International Journal of Speech Technology 21(4):1021–1031

    Article  Google Scholar 

  24. Revathi A, Jeyalakshmi C, Thenmozhi K (2019) Person Authentication using speech as a biometric against play back attacks. Journal of Multimedia tools and applications 78(2):1569–1582

    Article  Google Scholar 

  25. Sathiyamurthi P, Ramakrishnan (2017) Speech encryption using chaotic shift keying for secured speech communication. EURASIP Journal on Audio, Speech, and Music Processing 20:1–11

    Google Scholar 

  26. Sheela SJ, Suresh K (2017) V and Deepaknath Tandur, “Chaos based speech encryption using modified Henon map”. Proceedings of IEEE International Conference on Electrical, Computer and Communication Technologies

    Google Scholar 

  27. Sheela SJ, Suresh KV, Tandur D (2017) A Novel Audio Cryptosystem Using Chaotic Maps and DNA Encoding. Journal of Computer Networks and Communications 2017:1–12

    Article  Google Scholar 

  28. Singh N (2019) Voice Biometric: Revolution in Field of Security. CSI Communications 43(8):24–25

    Google Scholar 

  29. Slimani D, Merazka F (2018) Encryption of speech signal with multiple secret keys. Procedia computer science 128:79–88

    Article  Google Scholar 

  30. Wu Y, Joseph P, Noonan, Agaian S (2011) NPCR and UACI randomness tests for image encryption. Cyber Journals: Multidisciplinary Journals in science and technology, journal of selected areas in telecommunications (JSAT) 2:31–38

    Google Scholar 

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  1. Department of ECE, School of EEE, SASTRA Deemed to be University, Thanjavur, Tamilnadu, India

    R. Nagakrishnan & A. Revathi

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  1. R. Nagakrishnan
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  2. A. Revathi
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Nagakrishnan, R., Revathi, A. A robust cryptosystem to enhance the security in speech based person authentication. Multimed Tools Appl 79, 20795–20819 (2020). https://doi.org/10.1007/s11042-020-08846-1

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  • DOI: https://doi.org/10.1007/s11042-020-08846-1

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