Advertisement

Concealing Fingerprint-Biometric Data into Audio Signals for Identify Authentication

  • Sani M. Abdullahi
  • Hongxia WangEmail author
  • Qing Qian
  • Wencheng Cao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10082)

Abstract

Numerous security issues are raised through the transmission and storage of biometric data due to its high sensitivity and extremely crucial purpose. However, it might be impossible to recover if lost, counterfeited or hacked, thereby ruining the general aim of securing it. In this paper, an 8-layered feature enhancement algorithm is proposed. A centroid based semi-fragile audio watermarking is used to conceal the enhanced fingerprint biometric data into audio signals. The algorithm starts by encrypting our enhanced/extracted fingerprint template and converting it into binary bits prior to watermarking. It hence proceeds with computing the centroid of each audio frame and once more encrypting the obtained watermark using a Logistic map operation. DWT and DCT are performed on the sub-band which carries the centroid of audio thereby embedding the encrypted watermark bits into the domain signal. ODG and SDG analysis are performed on both the original and watermarked signal and the results signify the efficiency of the proposed scheme. Moreover, some signal processing operations are finally carried out on the watermarked signal and the outcome was intriguing as all attacks are counteracted.

Keywords

Biometrics Fingerprint feature Embedding Identity authentication Watermarking 

Notes

Acknowledgement

This work was supported by the National Science Foundation of China (NSFC) under the grant No. U1536110.

References

  1. 1.
    Gaurav, B., Wu, Q.M.J.: Chaos-based security solution for fingerprint data during communication and transmission. IEEE Trans. Instr. Meas. 61(4), 876–887 (2012)CrossRefGoogle Scholar
  2. 2.
    Hua, G., Huang, J., Shi, Y.Q., Goh, J., Vrizlynn, L.L.T.: Twenty years of digital audio watermarking-a comprehensive review. Sig. Process. 128, 222–242 (2016)CrossRefGoogle Scholar
  3. 3.
    Anil, K.J., Arus, R., Sharath, P.: Biometrics: a tool for information security. IEEE Trans. Inf. Forensics Secur. 1(2), 125–143 (2006)CrossRefGoogle Scholar
  4. 4.
    Bas, P., Furon, T.: A new measure of watermarking security: the effective key length. IEEE Trans. Inf. Forensics Secur. 8(8), 1306–1317 (2013)CrossRefGoogle Scholar
  5. 5.
    Chunlei, L., Ruimin, Y., Zhoufeng, L., Jianjun, L., Zhenduo, G.: Semi-fragile self-recoverable watermarking scheme for face image protection. Comput. Electr. Eng. 54, 484–493 (2016)CrossRefGoogle Scholar
  6. 6.
    Mohammed, A., Fengling, H., Ron, V.: Fingerprint image watermarking approach using DTCWT without corrupting minutiae. In: IEEE 6th International Congress on Image and Signal Processing (CISP), vol. 3, pp. 1717–1723 (2013)Google Scholar
  7. 7.
    Arashdeep, K., Malay, K.D., Soni, K.M., Nidhi, T.: A secure and high payload digital audio watermarking using features from iris image. In: IEEE International Conference on Contemporary Computing and Informatics (IC3I), pp. 509–512 (2014)Google Scholar
  8. 8.
    Anil, K., Arun, A., Karthik, N.: Introduction to Biometrics. Springer, Heidelberg (2011). ISBN 978-0-387-77325-4Google Scholar
  9. 9.
    Anush, S., Mayank, V., Richa, S.: Latent fingerprint matching: a survey. IEEE Access. IEEE Biom. Compendium RFIC Virtual J. 2, 982–1004 (2014)Google Scholar
  10. 10.
    Kai, C., Eryun, L., Anil, K.: Segmentation and enhancement of latent fingerprint. IEEE Trans. Pattern Anal. Mach. Intell. 36(9), 1847–1859 (2014)CrossRefGoogle Scholar
  11. 11.
    Josef, S., Mikael, N., Benny, S., Ingvar, C.: Adaptive fingerprint image enhancement with emphasis on preprocessing of data. IEEE Trans. Image Process. 22(2), 644–656 (2013)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Milos, V., Brankica, M., Andelija, M., Dragan, R.: Improving minutiae extraction in fingerprint images through robust enhancement. In: IEEE Conference on 21st Telecommunication Forum (TELFOR), pp. 506–509 (2013)Google Scholar
  13. 13.
    Ravi, K., Sai, K., Rajendra, P., Subba, R., Ravi, P.: Fingerprint minutia match using bifurcation technique. Int. J. Comput. Sci. Commun. Netw. 2(4), 478–486 (2012)Google Scholar
  14. 14.
    Gnanasivam, P., Muttan, S.: An efficient algorithm for fingerprint preprocessing and feature extraction. Procedia Comput. Sci. 2, 133–142 (2010)CrossRefGoogle Scholar
  15. 15.
    Maltoni, D., Maio, D., Jain, A., Prabhakar, S.: Handbook of Fingerprint Recognition, pp. 141–144. Springer, New York (2003)zbMATHGoogle Scholar
  16. 16.
    Wang, H., Fan, M.: Centroid-based semi-fragile audio watermarking in hybrid domain. China Sci. Inf. Sci. 53(3), 619–633 (2010)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Abraham, J., Kwan, P., Gao, J.: Fingerprint matching using a hybrid shape and orientation descriptor. State of the Art in Biometrics (2011). ISBN 978-953-307-489-4Google Scholar
  18. 18.
    Hua, G., Goh, J., Thing, V.L.L.: Time-spread echo-based watermarking with optimized imperceptibility and robustness. IEEE/ACM Trans. Audio Speech Lang. Process. 23(2), 227–239 (2015)CrossRefGoogle Scholar
  19. 19.
    Xiang, Y., Natgunanathan, I., Rong, Y., Guo, S.: Spread spectrum-based high embedding capacity watermarking method for audio signals. IEEE/ACM Trans. Audio Speech Lang. Process. 23(12), 2228–2237 (2015)CrossRefGoogle Scholar
  20. 20.
    International Telecommunication Union. Method for objective measurements of perceived audio quality (PAEQ) ITU-RBS.1387-1, Geneva, Switzerland (1998–2001)Google Scholar
  21. 21.
    Giang, M., Wu, X., Hua, Q.: A fast thinning algorithm for fingerprint image. In: 1st International Conference on Information Science and Engineering (ICISE), pp. 1039–1042 (2009)Google Scholar
  22. 22.
    Jianchun, H., Jinjun, B.: Normalization of fingerprint image using the local feature. In: International Conference on Computer Science and Service System, pp. 1643–1646 (2012)Google Scholar
  23. 23.
    Khan, M.K., Xie, L., Zhang, J.: Chaos and NDFT-based spread spectrum concealing of fingerprint-biometric data into audio signals. J. Digit. Sig. Process. 20, 179–190 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sani M. Abdullahi
    • 1
  • Hongxia Wang
    • 1
    Email author
  • Qing Qian
    • 1
  • Wencheng Cao
    • 1
  1. 1.School of Information Science and TechnologySouthwest Jiaotong UniversityChengduPeople’s Republic of China

Personalised recommendations