Skip to main content
SpringerLink
Log in

Digital ownership tags based on biometric features of iris and fingerprint for content protection and ownership of digital images and audio signals

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

Abstract

This paper is aimed to address the issue of ownership rights of digital data like images and audio signals. This is achieved by inserting a perceptually transparent unique digital pattern in the digital host signal. The digital pattern is generated by a methodical fusion of features extracted from iris image and fingerprint image. The fusion is done in such a way that the individual templates can be later decomposed from the digital pattern and can be used for identification. The pattern is optimized to a size which has acceptable payload under the perceptual transparency constraints of design requirements. The embedding is done using the singular value decomposition method for the audio signals and using discrete cosine transform method for the images. The recovered pattern is subjected to decomposition to individual templates, i.e. fingerprint and iris templates which were subjected to unique identification tests. Experimental results indicate that the embedding of the digital tag in the image or audio do not tamper the perceptual transparency and is also robust to signal processing attacks. The SNR of the watermarked signal is very good and the BER and Normalized correlation of the extracted watermark are very encouraging. The templates which were decomposed from the extracted digital watermark were mapped for unique identification even under serious attacks. Use of two biometric features for generating a digital watermark is a novel attempt for accurate identification of ownership of the digital data as these biometric features will be unique for every subject and hence this can be considered as a significant development towards digital right management (DRM) control.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Agarwal H, Raman B, Venkat I (2014) Blind reliable invisible watermarking method in wavelet domain for face image watermark. Multimed Tools Appl, Springer Sci 1–39

  2. Al-yaman MS, Al-Taee MA, Shahrour AT, Al-husseini IA (2011) Biometric based audio ownership verification using discrete wavelet transform and SVD techniques. 8th Int Multi-Conf Syst Signals Devices (SSD) 1–5

  3. Casia-irisv3, nst. of Automation, Chinese Academy of Sciences (2006) http://www.cbsr.ia.ac.cn/IrisDatabase.htm

  4. Charfeddine M, El’arbi M, Amar CB (2014) A new DCT audio watermarking scheme based on preliminary MP3 study. Multimedia Tools and Applications, Springer Verlag, 70(3):1521–1557

  5. Chen M, Chen Z, Zeng X, Xiong Z (2010) Model order selection in reversible image watermarking. Sel Top Signal Process IEEE J. 4(3):592–604. doi: 10.1109/JSTSP.2010.2049222

  6. Daugman J (1993) High confidence visual recognition of persons by a test of statistical independence. IEEE Trans Pattern Anal Mach Intell 15(11)

  7. Daugman J (2002) How iris recognition works. Proc 2002 Int Conf Image Process 1

  8. Dutta MK, Gupta P, Pathak VK (2009) Blind watermarking in audio signals using biometric features in wavelet domain. Int Conf IEEE Region 10, TENCON, 1–5

  9. Dutta MK, Gupta P, Pathak VK (2009) Biometric based unique key generation for audio watermarking. Int Conf Pattern Recog Mach Intell LNCS 5909:458–463

    Google Scholar 

  10. Dutta MK, Gupta P, Pathak VK (2010) Audio watermarking using pseudorandom sequences based on biometric templates. J Comput 5(3):372–379

    Article  Google Scholar 

  11. Dutta MK, Singh A, Burget R, Attasi H, Choudhary A, Soni KM (2013) Generation of biometric based unique digital watermark from iris image. 36th IEEE Int Conf Telecommun Signal Process , Rome, Italy 685–689

  12. Dutta MK, Singh A, Soni KM, Burget R, Riha K (2013) Watermark generation from fingerprint features for digital right management control. 36th IEEE International Conference on Telecommunications and Signal Processing, Rome, Italy 717–721

  13. Dutta MK, Singh A, Soni KM (2014) A secure algorithm for biometric-based digital image watermarking in DCT domain. Int J Comput Vis Robot (IJCVR) 4(1/2)

  14. Dutta MK, Singh A, Zia TA (2013) An efficient and secure digital image watermarking using features from iris image. Int Conf Control Commun Comput (ICCC) 451–456

  15. Katzenbeisser S, Petitcolas FAP ed (2000) Information hiding techniques for steganography and digital watermarking. Artech House, Inc

  16. Khalili M, Asatryan D (2013) Colour spaces effects on improved discrete wavelet transform-based digital image watermarking using Arnold transform map. Signal Process, IET 7(3):177–187. doi:10.1049/iet-spr.2012.0380

    Article  MathSciNet  Google Scholar 

  17. Kirovski D, Malvar HS (2003) Spread-spectrum watermarking of audio signals. IEEE Trans Signal Process 51(4):1020–1033

    Article  MathSciNet  Google Scholar 

  18. Kishore Kumar NK, Sheeba VS (2012) Blind biometric watermarking based on contourlet transform. Third Int Conf Comput Commun Netw Technol (ICCCNT) 1–6

  19. Lai C-C, Tsai C-C (2010) Digital image watermarking using discrete wavelet transform and singular value decomposition. IEEE Trans Instrum Meas 59(11):3060–3063

    Article  Google Scholar 

  20. Li Z, Yap K-H, Lei B-Y (2011) A new blind robust image watermarking scheme in SVD-DCT composite domain. Image Process (ICIP), 2011 18th IEEE International Conference on 2757–2760, 11–14. doi: 10.1109/ICIP.2011.6116241

  21. Ma B, Wang Y, Li C, Zhang Z, Huang D (2014) Secure multimodal biometric authentication with wavelet quantization based fingerprint watermarking. Multimed Tools Appl, Springer Sci 637–666

  22. Majumder S, Devi KJ, Sarkar SK (2013) Singular value decomposition and wavelet-based iris biometric watermarking. Biometrics, IET. 2(1):21–27. doi:10.1049/iet-bmt.2012.0052

  23. Maltoni D, Maio D, Jain AK, Prabhakar S (2009) Handbook of fingerprint recognition. Springer Professional Computing, 2nd edition

  24. Masek L, Kovesi P, thesis on “Recognition of Human Iris Patterns for Biometric Identification” http://www.csse.uwa.edu.au/~pk/studentprojects/libor/LiborMasek Thesis.pdf

  25. MK Dutta, P Gupta, Pathak VK (2014) A perceptible watermarking algorithm for audio signals. Multimedia Tools and Applications, Springer Verlag 73(2):691–713

  26. Rao NN, Thrimurthy P, Babu BR (2009) An efficient copyright protection scheme for digital images using biometrics and watermarking. 2nd IEEE Int Conf Comput Sci Inf Technol 69–74

  27. Ratha N, Connell JH, Bolle RM (2001) An analysis of minutiae matching strength. In: Proc. Audio and Video-based Biometric Person Authentication (AVBPA), 223–228, (Halmstad, Sweden)

  28. Shaw AK, Majumder S, Sarkar S, Sarkar SK (2013) A novel EMD based watermarking of fingerprint biometric using GEP. First Int Conf Comput Intell: Model Tech Appl (CIMTA) 10:172–183

    Google Scholar 

  29. Wang D-S, Li J-P, Yan Y-H (2008) A novel authentication scheme of the DRM system based on multimodal biometric verification and watermarking technique. Int Conf Apperceiving Comput Intell Anal 212–215

  30. Xiang S, Kim HJ, Huang J (2008) Invariant image watermarking based on statistical features in the low-frequency domain. IEEE Trans Circ Syst Video Technol 18(6):777–790

    Article  Google Scholar 

  31. Zhang F, Liu W, Lin W, Ngi Ngan K (2011) Spread spectrum image watermarking based on perceptual quality metric. Image Process, IEEE Trans 20(11):3207–3218. doi:10.1109/TIP.2011.2146263

    Article  MathSciNet  Google Scholar 

  32. Zhang X, Qian Z, Ren Y, Feng G (2011) Watermarking with flexible self-recovery quality based on compressive sensing and compositive reconstruction. IEEE Trans Inf Forensics Secur 6(4):1223–1232

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported in part by the Grants from Department of Science and Technology, No.DST/TSG/NTS/2011/173,Government of India & National Sustainability Program under Grant LO1401. For the research, Infrastructure of the Six Center was used.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Amity University, Noida, Uttar Pradesh, India

    Malay Kishore Dutta & Anushikha Singh

  2. Faculty of Electrical engineering, Brno University of Technology, Brno, Czech Republic

    Radim Burget

Authors
  1. Malay Kishore Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anushikha Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Radim Burget
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Malay Kishore Dutta.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dutta, M.K., Singh, A. & Burget, R. Digital ownership tags based on biometric features of iris and fingerprint for content protection and ownership of digital images and audio signals. Multimed Tools Appl 75, 16287–16313 (2016). https://doi.org/10.1007/s11042-015-2931-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-015-2931-8

Keywords

Search

Navigation