Verification of iris image authenticity using fragile watermarking

Open access


This paper proposes and evaluates a watermarking-based approach to certify the authenticity of iris images when they are captured by a genuine equipment. In the proposed method, the iris images are secretly signed before being used in biometric processes, and the resulting signature is embedded into the JPEG carrier image in the DCT domain in a data-dependent way. Any alteration of the original (certified) image makes the signature no longer corresponding to this image and this change can be quickly identified at the receiver site. Hence, it is called fragile watermarking to differentiate this method from regular watermarking that should present some robustness against image alterations. There is no need to attach any auxiliary signature data, hence the existing, already standardized transmission channels and storage protocols may be used. The embedding procedure requires to remove some part of the original information. But, by using the BATH dataset comprising 32 000 iris images collected for 1 600 distinct eyes, we verify that the proposed alterations have no impact on iris recognition reliability, although statistically significant, small differences in genuine score distributions are observed when the watermark is embedded to both the enrollment and verification iris images. This is a unique evaluation of how the watermark embedding of digital signatures into the ISO CROPPED iris images (during the enrollment, verification or both) influences the reliability of a well-established, commercial iris recognition methodology. Without loss in generality, this approach is targeted to biometric-enabled ID documents that deploy iris data to authenticate the holder of the document.

[1] Digital Watermarking Alliance, home page:, (accessed April 15, 2016).

[2] P. Lipiński, “Watermarking software in practical applications”, Bull. Pol. Ac.: Tech. 59 (1) 21–25 (2011).

[3] E. T. Lin, E. J. Delp, “A review of fragile image watermarks”, Proceedings of the Multimedia and SecurityWorkshop at ACM Multimedia, 35-39 (1999).

[4] J. Blackledge, Cryptography and Steganography: New Algorithms and Applications, Center for Advanced Studies, WUT (2011).

[5] U.S. Customs and Border Protection, NEXUS Program: (accessed April 15, 2016).

[6] Unique Identification Authority of India, AADHAAR: (accessed April 15, 2016).

[7] N. Bartlow, N. Kalka, B. Cukic, A. Ross, “Iris digital watermarking” in: Encyclopedia of Biometrics, Springer US, Boston, MA, 778–787 (2009)

[8] ISO/IEC 19794-6, “Information technology – Biometric data interchange formats – Part 6: Iris image data”. Final Draft International Standard (FDIS) (2011).

[9] M. Wang, K. Fan, X. Li, Q. Zeng, “A novel digital content protection scheme combining iris identity based digital signature and semi-fragile watermark”, International Conference on Communication Technology ICCT, 1–4 (2006)

[10] W. Na, Z. Chiya, L. Xia, W. Yunjin, “Enhancing iris-feature security with steganography”, 5th IEEE Conference on Industrial Electronics and Applications, 2233–2237 (2010)

[11] M. Fouad, A. El Saddik, E. Petriu, “Combining DWT and LSB watermarking to secure revocable iris templates”, 10th International Conference on Information Sciences Signal Processing and their Applications, 25–28 (2010).

[12] S. Majumder, K. J. Devi, S. K. Sarkar, “Singular value decomposition and wavelet-based iris biometric watermarking”, IET Biometrics 2 (6) 21–27, (2013).

[13] J. Dong, T. Tan, “Effects of watermarking on iris recognition performance”, 10th International Conference on Control, Automation, Robotics and Vision, 1156–1161 (2008)

[14] A. Lock, A. Allen, “Effects of reversible watermarking on iris recognition performance”, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 8 (4) 574 – 579 (2014).

[15] A. Wilkowski, W. Kasprzak, “Steganographic authentication method for electronic IDs”, Image Analysis and Recognition (ICIAR), Lecture Notes in Computer Science, Springer-Verlag Berlin-Heidelberg, 7950 726–733 (2013).

[16] N. Agrawal, M. Savvides, “Biometric data hiding: A 3 factor authentication approach to verify identity with a single image using steganography, encryption and matching”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 85–92 (2009).

[17] J. Hämmerle-Uhl, K. Raab, A. Uhl, “Experimental study on the impact of robust watermarking on iris recognition accuracy”, The ACM Symposium on Applied Computing, New York, 1479–1484 (2010).

[18] L. Debiasi, A. Uhl, Z. Sun, “Generation of iris sensor PRNU fingerprints from uncorrelated data”, International Workshop on Biometrics and Forensics (IWBF), 1–6 (2014).

[19] J. Daugman, C. Downing, “Effect of severe image compression on iris recognition performance”, IEEE Transactions on Information Forensics and Security 3 (1) 52–61 (2008).

[20] S. Jenisch, S. Lukesch, A. Uhl, “Comparison of compression algorithms’ impact on iris recognition accuracy II: revisiting JPEG”, Proceedings of SPIE, Security, Forensics, Steganography, and Watermarking of Multimedia Contents, 68190M–68190M–9 (2008).

[21] C. Rathgeb, A. Uhl, P. Wild, “Evaluating the impact of iris image compression on segmentation and recognition accuracy”, Tech. Rep. 2012-05, Dept. of Computer Sciences, University of Salzburg, Salzburg, Austria, (2012).

[22] P. Grother, E. Tabassi, G. W. Quinn, W. Salamon, “IREX I: Performance of iris recognition algorithms on standard images”, NIST Interagency Tech. Rep. 7629, Information Access Division, National Institute of Standards and Technology, (2009).

[23] J. Daugman, “High confidence visual recognition of persons by a test of statistical independence”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 15 (11), 1148–1161 (1993).

[24] J. Daugman, “New methods in iris recognition”, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 37 (5), 1167–1175 (2007).

[25] FotoNation UK Limited (formerly: SmartSensors Limited), Monro Iris Recognition Library and INterface (MIRLIN SDK), (accessed on Aug. 25, 2015).

[26] D. M. Monro, S. Rakshit, D. Zhang, “DCT-based iris recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 29 (04), 586–595 (2007).

[27] D. M. Monro, S. Rakshit, D. Zhang, “Correction to: DCT-based iris recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (09) 2, (2007).

[28] SmartSensors Limited, BATH University Iris Database (1600 classes), (accessed on Aug. 25, 2015).

[29] ISO/IEC 29794-6, “Information technology – Biometric sample quality – Part 6: Iris image data”. Final Draft International Standard (FDIS) (2014).

[30] G. W. Quinn, P. Grother, E. Tabassi, “Standard iris storage formats”, Handbook of Iris Recognition, Springer London, 55–66 (2013).

[31] A. J. Menezes, S. A. Vanstone, P. C. V. Oorschot, Handbook of Applied Cryptography, 1st Edition, CRC Press, Inc., Boca Raton (1996).

[32] S. Hetzl, P. Mutzel, “A graph-theoretic approach to steganography”, Proceedings of the 9th IFIP TC-6 TC-11 international conference on Communications and Multimedia Security (CMS), Springer-Verlag, Berlin, Heidelberg, 119–128 (2005).

[33] T. Adamski, W. Nowakowski, “The average time complexity of probabilistic algorithms for finding generators in finite cyclic groups”, Bull. Pol. Ac.: Tech. 63 (4) 989–996 (2015).

Bulletin of the Polish Academy of Sciences Technical Sciences

The Journal of Polish Academy of Sciences

Journal Information

IMPACT FACTOR 2016: 1.156
5-year IMPACT FACTOR: 1.238

CiteScore 2016: 1.50

SCImago Journal Rank (SJR) 2016: 0.457
Source Normalized Impact per Paper (SNIP) 2016: 1.239


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 150 106 10
PDF Downloads 73 65 4