Iris Image Watermarking Technique for Security and Manipulation Reveal

Rasha Thabit - Dijlah University College, Al Jamaa, 10001, Baghdad, Iraq
Saad Shukr - Al-Rasheed University College, Al Jamaa, 10001, Baghdad, Iraq


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.6.4.1287

Abstract


Providing security while storing or sharing iris images has been considered as an interesting research topic and accordingly different iris image watermarking techniques have been presented. Most of the available techniques have been presented to ensure the attachment of the secret data to their related iris images or to hide a logo which can be used for copyright purposes. The previous security techniques can successfully meet their aims; however, they cannot reveal the manipulations in the iris region. This paper presents an iris image watermarking technique that can provide security and reveal manipulations in the iris region. At the sender side, the proposed technique divides the image into two regions (i.e., iris region and non-iris region) and generates the manipulation reveal data from the iris region then embeds it in the non-iris region. At the receiver side, the secret data is extracted from the non-iris region and compared with calculated data from the iris region to reveal manipulations if exist. Different experiments have been conducted to evaluate the performance of the proposed technique which proved its efficiency not only in providing security but also in revealing any manipulations in the iris region.

Keywords


Manipulation reveals in iris images; verification and security; fake iris image.

Full Text:

PDF

References


G. Singh, R. K. Singh, R. Saha, and N. Agarwal, IWT Based Iris Recognition for Image Authentication, Procedia Comput. Sci., vol. 171, pp. 1868–1876, 2020, doi: https://doi.org/10.1016/j.procs.2020.04.200.

K. W. Bowyer, K. Hollingsworth, and P. J. Flynn, Image understanding for iris biometrics: A survey, Comput. Vis. Image Underst., vol. 110, no. 2, pp. 281–307, May 2008, doi: 10.1016/j.cviu.2007.08.005.

K. J. Anil, B. Ruud, and P. Sharath, Biometrics: Personal Identification in Networked Society. Springer Science & Business Media, 2006. [Online]. Available: https://www.springer.com/gp/book/9780387285399

S. H. Moi et al., An Improved Approach of Iris Biometric Authentication Performance and Security with Cryptography and Error Correction Codes, JOIV Int. J. Informatics Vis., vol. 6, no. 2–2, pp. 531–339, Aug. 2022, doi: 10.30630/joiv.6.2-2.1091.

M. A. M. Abdullah, S. S. Dlay, and W. L. Woo, Securing Iris Images with a Robust Watermarking Algorithm based on Discrete Cosine Transform, in Proceedings of the 10th International Conference on Computer Vision Theory and Applications, 2015, pp. 108–114. doi: 10.5220/0005305701080114.

K. W. Bowyer, K. P. Hollingsworth, and P. J. Flynn, A Survey of Iris Biometrics Research: 2008–2010, in Handbook of Iris Recognition, London: Springer London, 2013, pp. 15–54. doi: 10.1007/978-1-4471-4402-1_2.

P. De and D. Ghoshal, Human Iris Recognition for Clean Electoral Process in India by Creating a Fraud Free Voter Registration List, Procedia Comput. Sci., vol. 89, pp. 850–855, 2016, doi: https://doi.org/10.1016/j.procs.2016.06.071.

Q. Hu, S. Yin, H. Ni, and Y. Huang, An End to End Deep Neural Network for Iris Recognition, Procedia Comput. Sci., vol. 174, pp. 505–517, 2020, doi: https://doi.org/10.1016/j.procs.2020.06.118.

E. Mostafa, M. Mansour, and H. Saad, Parallel-Bit Stream for Securing Iris Recognition, IJCSI Int. J. Comput. Sci., vol. 9, no. 3–2, pp. 347–351, 2012, [Online]. Available: http://www.ijcsi.org/papers/IJCSI-9-3-2-347-351.pdf

J. Lu, T. Qu, and H. R. Karimi, Novel Iris Biometric Watermarking Based on Singular Value Decomposition and Discrete Cosine Transform, Math. Probl. Eng., vol. 2014, pp. 1–6, 2014, doi: 10.1155/2014/926170.

B.Alekya Hima bindu, Watermarking of digital images with iris based biometric data using wavelet and SVD, Int. J. Eng. Dev. Res., vol. 4, pp. 726–731, 2016.

A. Czajka, W. Kasprzak, and A. Wilkowski, Verification of iris image authenticity using fragile watermarking, Bull. Polish Acad. Sci. Tech. Sci., vol. 64, no. 4, pp. 807–819, Dec. 2016, doi: 10.1515/bpasts-2016-0090.

J. Dong and T. Tan, Effects of watermarking on iris recognition performance, in 2008 10th International Conference on Control, Automation, Robotics and Vision, Dec. 2008, pp. 1156–1161. doi: 10.1109/ICARCV.2008.4795684.

A. Lock and A. Allen, Effects of Reversible Watermarking on Iris Recognition Performance, World Acad. Sci. Eng. Technol. Int. J. Mech. Mechatronics Eng., vol. 8, no. 4, 2014, [Online]. Available: https://publications.waset.org/abstracts/9663/effects-of-reversible-watermarking-on-iris-recognition-performance

M. U. Celik, G. Sharma, A. M. Tekalp, and E. Saber, Lossless generalized-LSB data embedding, IEEE Trans. Image Process., vol. 14, no. 2, pp. 253–266, Feb. 2005, doi: 10.1109/TIP.2004.840686.

Jun Tian, Reversible data embedding using a difference expansion, IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 8, pp. 890–896, Aug. 2003, doi: 10.1109/TCSVT.2003.815962.

V. Sachnev, Hyoung Joong Kim, Jeho Nam, S. Suresh, and Yun Qing Shi, Reversible Watermarking Algorithm Using Sorting and Prediction, IEEE Trans. Circuits Syst. Video Technol., vol. 19, no. 7, pp. 989–999, Jul. 2009, doi: 10.1109/TCSVT.2009.2020257.

S. Weng, Y. Zhao, J.-S. Pan, and R. Ni, Reversible Watermarking Based on Invariability and Adjustment on Pixel Pairs, IEEE Signal Process. Lett., vol. 15, pp. 721–724, 2008, doi: 10.1109/LSP.2008.2001984.

Y.-C. Li, C.-M. Yeh, and C.-C. Chang, Data hiding based on the similarity between neighboring pixels with reversibility, Digit. Signal Process., vol. 20, no. 4, pp. 1116–1128, Jul. 2010, doi: 10.1016/j.dsp.2009.10.025.

B. Yang, M. Schmucker, W. Funk, C. Busch, and S. Sun, Integer DCT-based reversible watermarking for images using companding technique, Jun. 2004, p. 405. doi: 10.1117/12.527216.

S. Lee, C. D. Yoo, and T. Kalker, Reversible Image Watermarking Based on Integer-to-Integer Wavelet Transform, IEEE Trans. Inf. Forensics Secur., vol. 2, no. 3, pp. 321–330, Sep. 2007, doi: 10.1109/TIFS.2007.905146.

P. Subramanian, K. N. Krishna, R. M. Sebastian, and N. U. RAHMAN, Mukti-Biometric Systems, Int. J. Chem. Sci., vol. 14, no. S3, pp. 805–808, 2016, [Online]. Available: https://www.tsijournals.com/articles/multibiometric-systems.pdf

Tuan Hoang, Dat Tran, and D. Sharma, Remote multimodal biometric authentication using bit priority-based fragile watermarking, in 2008 19th International Conference on Pattern Recognition, Dec. 2008, pp. 1–4. doi: 10.1109/ICPR.2008.4761869.

M. Vatsa, R. Singh, and A. Noore, Feature based RDWT watermarking for multimodal biometric system, Image Vis. Comput., vol. 27, no. 3, pp. 293–304, Feb. 2009, doi: 10.1016/j.imavis.2007.05.003.

R. Thabit, Multi-Biometric Watermarking Scheme Based on Interactive Segmentation Process, Period. Polytech. Electr. Eng. Comput. Sci., vol. 63, no. 4, pp. 263–273, Sep. 2019, doi: 10.3311/PPee.14219.

R. Thabit and B. E. Khoo, Medical image authentication using SLT and IWT schemes, Multimed. Tools Appl., vol. 76, no. 1, pp. 309–332, 2017, doi: 10.1007/s11042-015-3055-x.

R. Thabit, Review of medical image authentication techniques and their recent trends, Multimed. Tools Appl., vol. 80, no. 9, pp. 13439–13473, 2021, doi: 10.1007/s11042-020-10421-7.

N. A. Memon and A. Alzahrani, Prediction-based Reversible Watermarking of CT Scan Images for Content Authentication and Copyright Protection, IEEE Access, p. 1, 2020, doi: 10.1109/ACCESS.2020.2989175.

Y. Kortli, M. Jridi, A. Al Falou, and M. Atri, Face Recognition Systems: A Survey, Sensors, vol. 20, no. 2, p. 342, Jan. 2020, doi: 10.3390/s20020342.

E. Fourati, W. Elloumi, and A. Chetouani, Anti-spoofing in face recognition-based biometric authentication using Image Quality Assessment, Multimed. Tools Appl., vol. 79, pp. 865–889, 2019.

Z. Akhtar, D. Dasgupta, and B. Banerjee, Face Authenticity: An Overview of Face Manipulation Generation, Detection and Recognition, SSRN Electron. J., 2019.

M. Aljanabi et al., Cloud Computing Issues, Challenges, and Needs: A Survey, JOIV Int. J. Informatics Vis., vol. 5, no. 3, pp. 298–305, Sep. 2021, doi: 10.30630/joiv.5.3.671.

S. Li, X. Chen, Z. Wang, Z. Qian, and X. Zhang, Data Hiding in Iris Image for Privacy Protection, IETE Tech. Rev., vol. 35, no. sup1, pp. 34–41, Dec. 2018, doi: 10.1080/02564602.2018.1520153.

Y. W. Lee and K. R. Park, Recent Iris and Ocular Recognition Methods in High- and Low-Resolution Images: A Survey, Mathematics, vol. 10, no. 12, p. 2063, Jun. 2022, doi: 10.3390/math10122063.

A. Narayanan, Fingerprint Minutiae Extraction. MATLAB Central File Exchange, 2022. [Online]. Available: https://www.mathworks.com/matlabcentral/fileexchange/31926-fingerprint-minutiae-extraction

R. Thabit and B. E. Khoo, Robust reversible watermarking scheme using Slantlet transform matrix, J. Syst. Softw., vol. 88, no. 1, 2014, doi: 10.1016/j.jss.2013.09.033.