IJETMS LANDING PAGE

International Journal of Engineering Technology and Management Sciences

2023, Volume 7 Issue 1

IMPLEMENTATION OF IMAGE AUTHENTICATION USING DIGITAL WATERMARKING WITH BIOMETRIC

AUTHOR(S)

D HARIKA, SYED NOORULLAH

DOI: https://doi.org/10.46647/ijetms.2023.v07i01.023

ABSTRACT
The rapid global development of E-commerce in terms of digitalization and distribution of digital contents in the form of image, audio, video, increases the possibility of unrestricted duplication and broadcasting of copyrighted data and the protection of crucial documents is highly significant. Digital watermarking inserts watermark into the cover or host data by unnoticeable modification. In this work digital watermarking with biometric features is done. In this work a technique to implement the hiding of an image inside another image using biometric features namely signature and fingerprint using watermarking techniques is done. To accomplish this, a hybrid watermarking scheme consisting of Discrete Wavelet Transform, Discrete Cosine Transform and Singular Value Decomposition (DWT-DCT-SVD) is proposed for image authentication that is robust against attacks. Here, singular values of watermark1 (fingerprint) and watermark2 (signature) are obtained by applying DWT-DCT-SVD. By adding both the singular values of watermarks we acquire the transformed watermark. To improve the security, robustness and provide authenticity for the image, a two-step watermarking method is demonstrated. The evaluation parameters like PSNR (Peak Signal to Noise Ratio), SSIM (Structured Similarity Index Method), normalized correlation coefficient (NCC) are used for image quality assessment.

Page No: 154 - 167

References:

1. Zhenwei Li *, Mengli Jia, “Blood Vessel Segmentation of Retinal Image Based on Dense-U-Net Network”, Micromachines 2021, 12, 1478. https://doi.org/10.3390/ mi12121478.
2. Wang, C.; Zhao, Z.; Ren, Q.; Xu, Y.; Yu, Y. Dense U-net Based on Patch-Based Learning for Retinal Vessel Segmentation. Entropy 2019, 21, 168.
3. Chen, Y. A Labeling-Free Approach to Supervising Deep Neural Networks for Retinal Blood Vessel Segmentation. arXiv 2017,arXiv:1704.07502.
4. Strisciuglio, N.; Azzopardi, G.; Vento, M.; Petkov, N. Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters. Mach. Vis. Appl. 2016, 27, 1137–1149.
5. Guo, C.; Szemenyei, M.; Pei, Y.; Yi, Y.; Zhou, W. SD-U-net: A Structured Dropout U-Net for Retinal Vessel Segmentation. In Proceedings of the IEEE 19th International Conference on Bioinformatics and Bioengineering, Athens, Greece, 28–30 October2019; pp. 439–444.
6. Alom, M.Z.; Yakopcic, C.; Hasan, M.; Taha, T.M.; Asari, V.K. Recurrent residual U-Net for medical image segmentation. J. Med.Imaging 2019, 6, 014006.
7. Wang, X.; Jiang, X. Retinal vessel segmentation by a divide-and-conquer funnel-structured classification framework. Signal Process. 2019, 165, 104–114.
8. Yan, Z.; Yang, X.; Cheng, K.T. A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation. IEEE J. Biomed. Health Inform. 2019, 23, 1427–1436.
9. Soomro, T.A.; Afifi, A.J.; Zheng, L.; Soomro, S.; Gao, J.; Hellwich, O.; Paul, M. Deep Learning Models for Retinal Blood Vessels Segmentation: A Review. IEEE Access 2019, 7, 71696–71717.
10. Suryani, E.; Susilo, M. The hybrid method of SOM artificial neural network and median thresholding for segmentation of blood vessels in the retina image fundus. Int. J. Fuzzy Log. Intell. Syst. 2019, 19, 323–331.

How to Cite This Article:
T M SOWJANYA, K PRASAD BABU. IMPLEMENTATION OF CLAHE CONTRAST ENHANCEMENT & OTSU THRESHOLDING IN RETINAL IMAGE PROCESSING. ijetms;7(1):154-167. DOI: 10.46647/ijetms.2023.v07i01.023