Maneuvering Digital Watermarking In Face Recognition
Maneuvering Digital Watermarking In Face Recognition
|© 2021 by IJETT Journal|
|Year of Publication : 2021|
|Authors : Osama R.Shahin, Zeinab M. Abdel Azim, Ahmed I Taloba
|DOI : 10.14445/22315381/IJETT-V69I11P213|
How to Cite?
Osama R.Shahin, Zeinab M. Abdel Azim, Ahmed I Taloba, "Maneuvering Digital Watermarking In Face Recognition," International Journal of Engineering Trends and Technology, vol. 69, no. 11, pp. 104-112, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I11P213
The challenges faced in the digital world are many, which could be resolved with some biometric recognition methods. These biometric recognition methods are encompassed within watermarking technology, steganography, cryptography, and many other schemes of security. These methods assist in securing digital images with the authentication of their owner. This paper briefly contextualizes the digital watermarking technique, which is referred to as Natural Preserving Transform (NPT) and Hartley Transform, which is endeavored in the face recognition process. The non-blind extraction and quasi-blind extraction techniques are used for extracting the watermark from the image in the proposed system. This paper articulates the application of this watermarking technique employed in face recognition through watermarking of various face images to multiple backgrounds encompassing gray-scale images. Natural Preserve Transform is employed as a part of the fuzzy logic watermarking. In the proposed system, NPT is employed for encoding a logo of grayscale watermarking text or logo image to a host image located anywhere. The robustness and performance of the proposed system are experimentally tested with the help of image processing operations like image compression, noise degradation, cropping. Due to its unique feature of uniform distribution of face images, this technique is selected among other methods in digital watermarking. The system is tested for its efficiency with experimental analyses, which could be confirmed with the results of the simulation. The above system is proposed for copyright protection, authentication, and security requirements.
Digital watermarking, face recognition, Natural Preserve Transform (NPT), Hartley transform.
 Rezq, O., & EL-SAYED, A., Geometrical Approach for Face Detection and Recognition. Menoufia Journal of Electronic Engineering Research, 18(1) (2008) 38-48.
 Zhang, D. D., Automated biometrics: Technologies and systems. Springer Science & Business Media. 7 (2013).
 Akhtar, Z., Hadid, A., Nixon, M. S., Tistarelli, M., Dugelay, J. L., & Marcel, S., Biometrics: In search of identity and security (Q & A). IEEE MultiMedia, 25(3) (2018) 22-35.
 Kannan, D., & Gobi, M., Extensive research on robust digital image watermarking techniques: a review. International Journal of Signal and Imaging Systems Engineering, 8(1-2) (2015) 89-104.
 Shahin, O. R., Brain tumor detection using watershed transform. Ann. Clin. Cytol. Pathol, 4(1) (2018) 1-6.
 Shahin, O. R., Kelash, H. M., &Mahrous, G. C20. Evolutionary Algorithm for Classification of Mass Lesions and Calcifications in Mammograms Using Fourier Analysis.
 Shahin, O. R., &Attiya, G., Classification of mammograms tumors using Fourier analysis. IJCSNS, 14(2) (2014) 110.
 Shahin, O., Kelash, H., Attiya, G., & Allah, O. F., Breast cancer detection based on dynamic template matching. Wulfenia J, 20(12) (2013) 193-205.
 Shahin, O. R., Alruily, M., Alsmarah, M., &Alruwaill, M., Breast cancer detection using modified Hough transform. Biomed. Res., 29 (2018) 3188-3191.
 Shahin, O. R., Kelash, H. M., Mahrous, G., & Allah, O. S. F., A Novel CAD System for Breast Cancer Detection. Cancer Biology, 4(3) (2014) 335-340.
 Shahin, O. R., Kelash, H. M., Mahrous, G., &Faragallah, O. S. C19. Breast Mass Detection in Mammograms using Modified K-means Clustering.
 Meng, Q., Zhao, S., Huang, Z., & Zhou, F., Magface: A universal representation for face recognition and quality assessment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2021) 14225-14234.
 Geng, K., & Yin, G., Using deep learning in infrared images to enable human gesture recognition for autonomous vehicles. IEEE Access, 8 (2020) 88227-88240.
 Shahin, O. R., &Alruily, M.., Vehicle Identification using Eigenvehicles. In 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT) (2019) 1-6 IEEE.
 Gupta, S., Thakur, K., & Kumar, M.., 2D-human face recognition using SIFT and SURF descriptors of face?s feature regions. The Visual Computer, 37(3) (2021) 447-456.
 Isa, Mohd Rizal Mohd, et al. .,A Watermarking Technique to Improve the Security Level in Face Recognition Systems.., Multimedia Tools and Applications, 76(22) (2017) 23805–33. Springer Link, doi:10.1007/s11042-016-4109-4.
 Ernawan, F., &Ariatmanto, D,. Image watermarking based on integer wavelet transform-singular value decomposition with variance pixels. International Journal of Electrical and Computer Engineering, 9(3) (2019) 2185-2195.
 Wójcik, Waldemar, et al. .,Face Recognition: Issues, Methods, and Alternative Applications.., Face Recognition - Semisupervised Classification, Subspace Projection, and Evaluation Methods, (2016). www.intechopen.com, doi:10.5772/62950.
 Lynch, J., Face-off: Law enforcement use of face recognition technology (2020). Available at SSRN 3909038.
 Fahmy, M. F., et al. .,A Quasi Blind Watermark Extraction of Watermarked Natural Preserve Transform Images.., 16th IEEE International Conference on Image Processing (ICIP), (2009) 3665–68. IEEE Xplore, doi:10.1109/ICIP.2009.5414254.
 Wójtowicz, Wioletta, and Marek R. Ogiela. .,Biometric Watermarks Based on Face Recognition Methods for Authentication of Digital Images.., Security and Communication Networks, 8(9) (2015) 1672–87. Wiley Online Library, doi:https://doi.org/10.1002/sec.1114.
 Kuraparthi, S., Kollati, M., &Kora, P., Robust Optimized Discrete Wavelet Transform-Singular Value Decomposition Based Video Watermarking. Traitement du Signal, 36(6) (2019).
 Fahmy, G., et al. .,Nonblind and Quasiblind Natural Preserve Transform Watermarking.., EURASIP Journal on Advances in Signal Processing, 2010(1) (2010) 452548. Springer Link, doi:10.1155/2010/452548.
 Hurrah, N. N., Parah, S. A., Loan, N. A., Sheikh, J. A., Elhoseny, M., & Muhammad, K., Dual watermarking framework for privacy protection and content authentication of multimedia. Future generation computer Systems, 94 (2019) 654-673.
 Cybersecurity: Assessment of Robustness to Digital Attacks - CNPP. https://www.cnpp.com/eng/Conformity-assessment-and-testing/Our-Tests/Cybersecurity-Assessment-of-robustness-to-digital-attacks. Accessed. (2021).
 Tzou, Kou-Hu, and To R. Hsing. .,A Study Of The Discrete Hartley Transform For Image Compression Applications.., Architectures and Algorithms for Digital Image Processing II, vol. 0534, International Society for Optics and Photonics, (1985) 108–15. www.spiedigitallibrary.org, doi:10.1117/12.946570.
 Narendra, K. C., and S. Satyanarayana. .,Hartley Transform Based Correlation Filters for Face Recognition.., International Conference on Signal Processing and Communications (SPCOM), (2016) 1–5. IEEE Xplore, doi:10.1109/SPCOM.2016.7746699.
 Li, J., You, S., & Robles-Kelly, A.,. A frequency-domain neural network for fast image super-resolution. In 2018 International Joint Conference on Neural Networks (IJCNN) (2018) 1-8. IEEE.
 Spagnolo, G. S., Cozzella, L., &Leccese, F., Phase correlation functions: FFT vs. FHT. ACTA IMEKO, 8(1) (2019) 87-92.
 Tsiktsiris, D., Ziouzios, D., &Dasygenis, M.,. A portable image processing accelerator using FPGA. In 7th International Conference on Modern Circuits and Systems Technologies (MOCAST) (2018) 1-4. IEEE.