Robust Image Watermarking Using Lifting Wavelet Transform and Convolutional Neural Network
Keywords:
Lifting Wavelet Transform, Convolutional Neural Network, LeNet5, Adaptive Gazelle Optimization Algorithm, Peak Signal-to-Noise Ratio, Structural Similarity IndexAbstract
The research introduces an innovative approach to resilient image watermarking, where the fusion of the Lifting Wavelet Transform (LWT) and the Convolutional Neural Network (CNN) forms the cornerstone. To refine the precision and resilience of the watermarking process, the study integrates the Adaptive Gazelle Optimization Algorithm (AGOA) into the LeNet-5 model. This integration lets AGOA fine-tune important hyperparameters like learning rate, batch size, and convolution kernel number without having to worry about the risks that come with doing it by hand. Overall, AGOA's main goal is to improve accuracy during the model training phase. This will make sure that the watermark can't be seen and is resistant to many possible attacks. Through the utilization of AGOA, the research endeavors to swiftly and efficiently pinpoint an array of optimal solutions, thus optimizing the watermarking process to its fullest potential. The performance of our AGOA-Improve-LeNet5 model is evaluated using 300 test images, with an average Peak Signal-to-Noise Ratio (PSNR) of 61.074 dB. The experimental outcomes demonstrate the effectiveness of our proposed methodology in achieving robust and high-quality image watermarking.
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S. I. Hasan, J. Boaddh, and A. Shrivastava, “A survey on visual properties and techniques of digital image data hiding,” International Journal of Scientific Research & Engineering Trends., no. 5, pp. 54-158, 2019.
O. Hosam, “Attacking image watermarking and steganography-a survey,” IJCNIS., vol. 3, no. 1, pp. 23–37, 2019. DOI:10.5815/ijitcs.2019.03.03
S. I. Batool, T. Shah, and M. Khan, “A color image watermarking scheme based on affine transformation and S4permutation,” Neural Comput. Appl., vol. 25, no. 7–8, pp. 2037–2045, 2014. https://doi.org/10.1007/s00521-014-1691-0
M. W. Hatoum, J. F. Couchot, R. Couturier, and R. Daraz, "Using deep learning for image watermarking attack," Signal Process. Image Commun., vol. 90, pp. 116019, 2021. https://doi.org/10.1016/j.image.2020.116019
N. M. Makbol, and B.E. Khoo, “Robust blind image watermarking scheme based on redundant discrete wavelet transform and singular value decomposition,” AEU-Int J Electron Commun., vol. 67, no. 2, pp.102–112, 2013. https://doi.org/10.1016/j.aeue.2012.06.008
J. E. Lee, Y. H. Seo, and D. W. Kim, "Convolutional neural network-based digital image watermarking adaptive to the resolution of image and watermark," Appl. Sci., vol. 10, no. 19, pp.6854, 2020. https://doi.org/10.3390/app10196854
F. Daraee, and S. Mozaffari, “Watermarking in binary document images using fractal codes,” Pattern Recognit. Lett., vol. 35, pp. 120–129, 2014. https://doi.org/10.1016/j.patrec.2013.04.022
W., Zheng, S. Mo, X. Jin, Y. Qu, F. Deng, J. Shuai, Z. Xie, C. Zheng, and S. Long, "Robust and high capacity watermarking for image based on DWT-SVD and CNN". In 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 1233-1237, IEEE, 2018.DOI: 10.1109/ICIEA.2018.8397898
Y.S. Lee, Y.H. Seo, D.W. Kim, “Blind image watermarking based on adaptive data spreading in n-level DWT subbands,” Secur. Commun. Netw., pp. 357251, 2019. https://doi.org/10.1155/2019/8357251
H. Fırat, M. E. Asker, M. İ. Bayindir, and D. Hanbay, "Spatial-spectral classification of hyperspectral remote sensing images using 3D CNN based LeNet-5 architecture," Infrared Phys. Technol., vol. 127, pp.104470, 2022. https://doi.org/10.1016/j.infrared.2022.104470
A. M. Abdelhakim, and M. Abdelhakim, “A time-efficient optimization for robust image watermarking using machine learning,” Expert Syst Appl., 100, pp.197-210, 2018.
A. M. Abdelhakim, H. I. Saleh, and A. M. Nassar, "A quality guaranteed robust image watermarking optimization with Artificial Bee Colony," Expert Syst Appl., vol. 72, pp.317-326, 2017. https://doi.org/10.1016/j.eswa.2016.10.056
Y. Guo, B. Z. Li, and N. Goel, "Optimised blind image watermarking method based on firefly algorithm in DWT‐QR transform domain", IET Image Process., vol. 11, no. 6, pp. 406-415, 2017. https://doi.org/10.1049/iet-ipr.2016.0515
M. Moosazadeh, and G. Ekbatanifard, "An improved robust image watermarking method using DCT and YCoCg-R color space," Optik., vol. 140, pp. 975-988, 2017. https://doi.org/10.1016/j.ijleo.2017.05.011
R. Mehta, N. Rajpal, and V. P. Vishwakarma, "LWT-QR decomposition based robust and efficient image watermarking scheme using Lagrangian SVR," Multimed. Tools Appl., vol. 75, pp. 4129-4150, 2016. https://doi.org/10.1007/s11042-015-3084-5
M. Vafaei, H. Mahdavi-Nasab, and H. Pourghassem, “A new robust blind watermarking method based on neural networks in wavelet transform domain,” World Appl. Sci. J., vol. 22, no. 11, pp.1572-1580. 2013.
W. Taiyue, and L. Hongwei, “A Novel digital image watermarking algorithm based on curvelet transform,” International Journal of Digital Content Technology and its Applications, vol. 7, no. 1, pp. 512, 2013. https://doi.org/10.1155/2015/937432
X. Wang, D. Ma, K. Hu, J. Hu, and L. Du, "Mapping based residual convolution neural network for non-embedding and blind image watermarking," Journal of Information Security and Applications., vol. 59, pp. 102820, 2021. https://doi.org/10.1016/j.jisa.2021.102820
H. K. Singh, and A. K. Singh, "Digital image watermarking using deep learning," Multimed. Tools Appl., pp.1-16, 2023. https://doi.org/10.1007/s11042-023-15750-x
S. Mellimi, V. Rajput, I. A. Ansari, and C. W. Ahn, "A fast and efficient image watermarking scheme based on deep neural network," Pattern Recognit. Lett., vol. 151, pp. 222-228, 2021. https://doi.org/10.1016/j.patrec.2021.08.015
M. Begum, and M. S. Uddin, "Towards the development of an effective image watermarking system," Security and Privacy, vol. 5, no. 2, pp. e196, 2022. https://doi.org/10.1002/spy2.196
S. S. Sharma, and V. Chandrasekaran, "A robust hybrid digital watermarking technique against a powerful CNN-based adversarial attack," Multimed. Tools Appl., vol. 79, no. 43-44, pp.32769-32790, 2020. https://doi.org/10.1007/s11042-020-09555-5
J. O. Agushaka, A. E. Ezugwu, and L. Abualigah, "Gazelle Optimization Algorithm: A novel nature-inspired metaheuristic optimizer," Neural Comput. Appl., vol. 35, no. 5, pp.099-4131, 2023. https://doi.org/10.1007/s00521-022-07854-6
M. Islam, A. Roy, and R. H. Laskar, "SVM-based robust image watermarking technique in LWT domain using different sub-bands," Neural Comput. Appl., vol. 32, pp.1379-1403, 2020. https://doi.org/10.1007/s00521-018-3647-2
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