Optimizing Data Embedding Through Coati-inspired Discrete Fourier Transform Techniques
Keywords:
Bit Error Rate, Cloud Data Embedding, Coati Optimization Algorithm (COA), Discrete Fourier Transform, Resilient Watermarking, Scalable Storage, Visual Information Integrity, Water markingAbstract
Information may be stored and retrieved efficiently because to cloud data embedding. Cloud platforms offer adaptable and scalable storage options, making it simple for customers to store and retrieve massive amounts of data as needed. Because of its scalability, businesses may adjust to their evolving data needs without being constrained by physical storage limits. It is mutual practice to manually alter the key limits used by data embedding approaches in an experimental form according to the application scenario and the picture. However, this can be a hassle and not work well in real-world situations. One optimisation technique that has recently been employed to increase performance is robust watermarking, which allows the critical operation parameter to be autonomously adjusted. Improved resilient watermarking approach in discrete Fourier transform via feast spectrum is projected in this research using the Coati optimisation algorithm (COA) in combination with visual info integrity and bit correct rate requirements. In particular, it optimises the watermark strength factor, number of bands, and frequency coefficients.
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Ghosh, A. M., & Grolinger, K. (2020). Edge-cloud computing for Internet of Things data analytics: Embedding intelligence in the edge with deep learning. IEEE Transactions on Industrial Informatics, 17(3), 2191-2200.
Thakkar, H. K., Dehury, C. K., & Sahoo, P. K. (2020). MUVINE: Multi-stage virtual network embedding in cloud data centers using reinforcement learning-based predictions. IEEE Journal on Selected Areas in Communications, 38(6), 1058-1074.
Zhang, W., Wang, D., Yu, S., He, H., & Wang, Y. (2021). Repeatable multi-dimensional virtual network embedding in cloud service platform. IEEE Transactions on Services Computing, 15(6), 3499-3512.
Dehury, C. K., & Sahoo, P. K. (2019). DYVINE: Fitness-based dynamic virtual network embedding in cloud computing. IEEE Journal on Selected Areas in Communications, 37(5), 1029-1045.
Huang, R., Xu, Y., Hong, D., Yao, W., Ghamisi, P., & Stilla, U. (2020). Deep point embedding for urban classification using ALS point clouds: A new perspective from local to global. ISPRS Journal of Photogrammetry and Remote Sensing, 163, 62-81.
Dai, W., Nishi, H., Vyatkin, V., Huang, V., Shi, Y., & Guan, X. (2019). Industrial edge computing: Enabling embedded intelligence. IEEE Industrial Electronics Magazine, 13(4), 48-56.
., He, H., & Wang, Y. (2021). Repeatable multi-dimensional virtual network embedding in cloud service platform. IEEE Transactions on Services Computing, 15(6), 3499-3512.
Anil, A., Shukla, V. K., & Mishra, V. P. (2020, June). Enhancing data security using digital watermarking. In 2020 International Conference on Intelligent Engineering and Management (ICIEM) (pp. 364-369). IEEE.
Fang, X., Lai, R., Zhou, Z., Chen, Z., Zheng, P., & Lu, W. (2022, July). Efficient and secure outsourced image watermarking in cloud computing. In International Conference on Artificial Intelligence and Security (pp. 526-537). Cham: Springer International Publishing.
Vasanthanayaki, C. (2020). Secure medical health care content protection system (SMCPS) with watermark detection for multi cloud computing environment. Multimedia Tools and Applications, 79, 4075-4097.
Qin, C., Qian, Z., Feng, G., & Zhang, X. (2019). Special issue on real-time image watermarking and forensics in cloud computing. Journal of Real-Time Image Processing, 16, 559-563.
Ray, A., & Roy, S. (2020). Recent trends in image watermarking techniques for copyright protection: a survey. International Journal of Multimedia Information Retrieval, 9(4), 249-270.
Kamili, A., Hurrah, N. N., Parah, S. A., Bhat, G. M., & Muhammad, K. (2020). DWFCAT: Dual watermarking framework for industrial image authentication and tamper localization. IEEE Transactions on Industrial Informatics, 17(7), 5108-5117.
Dong, X., Zhang, W., Shah, M., Wang, B., & Yu, N. (2020). Watermarking-based secure plaintext image protocols for storage, show, deletion and retrieval in the cloud. IEEE Transactions on Services Computing, 15(3), 1678-1692.
Zhang, M., Dong, J., Ren, N., & Guo, S. (2023). Lossless Watermarking Algorithm for Geographic Point Cloud Data Based on Vertical Stability. ISPRS International Journal of Geo-Information, 12(7), 294.
Zu, L., Li, H., Zhang, L., Lu, Z., Ye, J., Zhao, X., & Hu, S. (2023). E-SAWM: A Semantic Analysis-Based ODF Watermarking Algorithm for Edge Cloud Scenarios. Future Internet, 15(9), 283.
Ye, C., Tan, S., Wang, Z., Shi, B., & Shi, L. (2023). Hybridized Hierarchical Watermarking and Selective Encryption for Social Image Security. Entropy, 25(7), 1031.
Pallaw, V. K., Singh, K. U., Kumar, A., Singh, T., Swarup, C., & Goswami, A. (2023). A Robust Medical Image Watermarking Scheme Based on Nature-Inspired Optimization for Telemedicine Applications. Electronics, 12(2), 334.
El-Kenawy, E. S. M., Khodadadi, N., Khoshnaw, A., Mirjalili, S., Alhussan, A. A., Khafaga, D. S., ... & Abdelhamid, A. A. (2022). Advanced dipper-throated meta-heuristic optimization algorithm for digital image watermarking. Applied Sciences, 12(20), 10642.
Yang, Z., Sun, Q., Qi, Y., Li, S., & Ren, F. (2022). A hyper-chaotically encrypted robust digital image watermarking method with large capacity using compress sensing on a hybrid domain. Entropy, 24(10), 1486.
Cedillo-Hernandez, M., Cedillo-Hernandez, A., & Garcia-Ugalde, F. J. (2021). Improving dft-based image watermarking using particle swarm optimization algorithm. Mathematics, 9(15), 1795.
Dehghani, M., Montazeri, Z., Trojovská, E., & Trojovský, P. (2023). Coati Optimization Algorithm: A new bio-inspired metaheuristic algorithm for solving optimization problems. Knowledge-Based Systems, 259, 110011.
Jing L, Jingbing L, Jixin M et al (2019) A robust multi-watermarking algorithm for medical images based on DTCWT-DCT and Henon Map. Appl Sci 9:701–723.
Fengming Q, Jingbing L, Hui L, .et al (2020) A Robust Zero-Watermarking Algorithm for Medical Images Using Curvelet-Dct and RSA Pseudo-random Sequences. In: International Conference on Artifcial Intelligence and Security, Dublin, Ireland 179–190.
Yangxiu F, Jing L, Jingbing L,et al, (2022) Robust zero-watermarking algorithm for medical images based on SIFT and Bandelet-DCT. Multimedia Tools and Applications 81:16863–16879.
Cheng Z, Jing L, Jingbing L,et al, (2022) Multi-watermarking algorithm for medical image based on KAZE-DCT. J AMB INTEL HUM COMP 4:1–9.
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