1
Department(Computer Science &Engineering ) Shobhit Institute of Engineering and Technology Meerut, Uttar Pradesh, India
(ashishdixit1984@gmail.com)
2
Department(Computer Science &Engineering ) Shobhit Institute of Engineering and Technology Meerut, Uttar Pradesh, India
(prajanag@gmail.com)
3
Department (Computer Application ) Ajay Kumar Garg Engineering College, Ghaziabad (AKTU, Lucknow ) Ghaziabad, Uttar Pradesh, India
(bksharma888@yahoo.com)
4
Department of Computer Science and Engineering, Parul Institute of Technology, Gujarat,India; IEEE Senior Member, Parul University, India
(aditi.sharma@ieee.org)
Abstract :
Image watermarking preserves digital content. This study introduces a new watermarking approach employing Sub-Band Discrete Cosine Transform and Deep neural networks, GRNN and CNN. The method embeds robust, invisible watermarks in greyscale photos and compares the two neural network topologies. The watermark is added using sub-band DCT. Watermark embedding in low-frequency sub-bands resists photo processing. The binary watermark modifies sub-band DCT coefficients to determine embedding intensity, resisting signal deterioration, and assaults. GRNN and CNN neural networks extract watermarks accurately. CNN extracts hierarchical features from images, enabling robust watermark recovery even under distortions, whereas non-parametric GRNN stores the whole training data to create predictions. The watermarking approach is tested on several greyscale photos. PSNR, SSIM, MSE, and NCC measure performance. The watermark tests noise addition, compression, and filtering. Compare GRNN and CNN's watermark extraction strengths and shortcomings to assess image watermarking suitability.
Keywords :
Convolution Neural Network; General Regression Neural Network; Normalized Correlation; Discrete Cosine Transform.
References :
[1] Langelaar, G. C., Setyawan, I., & Lagendijk, R. L. (2000). Watermarking digital image and video data. A state-of-the-art overview. IEEE Signal processing magazine, 17(5), 20-46.
[2] Moulin, P. and Mincak, M. (2002) ‘A framework for evaluating the data-hiding capacity of image sources’, IEEE Transactions on Image Processing, Vol. 11, No. 9, pp.1029–1042.
[3] Cox, I. J., Kilian, J., Leighton, F. T., & Shamoon, T. (1997). Secure spread spectrum watermarking for multimedia. IEEE transactions on image processing, 6(12), 1673-1687.
[4] Celik, M., Sharma, G., Saber, E. and Tekalp, A.M. (2002) ‘Hierarchical watermarking for secure image authentication with localization’, IEEE Transaction on Image Processing, Vol. 11, No. 6, pp.585–595.
[5] Laskar, R. H., Choudhury, M., Chakraborty, K., & Chakraborty, S. (2011). A joint DWT-DCT-based robust digital watermarking algorithm for ownership verification of digital images. In Computer Networks and Intelligent Computing: 5th International Conference on Information Processing, ICIP 2011, Bangalore, India, August 5-7, 2011. Proceedings (pp. 482-491). Springer Berlin Heidelberg.
[6] V. Goar, A. Sharma, N. S. Yadav, S. Chowdhury and Y.-C. Hu, "IOT-based smart mask protection against the waves of covid-19", Journal of Ambient Intelligence and Humanized Computing, 2022.
[7] Yavuz, E., & Telatar, Z. (2006, September). SVD adapted DCT domain DC subband image watermarking against watermark ambiguity. In International Workshop on Multimedia Content Representation, Classification and Security (pp. 66-73). Berlin, Heidelberg: Springer Berlin Heidelberg
[8] Shen, R. M., Fu, Y. G., & Lu, H. T. (2005). A novel image watermarking scheme based on support vector regression. Journal of Systems and Software, 78(1), 1-8.
[9] Huang, S., & Zhang, W. (2009, May). Digital watermarking based on neural network and image features. In 2009 Second International Conference on Information and Computing Science (Vol. 2, pp. 238-240). IEEE.
[10] Tang, G., & Liao, X. (2004). A neural network-based blind watermarking scheme for digital images. In Advances in Neural Networks-ISNN 2004: International Symposium on Neural Networks, Dalian, China, August 19-21, 2004, Proceedings, Part II 1 (pp. 645-650). Springer Berlin Heidelberg.
[11] R. Dash, T. N. Nguyen, K. Cengiz and A. Sharma, "FTSVR: Fine-tuned support vector regression model for stock predictions", Neural Computing and Applications, 2021, [online] Available: https://10.1007/s00521-021-05842-w.
[12] S. Samanta, A. Sarkar and A. Sharma, "Networking Technologies and challenges for green IOT applications in urban climate", Machine Learning and Data Science, pp. 169-184, 2022.
[13] Singh, P. K. (2022). Robust and imperceptible image watermarking technique based on SVD, DCT, BEMD, and PSO in wavelet domain. Multimedia Tools and Applications, 81(16), 22001-22026.
[14] A. K. Vashishtha, M. Sharma and A. Sharma, "Mechanism Incorporating Secure Mutual Validation and Key Spreading Organization in Intelligent Transport System," 2022 International Conference on Fourth Industrial Revolution Based Technology and Practices (ICFIRTP), Uttarakhand, India, 2022, pp. 219-225, doi: 10.1109/ICFIRTP56122.2022.10059443.
[15] Saqib, M., & Naaz, S. (2017). Spatial and frequency domain digital image watermarking techniques for copyright protection. Int. J. Eng. Sci. Technol.(IJEST), 9(6), 691-699.
[16] Ko, L. T., Chen, J. E., Hsin, H. C., Shieh, Y. S., & Sung, T. Y. (2012). A unified algorithm for subband-based discrete cosine transform. Mathematical Problems in Engineering, 2012.
[17] Mehta, R., Rajpal, N., & Vishwakarma, V. P. (2015). Sub-band discrete cosine transform-based greyscale image watermarking using general regression neural network. International Journal of Signal and Imaging Systems Engineering, 8(6), 380-389.
[18] G. Sonowal, A. Sharma and L. Kharb, "Spear-phishing emails verification method based on verifiable secret sharing scheme", Journal of Information Assurance & Security, vol. 16, no. 3, pp. 117-124, 2021.
[19] Dixit, A., Agarwal, R. P., & Sharma, B. K. (2023, May). Hybridization of Discrete Cosine Transform and Principal Component Analysis to Achieve Digital Watermarking. In 2023 International Conference on Disruptive Technologies (ICDT) (pp. 527-530). IEEE.
[20] V. Goar, A. Sharma and D. Chahal, "Android Asset Packaging Tool based Forensics Security and Predictive Analysis", Journal of Information Assurance & Security, vol. 16, no. 3, pp. 124-131, 2021.
[21] Mehta, R. and Rajpal, N. (2013) ‘General regression neural network based image watermarking using fractional DCT-II transform’, Proceedings of the IEEE 2nd International Conference on Image Information Processing (ICIIP), 9–11 December, Shimla, Himachal Pradesh, India, pp.340–345.
[22] Singh, A. K., Dave, M., & Mohan, A. (2014). Hybrid technique for robust and imperceptible dual watermarking using error correcting codes for application in telemedicine. International Journal of Electronic Security and Digital Forensics, 6(4), 285-305.
[23] Begum, M., & Uddin, M. S. (2020). Digital image watermarking techniques: a review. Information, 11(2), 110.
[24] Latif, A., Naghsh-Nilchi, A. R., & Monadjemi, S. A. (2010). A parametric slant-Hadamard system for robust image watermarking. Journal of Circuits, Systems, and Computers, 19(02), 451-477.
[25] Sung, T. Y., Shieh, Y. S., & Hsin, H. C. (2010). An efficient VLSI linear array for DCT/IDCT using subband decomposition algorithm. Mathematical Problems in Engineering, 2010.
[26] Gafurov, A., Mukharamova, S., Saveliev, A., & Yermolaev, O. (2023). Advancing Agricultural Crop Recognition: The Application of LSTM Networks and Spatial Generalization in Satellite Data Analysis. Agriculture, 13(9), 1672.
| Style | # |
|---|---|
| MLA | Ashish Dixit, R. P. Aggarwal, B. K. Sharma, Aditi Sharma. "Safeguarding Digital Essence: A Sub-band DCT Neural Watermarking Paradigm Leveraging GRNN and CNN for Unyielding Image Protection and Identification." Journal of Intelligent Systems and Internet of Things, Vol. 10, No. 1, 2023 ,PP. 33-47 (Doi : https://doi.org/10.54216/JISIoT.100103) |
| APA | Ashish Dixit, R. P. Aggarwal, B. K. Sharma, Aditi Sharma. (2023). Safeguarding Digital Essence: A Sub-band DCT Neural Watermarking Paradigm Leveraging GRNN and CNN for Unyielding Image Protection and Identification. Journal of Journal of Intelligent Systems and Internet of Things, 10 ( 1 ), 33-47 (Doi : https://doi.org/10.54216/JISIoT.100103) |
| Chicago | Ashish Dixit, R. P. Aggarwal, B. K. Sharma, Aditi Sharma. "Safeguarding Digital Essence: A Sub-band DCT Neural Watermarking Paradigm Leveraging GRNN and CNN for Unyielding Image Protection and Identification." Journal of Journal of Intelligent Systems and Internet of Things, 10 no. 1 (2023): 33-47 (Doi : https://doi.org/10.54216/JISIoT.100103) |
| Harvard | Ashish Dixit, R. P. Aggarwal, B. K. Sharma, Aditi Sharma. (2023). Safeguarding Digital Essence: A Sub-band DCT Neural Watermarking Paradigm Leveraging GRNN and CNN for Unyielding Image Protection and Identification. Journal of Journal of Intelligent Systems and Internet of Things, 10 ( 1 ), 33-47 (Doi : https://doi.org/10.54216/JISIoT.100103) |
| Vancouver | Ashish Dixit, R. P. Aggarwal, B. K. Sharma, Aditi Sharma. Safeguarding Digital Essence: A Sub-band DCT Neural Watermarking Paradigm Leveraging GRNN and CNN for Unyielding Image Protection and Identification. Journal of Journal of Intelligent Systems and Internet of Things, (2023); 10 ( 1 ): 33-47 (Doi : https://doi.org/10.54216/JISIoT.100103) |
| IEEE | Ashish Dixit, R. P. Aggarwal, B. K. Sharma, Aditi Sharma, Safeguarding Digital Essence: A Sub-band DCT Neural Watermarking Paradigm Leveraging GRNN and CNN for Unyielding Image Protection and Identification, Journal of Journal of Intelligent Systems and Internet of Things, Vol. 10 , No. 1 , (2023) : 33-47 (Doi : https://doi.org/10.54216/JISIoT.100103) |