Optimized DWT Based Digital Image Watermarking and Extraction Using RNN-LSTM.

R. Radha Kumari; V. Vijaya Kumar; K. Rama Naidu

doi:10.9781/ijimai.2021.10.006

Authors

R. Radha Kumari Jawaharlal Nehru Technological University Anantapur
V. Vijaya Kumar Dean, Department of CSE & IT and Director CACR, Anurag Group of Institutions (India).
K. Rama Naidu Jawaharlal Nehru Technological University Anantapur

DOI:

https://doi.org/10.9781/ijimai.2021.10.006

Keywords:

Discrete Wavelet Transforms, Recurrent Network, Long Short Term Memory (LSTM), Simulated Annealing, Tunicate Swarm Algorithm, Watermarking

Abstract

The rapid growth of Internet and the fast emergence of multi-media applications over the past decades have led to new problems such as illegal copying, digital plagiarism, distribution and use of copyrighted digital data. Watermarking digital data for copyright protection is a current need of the community. For embedding watermarks, robust algorithms in die media will resolve copyright infringements. Therefore, to enhance the robustness, optimization techniques and deep neural network concepts are utilized. In this paper, the optimized Discrete Wavelet Transform (DWT) is utilized for embedding the watermark. The optimization algorithm is a combination of Simulated Annealing (SA) and Tunicate Swarm Algorithm (TSA). After performing the embedding process, the extraction is processed by deep neural network concept of Recurrent Neural Network based Long Short-Term Memory (RNN-LSTM). From the extraction process, the original image is obtained by this RNN-LSTM method. The experimental set up is carried out in the MATLAB platform. The performance metrics of PSNR, NC and SSIM are determined and compared with existing optimization and machine learning approaches. The results are achieved under various attacks to show the robustness of the proposed work.

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References

I. Hamamoto, M. Kawamura. “Image watermarking technique using embedder and extractor neural networks.” IEICE TRANSACTIONS on Information and Systems, Vol.102, No. 1, January 2019, pp. 19-30. DOI: https://doi.org/10.1587/transinf.2018MUP0006

X. Zhou, H. Zhang, and C. Wang. “A robust image watermarking technique based on DWT, APDCBT, and SVD.” Symmetry, Vol .10, No. 3, March 2018, pp. 77. DOI: https://doi.org/10.3390/sym10030077

Z. Renjie, X. Zhang, M. Shi, and Z. Tang. “Secure neural network watermarking protocol against forging attack.” EURASIP Journal on Image and Video Processing, September 2020, pp. 1-12. DOI: https://doi.org/10.1186/s13640-020-00527-1

S.P. Ambadekar, J. Jain, and J. Khanapuri. “Digital image watermarking through encryption and DWT for copyright protection.” In Recent Trends in Signal and Image Processing, Vol. 727, May 2018, pp. 187-195.

T.K Araghi, A. A Manaf. “An enhanced hybrid image watermarking scheme for security of medical and non-medical images based on DWT and 2-D SVD.” Future Generation Computer Systems Vol. 101, December 2019, pp. 1223-1246. DOI: 10.1016/j.future.2019.07.064.

M. Ali, and C. W. Ahn. “An optimal image watermarking approach through cuckoo search algorithm in wavelet domain.” International Journal of System Assurance Engineering and Management, Vol. 9, No. 3, June 2018, pp. 602-611. DOI: 10.1007/s13198-014-0288-4.

D. Rajani, and P. Rajesh Kumar. “An optimized blind watermarking scheme based on principal component analysis in redundant discrete wavelet domain.” Signal Processing, Vol. 172, July 2020, pp. 107556. DOI: https://doi.org/10.1016/j.sigpro.2020.107556

X. Kang, F. Zhao, G. Lin, and Y. Chen. “A novel hybrid of DCT and SVD in DWT domain for robust and invisible blind image watermarking with optimal embedding strength.” Multimedia Tools and Applications, Vol. 77, No. 11, July2017, pp. 13197-13224. DOI: https://doi.org/10.1007/s11042-017-4941-1.

P. Kadian, N. Arora, and S. M. Arora. “Performance Evaluation of Robust Watermarking Using DWT-SVD and RDWT-SVD.” In 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN) IEEE, May 2019, pp. 987-991.

A.K. Abdulrahman, and S. Ozturk. “A novel hybrid DCT and DWT based robust watermarking algorithm for color images.” Multimedia Tools and Applications, Vol. 78, no. 12, January 2019, pp. 17027-17049, DOI: https://doi.org/10.1007/s11042-018-7085-z

F.N. Thakkar, and V. K. Srivastava. “Performance comparison of recent optimization algorithm Jaya with particle swarm optimization for digital image watermarking in complex wavelet domain.” Multidimensional Systems and Signal Processing, Vol. 30, no. 4, 2019, pp. 1769-1791.

M. Ahmadi, A. Norouzi, N. Karimi, S. Samavi, and A. Emami. “ReDMark: Framework for residual diffusion watermarking based on deep networks.” Expert Systems with Applications, Vol. 146, May 2020 pp.113157.

F. López, L. de la Fuente Valentín, and I. S. M. de Mendivil. “Detecting image brush editing using the discarded coefficients and intentions.” International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 5, no. 5, 2019, pp. 15-21.

N. Saleem, and M. I. Khattak. “Deep Neural Networks for Speech Enhancement in Complex-Noisy Environments.” International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 6, no. 1, 2020, pp. 84-90.

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.” Applied Sciences, Vol.10, no. 19, September 2020, pp. 6854. https://doi.org/10.3390/app1019685

Y. Nagai, Y. Uchida, S. Sakazawa, and S. Satoh. “Digital watermarking for deep neural networks.” International Journal of Multimedia Information Retrieval, Vol. 7, no. 1, February 2018, pp. 3-16. DOI: https://doi.org/10.1007/s13735-018-0147-1

F. Wang, X. Liu, G. Deng, X. Yu, H. Li, and Q. Han. “Remaining life prediction method for rolling bearing based on the long short-term memory network.” Neural Processing Letters, Vol. 50, no. 3, March 2019, pp. 2437-2454. DOI: 10.1007/s11063-019-10016-w.

T.K. Araghi, and A. A. Manaf. “An enhanced hybrid image watermarking scheme for security of medical and non-medical images based on DWT and 2-D SVD.” Future Generation Computer Systems Vol. 101, December 2019, pp. 1223-1246. DOI: 10.1016/j.future.2019.07.064.

S.S. Alotaibi, “Optimization insisted watermarking model: hybrid firefly and Jaya algorithm for video copyright protection.” Soft Computing, Vol. 24, March 2020, pp. 14809-14823. DOI: https://doi.org/10.1007/s00500-020-04833-8

P. Garg, and R.R. Kishore. “Optimized color image watermarking through watermark strength optimization using particle swarm optimization technique.” Journal of Information and Optimization Sciences Vol. 41, No.6, 2020, pp. 1-14.

F. Kahlessenane, A. Khaldi, R. Kafi, and S. Euschi. “A DWT based watermarking approach for medical image protection.” Journal of Ambient Intelligence and Humanized Computing, August 2020, pp. 1-8.

J. Liu, Huang, Y. Luo, L. Cao, S. Yang, D. Wei, and R. Zhou. “An optimized image watermarking method based on HD and SVD in DWT domain.” IEEE Access, Vol. 7, May 2019, pp. 80849-80860. DOI: 10.1109/ACCESS.2019.2915596.

A. Ashima, and A.K. Singh. “An improved DWT-SVD domain watermarking for medical information security.” Computer Communications Vol. 152, February 2020, pp. 72-80. DOI: https://doi.org/10.1016/j.comcom.2020.01.038.

R.R. Sunesh Kishore, and A. Saini. “Optimized image watermarking with artificial neural networks and histogram shape.” Journal of Information and Optimization Sciences, Vol . 44, No. 7, September 2020, pp. 1597-1613.

Y. Guo, B-Z. Li, and N. Goel. ”Optimised blind image watermarking method based on firefly algorithm in DWT-QR transform domain.” IET Image processing, Vol. 11, No. 6, June 2017, pp. 406-415. DOI: 10.1049/ietipr.2016.0515.

R. Rajeev, J. Abdul Samath, and N. K. Karthikeyan. “An Intelligent Recurrent Neural Network with Long Short-Term Memory (LSTM) BASED Batch Normalization for Medical Image Denoising.” Journal of medical systems, Vol. 43, No. 8, June 2019, pp. 234. https://doi.org/10.1007/s10916-019-1371-9

M.F. Kazemi, M. A. Pourmina, and A. H. Mazinan. “Analysis of watermarking framework for color image through a neural network-based approach.” Complex & Intelligent Systems, Vol. 6, January 2020, pp. 213-220. https://doi.org/10.1007/s40747-020-00129-4

N. Leite, F. Melício, and A.C. Rosa. “A fast simulated annealing algorithm for the examination timetabling problem.” Expert Systems with Applications, Vol. 122, May 2019, pp. 137-151. DOI: https://doi.org/10.1016/j.eswa.2018.12.048

S. Kaur, L.K. Awasthi, A. L. Sangal, and G. Dhiman. “Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization.” Engineering Applications of Artificial Intelligence Vol. 90, pp. 103541. https://doi.org/10.1016/j.engappai.2020.103541

W. Ding, Y. Ming, Z. Cao, and C.T. Lin. “A Generalized Deep Neural Network Approach for Digital Watermarking Analysis. “IEEE Transactions on Emerging Topics in Computational Intelligence. 2021.

S. Hochreiter and J. Schmidhuber. “Long short-term memory”. Neural computation, Vol. 9, no. 8, 1997, pp. 1735-1780.

H.W. Makram, J-F. Couchot, R. Couturier, and R. Darazi. “Using Deep learning for image watermarking attack.” Signal Processing: Image Communication, Vol. 90, October 2020, pp. 116019. https://doi.org/10.1016/j.image.2020.116019.