Digital image watermarking using DWT-SVD with enhanced tunicate swarm optimization algorithm

Image watermarking is the vital process in leveraging digital rights management for valuable intellectual properties. Recently, large number of works was found out to build the image watermarking efficiency in order to cater to the security demands of content protection. However, it is essential to...

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Published inMultimedia tools and applications Vol. 82; no. 18; pp. 28259 - 28279
Main Authors Kumari, Ms R. Radha, Kumar, V. Vijaya, Naidu, K. Rama
Format Journal Article
LanguageEnglish
Published New York Springer US 01.07.2023
Springer Nature B.V
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ISSN1380-7501
1573-7721
DOI10.1007/s11042-023-14618-4

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Summary:Image watermarking is the vital process in leveraging digital rights management for valuable intellectual properties. Recently, large number of works was found out to build the image watermarking efficiency in order to cater to the security demands of content protection. However, it is essential to take into account the robustness of watermarking methods. Therefore, robust image watermarking techniques are introduced to show robustness against various attacks and other issues in watermarking. In this paper, a digital image watermarking technique based on Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) is proposed. The DWT and SVD methods are hybridized to enhance the embedding capacity. Based on the low variance calculation, optimal image blocks are selected by the Enhanced Tunicate Swarm Algorithm. TSA optimization is enhanced by the optimization of Sine Cosine Algorithm which is used to update the optimal solution of low variance value. Before embedding, the watermark is scrambled by Arnold scrambling and Tent Map chaotic encryption for double layer security to the watermark image. After embedding, the extraction process is carried out. The experiments are conducted on Pepper, Barbara, Lena, Baboon and Airplane image sets. The results are compared with different watermarking approaches and optimization approaches in terms of Peak Signal to Noise ratio (PSNR), Normalization Correlation (NC), and Structural Similarity Index Measure (SSIM). To show the robustness, the proposed method is evaluated under various geometric attacks and found to achieve better accuracy and robustness compared to existing methods.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-023-14618-4