research-article

A block-based RDWT-SVD image watermarking method using human visual system characteristics

Authors:

Ferda Ernawan and

Muhammad Nomani KabirAuthors Info & Claims

The Visual Computer, Volume 36, Issue 1

Pages 19 - 37

https://doi.org/10.1007/s00371-018-1567-x

Published: 01 January 2020 Publication History

Abstract

With the rapid growth of internet technology, image watermarking method has become a popular copyright protection method for digital images. In this paper, we propose a watermarking method based on

4 \times 4

image blocks using redundant wavelet transform with singular value decomposition considering human visual system (HVS) characteristics expressed by entropy values. The blocks which have the lower HVS entropies are selected for embedding the watermark. The watermark is embedded by examining

U_{2, 1}

and

U_{3, 1}

components of the orthogonal matrix obtained from singular value decomposition of the redundant wavelet transformed image block where an optimal threshold value based on the trade-off between robustness and imperceptibility is used. In order to provide additional security, a binary watermark is scrambled by Arnold transform before the watermark is embedded into the host image. The proposed scheme is tested under various image processing, compression and geometrical attacks. The test results are compared to other watermarking schemes that use SVD techniques. The experimental results demonstrate that our method can achieve higher imperceptibility and robustness under different types of attacks compared to existing schemes. Our method provides high robustness especially under image processing attacks, JPEG2000 and JPEG XR attacks. It has been observed that the proposed method achieves better performance over the recent existing watermarking schemes.

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Cited By

Vaidya SMouli P(2024)Robust digital color image watermarking based on compressive sensing and DWTMultimedia Tools and Applications10.1007/s11042-023-15349-283:2(3357-3371)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15349-2
Gaur SChaudhary KGoel VBarthwal V(2023)An Adaptive Block-Based Watermarking Scheme Using RDWT-SVD and Particle Swarm OptimizationSN Computer Science10.1007/s42979-023-02136-x4:5Online publication date: 29-Aug-2023
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Jaiswal SPandey M(2023)Color Watermark Extraction Using Deep Neural Network in IWT Domain with PCA-Based Statistical Feature ReductionSN Computer Science10.1007/s42979-023-02132-14:5Online publication date: 1-Sep-2023
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A block-based RDWT-SVD image watermarking method using human visual system characteristics

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Published In

cover image The Visual Computer: International Journal of Computer Graphics

The Visual Computer: International Journal of Computer Graphics Volume 36, Issue 1

Jan 2020

225 pages

ISSN:0178-2789

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© Springer-Verlag GmbH Germany, part of Springer Nature 2018.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 January 2020

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UMP Research Grant Scheme (RDU170399).

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Cited By

Vaidya SMouli P(2024)Robust digital color image watermarking based on compressive sensing and DWTMultimedia Tools and Applications10.1007/s11042-023-15349-283:2(3357-3371)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15349-2
Gaur SChaudhary KGoel VBarthwal V(2023)An Adaptive Block-Based Watermarking Scheme Using RDWT-SVD and Particle Swarm OptimizationSN Computer Science10.1007/s42979-023-02136-x4:5Online publication date: 29-Aug-2023
https://dl.acm.org/doi/10.1007/s42979-023-02136-x
Jaiswal SPandey M(2023)Color Watermark Extraction Using Deep Neural Network in IWT Domain with PCA-Based Statistical Feature ReductionSN Computer Science10.1007/s42979-023-02132-14:5Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1007/s42979-023-02132-1
Jaiswal SPandey M(2023)Digital Watermark Extraction Using RS-KNN and RS-LDA with LWT and Statistical FeaturesSN Computer Science10.1007/s42979-023-01924-94:5Online publication date: 28-Jun-2023
https://dl.acm.org/doi/10.1007/s42979-023-01924-9
Ernawan FAriatmanto D(2023)A recent survey on image watermarking using scaling factor techniques for copyright protectionMultimedia Tools and Applications10.1007/s11042-023-14447-582:18(27123-27163)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1007/s11042-023-14447-5
Nha PThanh TPhong N(2023)An effective embedding algorithm for blind image watermarking technique based on Hessenberg decompositionApplied Intelligence10.1007/s10489-023-04903-y53:21(25467-25489)Online publication date: 8-Aug-2023
https://dl.acm.org/doi/10.1007/s10489-023-04903-y
Sinhal RAnsari I(2023)Machine learning based multipurpose medical image watermarkingNeural Computing and Applications10.1007/s00521-023-08457-535:31(23041-23062)Online publication date: 24-Mar-2023
https://dl.acm.org/doi/10.1007/s00521-023-08457-5
Zhang BWu YChen B(2022)Embedding Guided End-to-End Framework for Robust Image WatermarkingSecurity and Communication Networks10.1155/2022/72594692022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/7259469
Jie L(2022)A DWT-SVD Adaptive Digital Watermarking Algorithm Based on Chaotic EncryptionProceedings of the 2022 6th International Conference on Video and Image Processing10.1145/3579109.3579141(173-181)Online publication date: 23-Dec-2022
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Anand AKumar Singh A(2022)A Comprehensive Study of Deep Learning-based Covert CommunicationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/350836518:2s(1-19)Online publication date: 6-Oct-2022
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