Performance Analysis of Image Watermarking for Different Sub-bands Using LWT and Arnold Map

Main Article Content

Sushma Jaiswal
Manoj Kumar Pandey

Abstract

In this paper, blind image watermarking is proposed for grey-scale images using LWT and Arnold maps. A comprehensive analysis of robustness and imperceptibility for different sub-band is analyzed, and a robust sub-band against different attacks is determined for designing a system robust against intentional attacks or combined attacks. The importance of embedding a watermark in several sub-bands has been examined to increase the robustness against various image attacks while retaining a reasonable level of imperceptibility. During the study, robustness is analyzed and watched against the number of attacks such as compression attacks, noisy attacks, de-noising attacks, and geometric attacks. It is moreover seen that higher sub-bands are seen to offer good imperceptibility, and robustness performance depends on the nature of attacks. It has also been noticed that entire attacks affect the watermarked image in a different way. A standard image dataset is used to test the suggested concept, and it is discovered that sub-band 1 performs admirably for strength (robustness) and imperceptibility against different image attacks.

Article Details

How to Cite
[1]
S. Jaiswal and M. K. Pandey, “Performance Analysis of Image Watermarking for Different Sub-bands Using LWT and Arnold Map”, ECTI-CIT Transactions, vol. 17, no. 2, pp. 278–291, Jun. 2023.
Section
Research Article

References

C. J. Biermann, Handbook of Pulping and Papermaking, 2nd ed. San Diego, California, USA, Academic Press, 1996.

S. Kumar, B. K. Singh and M. Yadav, “A Recent Survey on Multimedia and Database Watermarking,” Multimedia Tools and Applications, vol. 79, pp. 20149–20197, 2020.

S. Liu, Z. Pan and H. Song, “Digital Image Watermarking Method Based on DCT and Fractal Encoding,” IET Image Process, vol. 11, pp. 815–821, 2017.

B. Lutovac, M. Dakovi ́c, S. Stankovi ́c and I. Orovi ́c, “An algorithm for robust image watermarking based on the DCT and Zernike moments,” Multimedia Tools Application, vol. 76, pp.23333-23352, 2017.

D.G. Savakar and A. Ghuli, “Non-Blind Digital Watermarking with Enhanced Image Embedding Capacity Using Meyer Wavelet Decomposition, SVD, and DFT,” Pattern Recognition and Image Analysis, vol. 27, pp. 511–517, 2017.

V. Solachidis and I. Pitas, “Circularly Symmetric Watermark Embedding in 2-D DFT Domain,” in IEEE Transactions on Image Processing, vol. 10, no. 11, pp. 1741-1753, 2001.

P. Khare and V. K. Srivastava, “A Novel Dual Image Watermarking Technique Using Homomorphic Transform and DWT,” Journal of Intelligent Systems, vol. 30, pp. 297–311, 2021.

Singh D, Singh SK (2016) DWT-SVD and DCT based robust and blind watermarking scheme for copyright protection. Multimed Tools Appl 76(11):13001–13024

S. Jia, Q. Zhou and H. Zhou, “A Novel Color Image Watermarking Scheme Based on DWT and Q.R. Decomposition,” Journal of Applied Science and Engineering, vol. 20, pp. 193–200, 2017.

S. Singh, V. S. Rathore, R. Singh and M. K. Singh, “Hybrid semi-blind image watermarking in redundant wavelet domain,” Multimedia Tools Application, vol. 76, pp. 19113–19137, 2017.

R. Thanki, A. Kothari and S. Borra, “Hybrid, blind and robust image watermark-

ing: RDWT–NSCT based secure approach for telemedicine applications,” Multimedia Tools and Applications, vol. 80, pp. 27593-27613, 2021.

F. Ernawan and M. N. Kabir, “A block-based RDWT-SVD image watermarking method using human visual system characteristics,” The Visual Computer, vol. 36, pp. 19-37, 2020.

R. Mehta, K. Gupta and A. K. Yadav, “An adap-tive framework to image watermarking based on the twin support vector regression and genetic algorithm in lifting wavelet transform domain,” 2022 Multimedia Tools and Applications, vol. 79, pp. 18657–18678, 2020.

A. K. Singh, “Robust and distortion control dual watermarking in LWT domain using DCT and error correction code for color medical image,” Multimedia Tools and Applications, vol. 78, pp. 30523–30533, 2019.

V. S. Verma, R.K. Jha and A. Ojha, “Digital watermark extraction using support vector machine with principal component analysis-based feature reduction,” Journal of Visual Communication and Image Representation, vol. 31, pp. 75–85, 2015

S. B. B. Ahmadi, G. Zhang, S. Wei and L. Boukela, “An intelligent and blind image watermarking scheme based on hybrid SVD transforms using human visual system characteristics,” The Visual Computer, vol. 37, pp. 385-409, 2021.

A. Zear and P. K. Singh, “Secure and robust color image dual watermarking based on LWTDCT-SVD,” Multimedia Applications and Services, vol. 81, pp. 26721-26738, 2022.

S. Roy and A. K. Pal, “A robust blind hybrid image watermarking scheme in RDWT-DCT domain using Arnold scrambling,” Multimedia Tools and Applications, vol. 76, pp. 3577–3616, 2017.

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, pp. 17027–17049, 2019.

M. Islam, A. Roy and R. H. Laskar, “SVM-based robust image watermarking technique in LWT domain using different sub-bands,” Neural Computing and Applications, vol. 32, pp. 1379–1403, 2020.

X. Zhou, C. Cao, J. Ma and L. Wang, “Adaptive Digital Watermarking Scheme Based on Support Vector Machines and Optimized Genetic Algorithm,” Hindawi, Mathematical Problems in Engineering, Article ID 2685739, 9 pages, 2018.

K. Ramanjaneyulu and K. Rajarajeswari, “Wavelet-based oblivious image watermarking scheme using genetic algorithm,” IET Image Processing, vol. 6, no. 4, pp. 364-373, 2012.

D. Ariatmanto and F. Ernawan, “Adaptive scaling factors based on the impact of selected DCT coefficients for image watermarking,” Journal of King Saud University Computer and Information Sciences, vol. 34, no. 3, 605-614, 2022.

L. Rakhmawati, W. Wirawan, S. Suwadi, C. Delpha and P. Duhamel, “Blind robust image watermarking based on adaptive embedding strength and distribution of quantified coefficients,” Expert Systems with Applications, vol. 187, 115906,

S. Qingtang, D. Liu and Y. Sun, “A robust adaptive blind color image is watermarking for resisting geometric attacks,” Information Sciences, vol. 606, pp. 194-212, 2022.

X. Wang, F. Peng, P. Niu and H. Yang, “Statistical image watermark decoder using NSM-HMT in NSCT-FGPCET magnitude domain,” Journal of Information Security and Applications, vol. 69, 103312, 2022.

J.-M. Guo and S. Seshathiri, “Watermarking in dot-diffusion halftones using adaptive classmatrix and error diffusion,” ECTI-CIT Transactions, vol. 13, no. 1, pp. 1–8, Jun. 2019.

L.Y.Hsu,H.T.HuandH.H.Chou,“Ahighcapacity QRD-based blind color image watermarking algorithm incorporated with A.I. technologies,” Expert Systems with Applications, vol. 199, 117134, 2022.

C. Song, S. Sudirman and M. Merabti, “A robust region-adaptive dual image watermarking technique,” Journal of Visual Communication and Image Representation vol. 23, no. 3, pp. 549–568, 2012.

V. S. Verma, R. K.Jha and A. Ojha, “Significant region-based robust watermarking scheme in lifting wavelet transform domain,” Expert Systems with Application, vol. 42, no. 21, pp. 8184–8197, 2015.

M. Talbi and M. S. Bouhlel, “Secure Image Watermarking Based on LWT and SVD,” International Journal of Image and Graphics, vol. 18, no. 4, 1850021, 25 pages, 2018.

C. -C. Lai and C. -C. Tsai, “Digital Image Watermarking Using Discrete Wavelet Transform and Singular Value Decomposition,” in IEEE Transactions on Instrumentation and Measurement, vol. 59, no. 11, pp. 3060-3063, Nov. 2010.

V. S. Verma and R.K. Jha, “Improved watermarking technique based on significant difference of lifting wavelet coefficients,” Signal, Image and Video Processing, vol. 9, pp. 1443–1450, 2015.

http://www.imageprocessingplace.com/root_files_V3/image_databases.htm (accessed Dec, 2021).

M. Islam and R.H.Laskar, “Geometric distortion correction based robust watermarking scheme in LWT-SVD domain with digital watermark ex-Performance Analysis of Image Watermarking for Different Sub-bands Using LWT and Arnold Map 291 extraction using SVM,” Multimedia Tools Application vol. 77, pp. 14407–14434, 2018.