Quality Improvement for Invisible Watermarking using Singular Value Decomposition and Discrete Cosine Transform
DOI:
https://doi.org/10.30812/matrik.v23i3.3744Keywords:
Discrete Cosine Transform, Invisible Watermarking, Quality Improvement, Singular Value DecompositionAbstract
Image watermarking is a sophisticated method often used to assert ownership and ensure the integrity of digital images. This research aimed to propose and evaluate an advanced watermarking technique that utilizes a combination of singular value decomposition methodology and discrete cosine transformation to embed the Dian Nuswantoro University symbol as proof of ownership into digital images. Specific goals included optimizing the embedding process to ensure high fidelity of the embedded watermark and evaluating the fuzziness of the watermark to maintain the visual quality of the watermarked image. The methods used in this research were singular value decomposition and discrete cosine transformation, which are implemented because of their complementary strengths. Singular value decomposition offers robustness and stability, while discrete cosine transformation provides efficient frequency domain transformation, thereby increasing the overall effectiveness of the watermarking process. The results of this study showed the efficacy of the Lena image technique in gray scale having a mean square error of 0.0001, a high peak signal-to-noise ratio of 89.13 decibels (dB), a universal quality index of 0.9945, and a similarity index structural of 0.999. These findings confirmed that the proposed approach maintains image quality while providing watermarking resistance. In conclusion, this research contributed a new watermarking technique designed to verify institutional ownership in digital images, specifically benefiting Dian Nuswantoro University. It showed significant potential for wider application in digital rights management.
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References
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[20] U. Sara, M. Akter, and M. S. Uddin, “Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study,†Journal of Computer and Communications, vol. 07, no. 03, pp. 8–18, 2019, doi: 10.4236/jcc.2019.73002.
[2] D. Raijada, K. Wac, E. Greisen, J. Rantanen, and N. Genina, “Integration of personalized drug delivery systems into digital health,†Adv Drug Deliv Rev, vol. 176, pp. 113857–113857, Sep. 2021, doi: 10.1016/j.addr.2021.113857.
[3] P. Aberna and L. Agilandeeswari, “Digital image and video watermarking: methodologies, attacks, applications, and future directions,†Multimed Tools Appl, vol. 83, no. 2, pp. 5531–5591, Jan. 2024, doi: 10.1007/s11042-023-15806-y.
[4] S. Gupta, K. Saluja, V. Solanki, K. Kaur, P. Singla, and M. Shahid, “Efficient methods for digital image watermarking and information embedding,†Measurement: Sensors, vol. 24, p. 100520, Dec. 2022, doi: 10.1016/j.measen.2022.100520.
[5] J. Patel, D. Tailor, K. Panchal, S. Patel, R. Gupta, and M. Shah, “All phase discrete cosine biorthogonal transform versus discrete cosine transform in digital watermarking,†Multimed Tools Appl, vol. 83, no. 6, pp. 16121–16138, Jul. 2023, doi: 10.1007/s11042-023-16106-1.
[6] Z. Yuan, Q. Su, D. Liu, and X. Zhang, “A blind image watermarking scheme combining spatial domain and frequency domain,†Visual Computer, vol. 37, no. 7, pp. 1867–1881, Jul. 2021, doi: 10.1007/s00371-020-01945-y.
[7] F. Yasmeen and M. S. Uddin, “An Efficient Watermarking Approach Based on LL and HH Edges of DWT–SVD,†SN Comput Sci, vol. 2, no. 2, pp. 1–16, Apr. 2021, doi: 10.1007/s42979-021-00478-y.
[8] A. Zear and P. K. Singh, “Secure and robust color image dual watermarking based on LWT-DCT-SVD,†Multimed Tools Appl, vol. 81, no. 19, pp. 26721–26738, Aug. 2022, doi: 10.1007/s11042-020-10472-w.
[9] A. Ray and S. Roy, “Recent trends in image watermarking techniques for copyright protection: a survey,†Int J Multimed Inf Retr, vol. 9, no. 4, pp. 249–270, Dec. 2020, doi: 10.1007/s13735-020-00197-9.
[10] S. M. Darwish and L. D. S. Al-Khafaji, “Dual Watermarking for Color Images: A New Image Copyright Protection Model based on the Fusion of Successive and Segmented Watermarking,†Multimed Tools Appl, vol. 79, no. 9–10, pp. 6503–6530, Mar. 2020, doi: 10.1007/s11042-019-08290-w.
[11] A. O. Mohammed, H. I. Hussein, R. J. Mstafa, and A. M. Abdulazeez, “A blind and robust color image watermarking scheme based on DCT and DWT domains,†Multimed Tools Appl, vol. 82, no. 21, pp. 32855–32881, Sep. 2023, doi: 10.1007/s11042-023-14797-0.
[12] A. Durafe and V. Patidar, “Development and analysis of IWT-SVD and DWT-SVD steganography using fractal cover,†Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 7, pp. 4483–4498, Jul. 2022, doi: 10.1016/j.jksuci.2020.10.008.
[13] T. Khanam, P. K. Dhar, S. Kowsar, and J.-M. Kim, “SVD-Based Image Watermarking Using the Fast Walsh-Hadamard Transform, Key Mapping, and Coefficient Ordering for Ownership Protection,†Symmetry (Basel), vol. 12, no. 1, pp. 1–20, Dec. 2019, doi: 10.3390/sym12010052.
[14] Y. Xue, K. Mu, Y. Wang, Y. Chen, P. Zhong, and J. Wen, “Robust Speech Steganography Using Differential SVD,†IEEE Access, vol. 7, pp. 153724–153733, 2019, doi: 10.1109/ACCESS.2019.2948946.
[15] Mohammed Hassan Abd and Osamah Waleed Allawi, “Secured Mechanism Towards Integrity of Digital Images Using DWT, DCT, LSB and Watermarking Integrations,†Ibn AL-Haitham Journal For Pure and Applied Sciences, vol. 36, no. 2, pp. 454–468, Apr. 2023, doi: 10.30526/36.2.3088.
[16] M. Begum, J. Ferdush, and M. S. Uddin, “A Hybrid robust watermarking system based on discrete cosine transform, discrete wavelet transform, and singular value decomposition,†Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 8, pp. 5856–5867, Sep. 2022, doi: 10.1016/j.jksuci.2021.07.012.
[17] E. A. Sofyan, C. A. Sari, H. Rachmawanto, and R. D. Cahyo, “High-Quality Evaluation for Invisible Watermarking Based on Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD),†Advance Sustainable Science, Engineering and Technology (ASSET), vol. 6, no. 1, 2024, doi: 10.26877/asset.v6i1.17186.
[18] J. Khandelwal, V. K. Sharma, D. Singh, and A. Zaguia, “Dwt-svd based image steganography using threshold value encryption method,†Computers, Materials and Continua, vol. 72, no. 2, pp. 3299–3312, 2022, doi: 10.32604/cmc.2022.023116.
[19] D. R. I. M. Setiadi, “PSNR vs SSIM: imperceptibility quality assessment for image steganography,†Multimed Tools Appl, vol. 80, no. 6, pp. 8423–8444, Mar. 2021, doi: 10.1007/s11042-020-10035-z.
[20] U. Sara, M. Akter, and M. S. Uddin, “Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study,†Journal of Computer and Communications, vol. 07, no. 03, pp. 8–18, 2019, doi: 10.4236/jcc.2019.73002.
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Published
2024-06-14
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How to Cite
[1]
D. W. Utomo, C. A. Sari, and F. O. Isinkaye, “Quality Improvement for Invisible Watermarking using Singular Value Decomposition and Discrete Cosine Transform”, MATRIK, vol. 23, no. 3, pp. 509–518, Jun. 2024, doi: 10.30812/matrik.v23i3.3744.
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