Dimension Reduction Based Robust Digital Image Watermarking Using Truncated Singular Value Decomposition and Discrete Wavelet Transform

Yıl 2022, , 761 - 768, 31.08.2022

Ahmet Haşim Yurttakal , Fahrettin Horasan

https://doi.org/10.35414/akufemubid.1141229

Öz

Signal processing transformations and mathematical techniques are generally used in watermarking techniques used in areas such as copyright protection, authentication, fingerprinting, content tagging. In this study, instead of the Singular Value Decomposition (SVD), which is preferred in most watermarking schemes, the dimension reduction-based truncated-SVD technique is used. This technique is combined with the Discrete Wavelet Transform. Compared to the baseline SVD-DWT-based technique, it has been observed that the proposed scheme has made progress in imperceptibility and resistance performances against all possible attacks, except histogram equalization. It is predicted that the proposed scheme will lead to alternative stamping schemes using different matrix decomposition and signal transformations.

Anahtar Kelimeler

Watermarking, Truncated Singular Value Decomposition, Discrete Wavelet Transform, Dimension Reduction, Data Hiding

Kesik Tekil Değer Ayrışımı ve Ayrık Dalgacık Dönüşümü Kullanılarak Boyut İndirgeme Tabanlı Dayanıklı Dijital Görüntü Damgalama

Öz

Telif hakkı koruma, kimlik doğrulama, parmak izi, içerik etiketleme gibi alanlarda kullanılan damgalama tekniklerinde genel olarak sinyal işleme dönüşümleri ve matematiksel teknikler kullanılır. Bu araştırmada çoğu damgalama tekniğinde tercih edilen Tekil Değer Ayrışımı (TDA) yerine, boyut indirgeme tabanlı Kesik-TDA tekniği kullanılmıştır. Önerilen bu teknik Ayrık Dalgacık Dönüşümü (ADD) ile birlikte kullanılmıştır. Temel TDA-ADD tabanlı yönteme göre önerilen yöntemin histogram eşitleme dışında tüm olası saldırılara karşı algılanamazlık ve dayanıklılık performanslarında ilerleme kaydettiği gözlenmiştir. Önerilen şemanın farklı matris ayrışımı ve sinyal işleme dönüşümlerinin kullanıldığı alternatif damgalama şemalarına yön vereceği tahmin edilmektedir.

Anahtar Kelimeler

Damgalama, Kesik Tekil Değer Ayrışımı, Ayrık Dalgacık Dönüşümü, Boyut İndirgeme, Veri Gizleme

Kaynakça

• Alshoura W. H., Zainol Z., The, J. S, Alawida M., and Alabdulatif, A., 2021. Hybrid SVD-Based Image Watermarking Schemes: A Review. IEEE Access, 9, 32931-32968.
• Ansari, I. A., Pant, M., and Ahn, C. W., 2016. Robust and false positive free watermarking in IWT domain using SVD and ABC. Engineering Applications of Artificial Intelligence, 49, 114-125.
• Byun, S. C., Lee, S. K., Tewfik, A. H., andAhn, B. H., 2003. A SVD-Based Fragile Watermarking Scheme for Image Authentication. IWDW 2002. Lecture Notes in Computer Science, 2613, 170-178.
• Chai, D., Wang, L., Chen, K., & Yang, Q., 2020. Secure federated matrix factorization. IEEE Intelligent Systems, 36(5), 11-20.
• Chen, J., and Saad, Y.,2008. Lanczos vectors versus singular vectors for effective dimension reduction. IEEE Transactions on Knowledge and Data Engineering, 21(8), 1091-1103.
• Ernawan, F., and Kabir, M. N., 2019. An improved watermarking technique for copyright protection based on tchebichef moments. IEEE Access, 7, 151985-152003.
• Evsutin, O., and Dzhanashia, K., 2022. Watermarking schemes for digital images: Robustness overview. Signal Processing: Image Communication, 100, 116523.
• Evsutin, O., Melman, A., and Meshcheryakov, R. 2020. Digital steganography and watermarking for digital images: A review of current research directions. IEEE Access, 8, 166589-166611.
• Fierro, R. D., and Hansen, P. C., 1997. Low-rank revealing UTV decompositions. Numerical Algorithms, 15(1), 37-55.
• Ganic, E., and Eskicioglu, A. M., 2004. Robust DWT-SVD domain image watermarking: embedding data in all frequencies. In Proceedings of the 2004 Workshop on Multimedia and Security, 166-174.
• Hernández-Lobato, J. M., Houlsby, N., and Ghahramani, Z., 2014. Probabilistic matrix factorization with non-random missing data. In International Conference on Machine Learning, 1512-1520.
• Horasan, F., 2022. A novel image watermarking scheme using ULV decomposition. Optik, 259, 168958.
• Jessup, E. R., and Martin, J. H., 2001. Taking a new look at the latent semantic analysis approach to information retrieval. Computational information retrieval, 121-144.
• Kadian, P; Arora, S. M. and Arora, N., 2021. Robust digital watermarking techniques for copyright protection of digital data: A survey. Wireless Personal Communications,118(4), 3225-3249.
• Kumar, S., Gupta, A., and Walia, G. S., 2021. Reversible data hiding: A contemporary survey of state-of-the-art, opportunities and challenges. Applied Intelligence, 52, 7373–7406.
• Kumar, S.,Singh, B. K., 2021. A Review on Digital Watermarking-Based Image Forensic Technique. Machine Vision and Augmented Intelligence—Theory and Applications, Lecture Notes in Electrical Engineering, 796, 91-100.
• Li, H., Liu, T., Wu, X., and Chen, Q.,2019. Research on bearing fault feature extraction based on singular value decomposition and optimized frequency band entropy. Mechanical Systems and Signal Processing, 118, 477-502.
• Litman, J., 2017. Digital Copyright. Digital Copyright, Maize Books University of Michigan Press
• Liu, J., Huang, J., Luo, Y., Cao, L., Yang, S., Wei, D., and Zhou, R., 2019. An optimized image watermarking method based on HD and SVD in DWT domain. IEEE Access, 7, 80849-80860.
• Liu, R., and Tan, T., 2002. An SVD-based watermarking scheme for protecting rightful ownership. IEEE Transactions on Multimedia, 4(1), 121-128.
• Liu, W., Yuan, K., and Ye, D., 2008. Reducing microarray data via nonnegative matrix factorization for visualization and clustering analysis. Journal of biomedical informatics, 41(4), 602-606.
• Luo, Y., Li, L., Liu, J., Tang, S., Cao, L., Zhang, S., Qiu, S., and Cao, Y., 2021. A multi-scale image watermarking based on integer wavelet transform and singular value decomposition. Expert Systems with Applications, 168, 114272.
• Nikulin, V., Huang, T. H., Ng, S. K., Rathnayake, S. I., and McLachlan, G. J., 2011. A very fast algorithm for matrix factorization. Statistics & probability letters, 81(7), 773-782.
• Postigo, H., 2012. The digital rights movement: The role of technology in subverting digital copyright, MIT Press.
• Rajendran, S., Kulkarni, V., Chaudhari, S., and Gupta, P. K., 2020. An update on medical data steganography and encryption. In Recent Trends in Image and Signal Processing in Computer Vision, 1124,181-199.
• Singh, L., Singh, A. K., and Singh, P. K. 2020. Secure data hiding techniques: a survey. Multimedia Tools and Applications, 79(23), 15901-15921.
• Vaidya P., andPvssr C.M., 2017. A robust semi-blind watermarking for color images based on multiple decompositions. Multimedia Tools and Applications, 76(24), 25623-25656.
• Wang, F. H., Pan, J. S., and Jain, L. C., 2009. Digital watermarking techniques. In Innovations in Digital Watermarking Techniques,232,11-26.
• Xiang, Y., Huang, J., Pérez-González, F., Hua, G., and Malik, H., 2016. IEEE access special section editorial: latest advances and emerging applications of data hiding. IEEE Access, 4, 9740-9742.
• Zainol, Z., Teh, J. S., and Alawida, M., 2020. A new chaotic image watermarking scheme based on SVD and IWT,IEEE Access,8, 43391-43406.
• Zhou, G., Cichocki, A., and Xie, S., 2012. Fast nonnegative matrix/tensor factorization based on low-rank approximation. IEEE Transactions on Signal Processing, 60(6), 2928-2940.