Digital Image Watermarking with Hybrid Structure of DWT, DCT, SVD Techniques and The Optimization with BFO Algorithm

Yıl 2024, Cilt: 27 Sayı: 3, 857 - 871, 25.07.2024

Sadık Yıldız , Furkan Üstünsoy , Hasan Hüseyin Sayan

https://doi.org/10.2339/politeknik.1192824

Öz

The copyright violations in digital images and the violations of the privacy of personal data are happened with the development of technology and the widespread use of the internet. The usage of watermarks in digital images provides high protection to image owners in copyright protection and in protection of personal data. In this paper, watermarks have been added to digital images by using discrete wavelet transform (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD) methods, respectively. “Gaussian”, “shot”, “salt & pepper” and “speckle” noises are added to watermarked images, respectively. The original image and the watermark have been extracted from the watermarked image after adding noise. The original image and watermark have been re-extracted from the optimized watermarked image by using the bacterial foraging optimization (BFO) technique. In this step, the particle swarm optimization (PSO) algorithm has been used to set the best position of the chemotactic parameters of the BFO algorithm. The peak signal to noise ratio (PSNR), normalized cross correlation (NCC) and image fidelity (IF) values have been calculated and compared to see the success performance of watermarking techniques and optimization techniques.

Anahtar Kelimeler

Watermarking , image cryptography , metaheuristic optimization

DWT, DCT, SVD Tekniklerinden oluşan Hibrid Yapı ile Dijital Görüntü Filigran Ekleme ve BFO Algoritması ile Optimizasyonu

Öz

Teknolojinin gelişmesi ve internet kullanımının yaygınlaşması ile sayısal görüntülerde telif hakkı ihlalleri ve kişisel verilerin gizliliğinde ihlaller yaşanmaktadır. Dijital görüntülerde filigran kullanmak telif haklarının korunmasında ve kişisel verilerin korunmasında görüntü sahiplerine yüksek koruma sağlamaktadır. Bu makale çalışmasında sayısal görüntülere sırasıyla ayrık dalgacık dönüşümü (DWT), ayrık kosinüs dönüşümü (DCT) ve tekil değer ayrıştırma (SVD) yöntemleri kullanılarak filigran eklendi. Filigran eklenen görüntülere sırasıyla “Gauss”, “Atış”, “tuz-biber” ve “benek” gürültüleri eklenmiştir. Gürültü ekleme işlemlerinin ardından filigran uygulanan görüntüden orijinal görüntü ve filigran çıkarılmıştır. Bakteri arama optimizasyonu (BFO) tekniği kullanılarak optimize edilen filigranlı görüntüden orijinal görüntü ve filigran tekrar çıkarılmıştır. Bu adımda BFO algoritmasının kemotaktik parametrelerinin en iyi pozisyonunu belirlemek için parçacık sürüsü optimizasyonu (PSO) algoritması kullanılmıştır. Filigran teknikleri ve optimizasyon tekniklerinin başarı performanslarını göstermek için tepe sinyal-gürültü oranı (PSNR), Normalleştirilmiş çapraz korelasyon (NCC) ve görüntü uygunluk (IF) değerleri hesaplanarak karşılaştırılmıştır.

Anahtar Kelimeler

Filigran Ekleme , görüntü şifreleme , meta-sezgisel optimizasyon

Kaynakça

• [1] Al-maweri, N. A. S., Wan Adnan, W. A., Ramli, A. R., Samsudin, K., and Syed Ahmad, S., M., “A hybrid digital image watermarking algorithm based on DCT-DWT and auto-thresholding.”, Security Comm. Networks, 8: 4373– 4395, (2015).
• [2] K.A. Al-Afandy, E.M. EL-Rabaie, F.E.A. El-Samie, O.S. Faragallah, A. ELmhalaway, A.M. Shehata,"A Comparative Study For Color Systems Used In The DCT-DWT Watermarking Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 1, no. 5, pp. 42-49 (2016).
• [3] Mishra A., Agarwal C., Sharma A., Bedi P., "Optimized gray-scale image watermarking using DWT–SVD and Firefly Algorithm", Expert Systems with Applications, Volume 41, Issue 17, Pages 7858- 7867, (2014).
• [4] Golshan F., Mohammadi K., “A hybrid intelligent SVD-based perceptual shaping of a digital image watermark in DCT and DWT domain”, The Imaging Science Journal, 61:1, 35-46, (2013).
• [5] Mehto A., Mehra N., "Adaptive Lossless Medical Image Watermarking Algorithm Based on DCT & DWT", Procedia Computer Science, Volume 78, Pages 88-94, (2016).
• [6] Sharma S., Chauhan U., Khanam R., Singh K., "Digital Watermarking using Grasshopper Optimization Algorithm", Open Computer Science, vol. 11, no. 1, pp. 330-336, (2021).
• [7] Bose A., Maity S. P., "Secure sparse watermarking on DWT-SVD for digital images", Journal of Information Security and Applications, Volume 68, 103255, (2022).
• [8] Abdulrahman A. K., Öztürk S., "Çoklu Görüntü Damgalama Yönteminde Farklı Frekans Bölgelerinin Değerlendirilmesi", Bilişim Teknolojileri Dergisi, c. 11, sayı. 1, ss. 75-88, (2018).
• [9] Bakwad K. M., Pattnaik S.S., Sohi B. S., Devi S., Panigrahi B. K. , Gollapudi S. V. R. S., “Bacterial Foraging Optimization Technique Cascaded with Adaptive Filter to Enhance Peak Signal to Noise Ratio from Single Image”, IETE Journal of Research, 55:4, 173-179,(2009).
• [10] Liu J., Huang J., Luo Y., Cao L., Yang S., Wei D., Zhou R., "An Optimized Image Watermarking Method Based on HD and SVD in DWT Domain", IEEE Access, vol. 7, pp. 80849-80860, (2019).
• [11] Kadian P., Arora N., Arora S. M., "Performance Evaluation of Robust Watermarking Using DWT-SVD and RDWT-SVD," 6th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 987-991, (2019).
• [12] E. Elbaşı, " Two Bands Wavelet Based Robust Semi-Blind Image Watermarking", Politeknik Dergisi, 11(4): 329-337, (2008).
• [13] Vishwakarma V. P., Dalal S., Sisaudia V., "Efficient Feature Extraction using DWT-DCT for Robust Face Recognition under varying Illuminations," 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), pp. 982-987, (2018).
• [14] Wang M., Jiang H., Li Y., "Face recognition based on DWT/DCT and SVM", International Conference on Computer Application and System Modeling (ICCASM), pp. V3-507-V3-510, (2010).
• [15] S. R. Sheriff, "Digital Image Watermarking using Singular Value Decomposition", AL-Rafidain Journal of Computer Sciences and Mathematics, 7, 3, p-p 187-200, (2010).
• [16] Dirckx S., Huybrechs D., Ongenae R., “On the computation of the SVD of Fourier submatrices”, arXiv:2208.12583v1 (math.NA), (2022).
• [17] Ahmadi S.B.B., Zhang G., Wei S., “Robust and hybrid SVD-based image watermarking schemes:” Multimed Tools Appl 79, 1075–1117, (2020).
• [18] Mathew K D., “SVD based Image Watermarking Scheme”, IJCA Special Issue on Evolutionary Computation for Optimization Techniques-ECOT, pp. 21-24, (2010).
• [19] Akyol S., Alataş B., "Güncel Sürü Zekası Optamizasyon Algoritmaları", Nevşehir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, c. 1, sayı. 1, ss. 0-0, (2012).
• [20] Das, S., Biswas, A., Dasgupta, S., Abraham, A., “Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications”, Foundations of Computational Intelligence, Volume 3, pp 23–55, (2009).
• [21] Zhang Q., Chen, H., Luo J., Xu Y., Wu C., Li C., "Chaos Enhanced Bacterial Foraging Optimization for Global Optimization", in IEEE Access, vol. 6, pp. 64905-64919, 2018.
• [22] Bolat B., Altun O., Cortes P., Yıldız Y. E., Topal A. O., “A Comparison of Metaheuristics for the Allocation of Elevators to Calls in Buildings”, Politeknik Dergisi, 20(3): 519-52, (2017).
• [23] Wang, D., Tan, D., Liu, L., “Particle swarm optimization algorithm: an overview”, Soft Comput 22, 387–408 (2018).
• [24] Akyol K., Feneaker S. O. F., “Kaynaklı Kiriş Tasarımı Mühendislik Problemi İçin Kaotik Çoklu-sürü Parçacık Sürü Optimizasyonu”, Politeknik Dergisi, 1-1, (2022).
• [25] Donuk K., Arı A., Özdemir M. F., “Hanbay D. Deep Feature Selection for Facial Emotion Recognition Based on BPSO and SVM”, Politeknik Dergisi, 1-1, (2022).
• [26] Bharati S., Rahman M. A., Mandal S., Podder P., "Analysis of DWT, DCT, BFO & PBFO Algorithm for the Purpose of Medical Image Watermarking", International Conference on Innovation in Engineering and Technology (ICIET), pp. 1-6, (2018).
• [27] Yousefi M., Omid M., Rafiee Sh., Ghaderi S.F., “Strategic planning for minimizing CO2 emissions using LP model based on forecasted energy demand by PSO Algorithm and ANN”, Internatıonal Journal Of Energy And Envıronment, Volume 4, Issue 6, pp.1041-1052,(2013).
• [28] Aliwa M. B., El-Tobely T. A., Fahmy M. M., Nasr M. S., Aziz M. H., “Fidelity and Robust Digital Watermarking Adaptively Pixel based on Medial Pyramid of Embedding Error Gray Scale Images”, IJCSNS International Journal of Computer Science and Network Security, Vol. 10 No. 6, pp. 284-314, (2010).
• [29] Singh P., Devi K. J., Thakkar H. K., Kotecha K., "Region-Based Hybrid Medical Image Watermarking Scheme for Robust and Secured Transmission in IoMT,", IEEE Access, vol. 10, pp. 8974-8993, (2022).
• [30] Thakkar, F.N., Srivastava, V.K., “A blind medical image watermarking: DWT-SVD based robust and secure approach for telemedicine applications”. Multimed Tools Appl, 76, 3669–3697, (2017).
• [31] Rajan B. K., Harshan H. M., Venugopal G., "Veterinary Image Enhancement using DWTDCT and Singular Value Decomposition," 2020 International Conference on Communication and Signal Processing (ICCSP), pp. 0920-0924, (2020).
• [32] Alzahrani A., Memon N. A., "Blind and Robust Watermarking Scheme in Hybrid Domain for Copyright Protection of Medical Images,", IEEE Access, vol. 9, pp. 113714-113734, (2021).
• [33] Ma J., ChenJ., Wu G., "Robust Watermarking via Multidomain Transform Over Wireless Channel: Design and Experimental Validation,", IEEE Access, vol. 10, pp. 92284-92293, (2022).