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Digital Watermarking using Dragonfly Optimization Algorithm | ||
| Journal of Information Technology Management | ||
| دوره 12، Special Issue: Deep Learning for Visual Information Analytics and Management.، 2020، صفحه 36-47 اصل مقاله (1.05 M) | ||
| نوع مقاله: Special Issue: Deep Learning for Visual Information Analytics and Management. | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2020.78888 | ||
| نویسندگان | ||
| Satender Sharma* 1؛ Usha Chauhan1؛ Ruqaiya Khanam2؛ Krishna Kant Singh3 | ||
| 1School of Electrical & Electronics Communication Engineering, Galgotias University, Greater Noida, India. | ||
| 2Department of CSE, Sharda University, Greater Noida, India. | ||
| 3Department of ECE, KIET Group of Institutions, Delhi-NCR, Ghaziabad, India. | ||
| چکیده | ||
| In this paper a novel digital watermarking algorithm is proposed. The proposed method comprises of a watermarking embedding and extraction algorithm using bio inspired optimization technique. Dragonfly algorithm (DA) is based on the static and dynamic swarming behaviors of dragonflies in nature. The dragonfly algorithm is used to optimize the scaling factor of the watermarking so that an optimal watermark is embedded. Watermarking algorithms take as input a cover image and the message. The cover image in the proposed method is decomposed into sub bands using discrete wavelet transform (DWT). Thereafter, it is converted to discrete cosine blocks (DCT). An optimal scaling factor is required for performing the watermarking. In this paper, DA is used for computing the scaling factor. The DA generated scaling factor is optimal and improves the performance of the watermarking. The inverse DWT and DCT are computed to extract the watermarked image from the cover image. The proposed method is applied on different images to evaluate the performance. The results obtained are compared with other state of the art methods. | ||
| کلیدواژه‌ها | ||
| Image watermarking؛ Dragonfly optimization؛ Discrete wavelet transform؛ Copyright protection | ||
| مراجع | ||
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Ali M, Ahn CW, Pant M (2014) A robust image watermarking technique using SVD and differential evolution in DCT domain. Optik 125:428–434. Bekkouch S, Faraoun KM(2015) Robust and reversible image watermarking scheme using combined DCTDWT-SVD transforms. J Inf Process Syst 11:406–420 Benoraira A, Benmahammed K, Boucenna N (2015) Blind image watermarking technique based on differential embedding in DWT and DCT domains. EURASIP J Adv Sig Pr 2015:55 Bharati, S., Rahman, M. A., Mandal, S., & Podder, P. (2018, December). Analysis of DWT, DCT, BFO & PBFO Algorithm for the Purpose of Medical Image Watermarking. In 2018 International Conference on Innovation in Engineering and Technology (ICIET) (pp. 1-6). IEEE. Divecha N, Jani NN (2013) Implementation and performance analysis of DCT-DWT-SVD based watermarking algorithms for color images. 2013 International Conference on Intelligent Systems and Signal Processing (ISSP) Fazli S, Moeini M (2016) A robust image watermarking method based on DWT, DCT, and SVD using a new technique for correction of main geometric attacks. Optik 127:964–972 Furqan A, Kumar M (2015) Study and analysis of robust DWT-SVD domain based digital image watermarking technique using MATLAB. 2015 IEEE International Conference on Computational Intelligence & Communication Technology Handito KW, Fauzi Z, Ma’ruf FA (2018) The comparison between SVD-DCT and SVD-DWT digital image watermarking. International Conference on Data and Information Science. IOP Conf. Series: Journal of Physics: Conf. Series 971 Khadam, U., Iqbal, M. M., Azam, M. A., Khalid, S., Rho, S., & Chilamkurti, N. (2019). Digital watermarking technique for text document protection using data mining analysis. IEEE Access, 7, 64955-64965. Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053-1073. Nandi, S., & Santhi, V. (2016). DWT–SVD-based watermarking scheme using optimization technique. In Artificial Intelligence and Evolutionary Computations in Engineering Systems (pp. 69-77). Springer, New Delhi. Strang, G. (1999). The discrete cosine transforms. SIAM review, 41(1):135-47. | ||
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