Robust Digital image watermarking method using Graph-based transform (GBT) and Genetic Algorithm
Subject Areas : Renewable energySayed Mohammad Raza Mousavi 1 , Alireza Naghsh 2
1 - MSE Student, Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
2 - Prof Assist, Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Keywords: Genetic Algorithm, Watermarking, digital image watermarking, robust watermark, graph-based transform (GBT), ownership protection,
Abstract :
Numerous methods have been introduced for digital images watermarking as well as rubosting them. In this method, with using Graph-based transform and extracts the best graph structure with genetic algorithm so that watermarking can be performed with maximum robustness. One of the common methods in watermarking robustness is the use of discrete cosine transform (DCT). In this study, we have shown that the proposed method is much more powerful than DCT. The proposed method is tested on five different color images such as Lena, Barbara, Boat, Baboon, Peppers. Watermark image (logo) is a random binary image with size 16 x 16 and 8 x 8 pixels. This simulation results show that the proposed method is more robust to similar methods such as discrete cosine transforms. Proposing Watermarking has been evaluated using Bit Error Rate (BER), Structural Similarity Index Measuring (SSIM) and Peak signal-to-noise ratio (PSNR) criteria and different strength Gaussian noise attacks, JPEG compression, median filter, bluring, rescaling, rotate and cropping attacks
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[2] S. M. Mousavi, A. Naghsh, A. A.Manaf, S. A. R. Abu-Bakar, “A robust medical image watermarking against salt and pepper noise for brain MRI images”, Multimedia Tools and Applications, Vol. 76, No. 7, pp. 10313-10342, April 2017 (doi:10.1007/s11042-016-3622-9).
[3] S. M Mousavi, A. Naghsh, S. A. R Abu-Bakar, “A heuristic automatic and robust ROI detection method for medical image warermarking”, Journal of Digital Imaging Vol. 28, No. 4, pp. 417–427, Mar. 2015 (doi: 10.1007/s10278-015-9770-z).
[4] S. Saneie, A. Naghsh, “Introducing a new method of robust digital image watermarking against cropping and salt and pepper noise using sudoku”, Majlesi Journal of Multimedia Processing, Vol. 4, No. 4, pp. 9-15, Dec. 2015.
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[7]B. Behravan, A. Naghsh, “Introducing a new method of image reconstruction against crop attack using sudoku watermarking algorithm”, Proceeding of the IEEE/IRRIA, Shahrekord, Iran, April2017 (doi:10.1109/PRIA.2017.7983042).
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[9] R. A. Alotaibi, L. A. Elrefaei, “Text-image watermarking based on integer wavelet transform (IWT) and discrete cosine transform (DCT)”, Applied Computing and Informatics vol.15 , No. 2, pp.191-202, July 2019 (doi:10.1016/j.aci.2018.06.003)
[10] J. Hou, H. Liu, L. Chau, “Graph-based transform for data decorrelation”, Proceeding of the IEEE/DSP, Beijing, China, Oct2016. (doi: 10.1109/ICDSP.2016.7868540).
[11] M. Farzaneh, M. Asgari, R. Toroghi, “Audio compression using graph-based transform”, Proceeding of the IEEE/ISTEL, Dec.2018 (doi:10.1109/ISTEL.2018.8661027).
[12] W. Kim, S. K. Narang, “Graph based transforms for depth video coding, acoustics”, Proceeding of the IEEE/ICASSP), Kyoto, Japan, Mar 2012 (doi:10.1109/ICASSP.2012.6288008).
[13] X. Wen, H.Zhang, X.Xu, J.Quan, “A new watermarking approach based on probabilistic neural network in wavelet domain”, Soft Computing , Vol. 13, No.4 pp. 355–36, Feb 2009 (doi:10.1007/s00500-008-0331-y).
[14] V. Sachnev, H.J. Kim, S.Sundaram S, Y.Q. Shi., “Reversible watermarking algorithm sing sorting and prediction”, IEEE Trans. on Circuits and Systems for Video Technology, Vol. 19, No. 7, pp. 989 – 999, Apr 2009 (doi: 10.1109/TCSVT.2009.2020257).
[15] A. Ansari, S. Hong, G. Saavedra, B. Javidi, M. Martinez-Corral, “Ownership protection of plenoptic images by robust and reversible watermarking”, Optics and Lasers in Engineering, Vol. 107, No. 1, pp.325-334 Aug. 2018 (doi:10.1016/j.optlaseng.2018.03.028).
_||_[1] V. Potdar, E. Chang, “A survey of digital image watermarking techniques”, Proceeding of the IEEE/INDIN, Perth, WA, Australia, Australia, Aug. 2005 (doi:10.1109/INDIN.2005.1560462).
[2] S. M. Mousavi, A. Naghsh, A. A.Manaf, S. A. R. Abu-Bakar, “A robust medical image watermarking against salt and pepper noise for brain MRI images”, Multimedia Tools and Applications, Vol. 76, No. 7, pp. 10313-10342, April 2017 (doi:10.1007/s11042-016-3622-9).
[3] S. M Mousavi, A. Naghsh, S. A. R Abu-Bakar, “A heuristic automatic and robust ROI detection method for medical image warermarking”, Journal of Digital Imaging Vol. 28, No. 4, pp. 417–427, Mar. 2015 (doi: 10.1007/s10278-015-9770-z).
[4] S. Saneie, A. Naghsh, “Introducing a new method of robust digital image watermarking against cropping and salt and pepper noise using sudoku”, Majlesi Journal of Multimedia Processing, Vol. 4, No. 4, pp. 9-15, Dec. 2015.
[5] S. Saneie, A. Naghsh, “Robust digital image watermarking against cropping using sudoku puzzle in spatial and transform domain”, Journal of Intelligent Procedures in Electrical Technology,Vol.7, No.27, pp. 26-13, Nov 2016.
[6] M. S. Goli, A. Naghsh, “Introducing a new method robust against cropattackin digital image watermarking usingtwo-step sudok”, Proceeding of the IEEE/IRRIA, Shahrekord, Iran, April2017 (doi:10.1109/PRIA.2017.7983054).
[7]B. Behravan, A. Naghsh, “Introducing a new method of image reconstruction against crop attack using sudoku watermarking algorithm”, Proceeding of the IEEE/IRRIA, Shahrekord, Iran, April2017 (doi:10.1109/PRIA.2017.7983042).
[8] E. Najafi, “A robust embedding and blind extraction of image watermarking based on discrete wavelet transform”, Mathematical Sciences,Vol.11, No. 4, pp 307–318 2017. Dec2017, (doi:10.1007/s40096-017-0233-1)
[9] R. A. Alotaibi, L. A. Elrefaei, “Text-image watermarking based on integer wavelet transform (IWT) and discrete cosine transform (DCT)”, Applied Computing and Informatics vol.15 , No. 2, pp.191-202, July 2019 (doi:10.1016/j.aci.2018.06.003)
[10] J. Hou, H. Liu, L. Chau, “Graph-based transform for data decorrelation”, Proceeding of the IEEE/DSP, Beijing, China, Oct2016. (doi: 10.1109/ICDSP.2016.7868540).
[11] M. Farzaneh, M. Asgari, R. Toroghi, “Audio compression using graph-based transform”, Proceeding of the IEEE/ISTEL, Dec.2018 (doi:10.1109/ISTEL.2018.8661027).
[12] W. Kim, S. K. Narang, “Graph based transforms for depth video coding, acoustics”, Proceeding of the IEEE/ICASSP), Kyoto, Japan, Mar 2012 (doi:10.1109/ICASSP.2012.6288008).
[13] X. Wen, H.Zhang, X.Xu, J.Quan, “A new watermarking approach based on probabilistic neural network in wavelet domain”, Soft Computing , Vol. 13, No.4 pp. 355–36, Feb 2009 (doi:10.1007/s00500-008-0331-y).
[14] V. Sachnev, H.J. Kim, S.Sundaram S, Y.Q. Shi., “Reversible watermarking algorithm sing sorting and prediction”, IEEE Trans. on Circuits and Systems for Video Technology, Vol. 19, No. 7, pp. 989 – 999, Apr 2009 (doi: 10.1109/TCSVT.2009.2020257).
[15] A. Ansari, S. Hong, G. Saavedra, B. Javidi, M. Martinez-Corral, “Ownership protection of plenoptic images by robust and reversible watermarking”, Optics and Lasers in Engineering, Vol. 107, No. 1, pp.325-334 Aug. 2018 (doi:10.1016/j.optlaseng.2018.03.028).