Abstract
A digital watermarking is a technique which is protecting the user authenticity by embedding the digital code in the image. In this paper, an attempt is made to invisible watermarking technique based on Local Binary Pattern (LBP), Least Significant Bit (LSB), Gabor, Histogram of Gradient (HoG), and Multi-class Support Vector Machine (SVM). The embedded image is classified through multi-class SVM with magnitude and direction by HoG features. It is a kind of invisible watermarking technique as we need the original image for identification. The Peak Signal Noise Ratio (PSNR) 31.12 dB is used to evaluate the strength of noise between the watermarked image and original image. Subsequently, the image identification is done by using multi SVM classification and measured with 98.75% accuracy of watermarked image.
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References
Rakhmawati L, Wirawan W, Suwadi S (2019) A recent survey of self-embedding fragile watermarking scheme for image authentication with recovery capability. EURASIP J Image Video Process 2019(1):61
Tao H, Chongmin L, Zain JM, Abdalla AN (2014) Robust im-age watermarking theories and techniques: A review. J Appl Res Technol 12(1):122–138
Zhang H, Wang C, Zhou X (2017) Fragile watermarking based on LBP for blind tamper detection in images. JIPS J Inf Process Syst 13(2):385–399
Jain P, Ghanekar U (2018) Robust watermarking technique for textured images. Proc Comput Sci 125:179–186
Muhammad K, Sajjad M, Mehmood I, Rho S, Baik SW (2016) A novel magic LSB substitution method (M-LSB-SM) using multi-level encryption and achromatic component of an image. Multimedia Tools Appl 75(22):14867–14893
Gong X, Yu F, Zhao X, Wang S (2018) Security analysis of a self-embedding fragile image watermark scheme. arXiv preprint arXiv:1812.11735 (2018)
Bamatraf, Abdullah, Rosziati Ibrahim, and Mohd Najib B. Mohd Salleh. ”Digital watermarking algorithm using LSB.” In 2010 International Conference on Com- puter Applications and Industrial Electronics, pp. 155–159. IEEE, 2010.
Chang J-D, Chen B-H, Tsai C-S (2013) LBP-based fragile watermarking scheme for image tamper detection and recovery. In: 2013 International symposium on next-generation electronics. IEEE, New York, pp 173–176
Zhang H, Wang C, Zhou X (2017) An improved secure semi-fragile watermarking based on LBP and Arnold transform. J Inf Pro-cess Syst 13(5)
Wenyin Z, Shih FY (2011) Semi-fragile spatial watermarking based on local binary pattern operators. Opt Commun 284(16–17):3904–3912
Pinjari SA, Patil NN (2016) A pixel based fragile watermarking technique using LBP (Local Binary Pattern). In: 2016 international conference on global trends in signal processing, information computing and communication (ICGTSPICC). IEEE, New York, pp 194–196
Mandal JK, Ghosal SK, Zizka J (2013) A Fragile watermarking based on binomial transform in color images. In: Proceedings of third international conference on computer science and information technology (CC- SIT 2013), pp 18–20
Ghorbani M, Targhi AT, Dehshibi MM (2015) HOG and LBP: towards a robust face recognition system. In: 2015 tenth international conference on digital information management (ICDIM). IEEE, New York, pp 138–141
Shavers C, Li R, Lebby G (2006) An SVM-based approach to face detection. In: 2006 Proceeding of the thirty-eighth southeastern symposium on system theory. IEEE, New York, pp 362–366
Heisele B, Ho P, Poggio T (2001) Face recognition with support vector machines: Global versus component-based approach. In: Proceedings eighth IEEE international conference on computer vision, ICCV 2001, vol 2. IEEE, New York, pp 688–694
Nanni L, Lumini A, Brahnam S (2012) Survey on LBP based texture descriptors for image classification. Expert Syst Appl 39(3):3634–3641
Chalamala SR, Kakkirala KR (2015) Local binary patterns for digital image watermarking. In: 2015 3rd international conference on artificial intelligence, modelling and simulation (AIMS). IEEE, New York, pp 159–162
Xia W, Yin S, Ouyang P (2013) A high precision feature based on LBP and Gabor theory for face recognition. Sensors 13(4):4499–4513
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Mohana, S.D., Bharathi, R.K. (2021). Face Template Security: LBP-Based LSB Watermarking Technique for Multi-class SVM Classification Using HoG. In: Komanapalli, V.L.N., Sivakumaran, N., Hampannavar, S. (eds) Advances in Automation, Signal Processing, Instrumentation, and Control. i-CASIC 2020. Lecture Notes in Electrical Engineering, vol 700. Springer, Singapore. https://doi.org/10.1007/978-981-15-8221-9_148
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DOI: https://doi.org/10.1007/978-981-15-8221-9_148
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