Abstract
A digital watermarking algorithm based on Kalman filter and image fusion is proposed. The digital watermarking can be viewed as a process that embedding a weak signal (watermark) to a strong signal (original image), so the process of watermarking can be viewed as a process of image fusion. In the proposed watermarking algorithm, the watermark embedding and extraction process are expressed as the state estimate process, and Kalman filter is used as an optimal estimation algorithm in the process of image fusion. An optimal estimation model is built according to the watermark image and the original image, and then the state equation and the corresponding measurement equation are built. The optimal estimation is archived in case of the minimum estimation error variance. Crossentropy and mutual information are used to evaluate the performance of image fusion. Experimental results show that the proposed algorithm has a good performance in both robustness and invisibility.



Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Cao C, Hovakimyan N (2008) Vision-based tracking using intelligent excitation. Int J Control 81(11):1763–1778
Chang C, Lin P, Yeh J (2009) Preserving robustness and removability for digital watermarks using subsampling and difference correlation. Inf Sci 179(13):2283–2293
Cox IJ, Killian J, Leighton FT, Shamoon T (1997) Secure spread spectrum watermarking for multimedia. IEEE Trans Image Process 6(12):1673–1687
Fang Y, Huang J, Shi Y (2003) Image watermarking algorithm applying CDMA. In Proceedings of the international symposium on circuits and systems. pp 948–951
Fang Y, Wu S, Huang J (2004) DWT-Based CDMA watermarking resist cropping. ACTA Autom Sin 30(3):442–448
Figueiredo MT, Nowak D (2003) An EM algorithm for waveletbased image restoration. IEEE Trans Image Process 12(8):906–916
Guo JM, Pei SC, Lee H (2008) Paired Subimage matching watermarking method on ordered dither images and its high-quality progressive coding. IEEE Trans Multimed 10(1):16–30
Ho AS, Zhu X, Shen J, Marziliano P (2008) Fragile Watermarking Based on Encoding of the Zeroes of the z-Transform. IEEE Trans Inf Forensic Secur 3(3):567–569
Hsieh M, Tseng D, Huang Y (2001) Hiding Digital Watermarks Using Multiresolution Wavelet Transform. IEEE Trans Ind Electron 48(5):875–882
Hsu C, Wu J (1999) Hidden digital watermarks in images. IEEE Trans Image Process 8(1):58–68
Kalman R, Bucy R (1961) New results in linear filtering and prediction theory. J Basic Eng Trans ASME 83:193–196
Ker D (2005) Steganalysis of LSB matching in grayscale images. IEEE Signal Process Lett 12(6):441–444
Khan Shiraj, Bandyopadhyay Sharba, Ganguly R et al (2007) Relative performance of Mutual Information estimation methods for quantifying the dependence among short and noisy data. Phys Rev E 76(2):026209
Khan A, Moura J (2008) Distributing the Kalman filter for large-scale systems. IEEE Trans Signal Process 56(10):4919–4935
Miller M, Doerr G, Cox J (2004) Applying informed coding and embedding to design a robust high-capacity watermark. IEEE Trans Image Process 13(6):792–807
Kirovski D, Petitcolas F (2003) Blind pattern matching attack on watermarking systems. IEEE Trans Signal Process 51(4):1045–1053
Kundur D, Hatzinakos D (2004) Toward robust logo watermarking using multiresolution image fusion principles. IEEE Trans Multimed 6(1):185–198
Kwon B, Han S, Kwon W (2007) Minimum variance FIR smoothers for continuous-time state space signal models. IEEE Signal Process Lett 14(12):1024–1027
Lan T, Erdogmus D (2007) Maximally informative feature and sensor selection in pattern recognition using local and global independent component analysis. J VLSI Signal Process Systems Signal Image Video Technol 48(1):39–52
Lee Y, Kim H, Park Y (2009) A new data hiding scheme for binary image authentication with small image distortion. Inf Sci 179(22):3866–3884
Lin HS, Liao HM, Lu CS, Lin JC (2005) Fragile watermarking for authenticating 3-D polygonal meshes. IEEE Trans Multimed 7(6):997–1006
Lu Z, Xu D, Sun S (2005) Multipurpose image watermarking algorithm based on multistage vector quantization. IEEE Trans Image Process 14(6):822–831
Maeno K, Sun Q, Chang S, Suto M (2006) New semi-fragile image authentication watermarking techniques using random bias and nonuniform quantization. IEEE Trans Multimed 8(1):32–45
Alaeddin M, Maryam Y (2010) Image fusion algorithms for color and gray level images based on LCLS method and novel artificial neural network Neurocomputing. Neurocomputing 73(4–6):937–943
Mirikitani DT, Nikolaev N (2010) Efficient online recurrent connectionist learning with the ensemble Kalman filter. Neurocomputing 73(4–6):1024–1030
Montero J, Ruan D (2010) Modelling uncertainty. Inf Sci 180(6):799–802
Murillo-Fuentes J (2009) Independent component analysis in the blind watermarking of digital images. Neurocomputing 70(16–18):2881–2890
Olama M, Djouadi Seddik, Papageorgiou G, Charalambous D (2008) Position and velocity tracking in mobile networks using particle and Kalman filtering with comparison. IEEE Trans Veh Technol 57(2):1001–1010
Pei SC, Guo JM (2006) High capacity data hiding in halftone images using minimal error bit searching and least mean square filter. IEEE Trans Image Process 15(6):1665–1679
Petitcolas FA, Anderson RJ, Kuhn MG (1999) Information hiding—a survey. Proc IEEE 87(7):1062–1078
Pina A, Zaverucha G (2009) Applying REC analysis to ensembles of particle filters. Neural Comput Appl 18(10):25–35
Sasikala M, Kumaravel N (2007) A comparative analysis of feature based image fusion methods. Inf Technol J 6(8):1224–1230
Senjyu T, Kinjo K, Urasaki N, Uezato K (2003) High efficiency control of synchronous reluctance motors using extended Kalman filter. IEEE Trans Ind Electron 50(4):726–732
Su D, Wu X (2006) Image fusion based on multi-feature fuzzy clustering. J Comput-Aid Design Comput Graph 18(6):838–843
Sun Z, Liu J, Sun J, Sun X, Ling J (2009) A motion location based video watermarking scheme using ICA to extract dynamic frames. Neural Comput Appl 18(5):507–514
Szabat K, Orlowska-Kowalska T (2008) Performance improvement of industrial drives with mechanical elasticity using nonlinear adaptive Kalman filter. IEEE Trans Ind Electron 55(3):1075–1084
Toprak A, Guler I (2008) Angiograph image restoration with the use of rule base fuzzy 2D Kalman filter. Expert Syst Appl 35(4):1752–1761
Tseng H, Hsieh C (2009) Prediction-based reversible data hiding. Inf Sci 179(14):2460–2469
Usman A, Moura J (2008) Distributing the Kalman filter for large-scale systems Khan. IEEE Trans Signal Process 56(10):4919–4935
Wang S, Zheng D, Zhao J, Tam W, Speranza F (2007) An image quality evaluation method based on digital watermarking. IEEE Trans Circuit Syst Video Technol 17(1):98–105
Young SS (2006) Attitude estimation by multiple-mode Kalman filters. IEEE Trans Ind Electron 53(4):1386–1389
Zhang J, Tian L, Tai H (2004) A new watermarking method based on Chaotic maps. IEEE Int Conf Multimed Expo, Taipei pp 939–942
Zhang F, Zhang X, Zhang H (2007) Digital image watermarking capacity and detection error rate. Pattern Recogn Lett 28(1):1–10
Zhang F, Pan Z, Cao K, Zheng F, Wu F (2008) The upper and lower bounds of the information-hiding capacity of digital images. Inf Sci 178:2950–2959
Acknowledgment
This research was supported by the National Natural Science Foundation of China grants no. 60873039.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhang, F., Zhang, X. & Shang, D. Digital watermarking algorithm based on Kalman filtering and image fusion. Neural Comput & Applic 21, 1149–1157 (2012). https://doi.org/10.1007/s00521-011-0656-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-011-0656-9