Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 6/2012

01.09.2012 | LSMS2010 and ICSEE 2010

Digital watermarking algorithm based on Kalman filtering and image fusion

Erschienen in: Neural Computing and Applications | Ausgabe 6/2012

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.
Vorheriger Artikel Application of nonlinear PCA for fault detection in polymer extrusion processes
Nächster Artikel Quantitative methods for detecting cerebral infarction from multiple channel EEG recordings

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
2.
Chang C, Lin P, Yeh J (2009) Preserving robustness and removability for digital watermarks using subsampling and difference correlation. Inf Sci 179(13):2283–2293CrossRef
3.
Cox IJ, Killian J, Leighton FT, Shamoon T (1997) Secure spread spectrum watermarking for multimedia. IEEE Trans Image Process 6(12):1673–1687CrossRef
4.
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
5.
Fang Y, Wu S, Huang J (2004) DWT-Based CDMA watermarking resist cropping. ACTA Autom Sin 30(3):442–448
6.
Figueiredo MT, Nowak D (2003) An EM algorithm for waveletbased image restoration. IEEE Trans Image Process 12(8):906–916MathSciNetCrossRef
7.
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):16–30CrossRef
8.
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):567–569CrossRef
9.
Hsieh M, Tseng D, Huang Y (2001) Hiding Digital Watermarks Using Multiresolution Wavelet Transform. IEEE Trans Ind Electron 48(5):875–882CrossRef
10.
Hsu C, Wu J (1999) Hidden digital watermarks in images. IEEE Trans Image Process 8(1):58–68CrossRef
11.
Kalman R, Bucy R (1961) New results in linear filtering and prediction theory. J Basic Eng Trans ASME 83:193–196MathSciNetCrossRef
12.
Ker D (2005) Steganalysis of LSB matching in grayscale images. IEEE Signal Process Lett 12(6):441–444CrossRef
13.
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):026209MathSciNetCrossRef
14.
Khan A, Moura J (2008) Distributing the Kalman filter for large-scale systems. IEEE Trans Signal Process 56(10):4919–4935MathSciNetCrossRef
15.
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):792–807CrossRef
16.
Kirovski D, Petitcolas F (2003) Blind pattern matching attack on watermarking systems. IEEE Trans Signal Process 51(4):1045–1053MathSciNetCrossRef
17.
Kundur D, Hatzinakos D (2004) Toward robust logo watermarking using multiresolution image fusion principles. IEEE Trans Multimed 6(1):185–198CrossRef
18.
Kwon B, Han S, Kwon W (2007) Minimum variance FIR smoothers for continuous-time state space signal models. IEEE Signal Process Lett 14(12):1024–1027CrossRef
19.
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):39–52CrossRef
20.
Lee Y, Kim H, Park Y (2009) A new data hiding scheme for binary image authentication with small image distortion. Inf Sci 179(22):3866–3884CrossRef
21.
Lin HS, Liao HM, Lu CS, Lin JC (2005) Fragile watermarking for authenticating 3-D polygonal meshes. IEEE Trans Multimed 7(6):997–1006CrossRef
22.
Lu Z, Xu D, Sun S (2005) Multipurpose image watermarking algorithm based on multistage vector quantization. IEEE Trans Image Process 14(6):822–831CrossRef
23.
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):32–45CrossRef
24.
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
25.
Mirikitani DT, Nikolaev N (2010) Efficient online recurrent connectionist learning with the ensemble Kalman filter. Neurocomputing 73(4–6):1024–1030
26.
Montero J, Ruan D (2010) Modelling uncertainty. Inf Sci 180(6):799–802CrossRef
27.
Murillo-Fuentes J (2009) Independent component analysis in the blind watermarking of digital images. Neurocomputing 70(16–18):2881–2890
28.
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):1001–1010CrossRef
29.
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):1665–1679CrossRef
30.
Petitcolas FA, Anderson RJ, Kuhn MG (1999) Information hiding—a survey. Proc IEEE 87(7):1062–1078CrossRef
31.
Pina A, Zaverucha G (2009) Applying REC analysis to ensembles of particle filters. Neural Comput Appl 18(10):25–35
32.
Sasikala M, Kumaravel N (2007) A comparative analysis of feature based image fusion methods. Inf Technol J 6(8):1224–1230CrossRef
33.
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):726–732CrossRef
34.
Su D, Wu X (2006) Image fusion based on multi-feature fuzzy clustering. J Comput-Aid Design Comput Graph 18(6):838–843MathSciNet
35.
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):507–514CrossRef
36.
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):1075–1084CrossRef
37.
Toprak A, Guler I (2008) Angiograph image restoration with the use of rule base fuzzy 2D Kalman filter. Expert Syst Appl 35(4):1752–1761CrossRef
38.
Tseng H, Hsieh C (2009) Prediction-based reversible data hiding. Inf Sci 179(14):2460–2469MATHCrossRef
39.
Usman A, Moura J (2008) Distributing the Kalman filter for large-scale systems Khan. IEEE Trans Signal Process 56(10):4919–4935MathSciNetCrossRef
40.
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):98–105CrossRef
41.
Young SS (2006) Attitude estimation by multiple-mode Kalman filters. IEEE Trans Ind Electron 53(4):1386–1389CrossRef
42.
Zhang J, Tian L, Tai H (2004) A new watermarking method based on Chaotic maps. IEEE Int Conf Multimed Expo, Taipei pp 939–942
43.
Zhang F, Zhang X, Zhang H (2007) Digital image watermarking capacity and detection error rate. Pattern Recogn Lett 28(1):1–10CrossRef
44.
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 178:2950–2959MathSciNetCrossRef
Metadaten
Titel
Digital watermarking algorithm based on Kalman filtering and image fusion
Publikationsdatum
01.09.2012
Erschienen in
Neural Computing and Applications / Ausgabe 6/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-011-0656-9

Weitere Artikel der Ausgabe 6/2012

Neural Computing and Applications 6/2012 Zur Ausgabe

LSMS2010 and ICSEE 2010

Bandwidth scheduling and optimization using non-cooperative game model-based shuffled frog leaping algorithm in a networked learning control system

Original Article

NBSOM: The naive Bayes self-organizing map

Original Article

SOMwise regression: a new clusterwise regression method

LSMS2010 and ICSEE2010

Training support vector data descriptors using converging linear particle swarm optimization

LSMS2010 and ICSEE 2010

A hybrid genetic algorithm for two-stage multi-item inventory system with stochastic demand

Original Article

Design of an adaptive self-organizing fuzzy neural network controller for uncertain nonlinear chaotic systems