Title :
Distribution independent blind watermarking
Author :
Sahraeian, S.M.E. ; Akhaee, M.A. ; Marvasti, F.
Author_Institution :
Dept. of Electr. & Comput. Eng., Texas A&M Univ., College Station, TX, USA
Abstract :
In this paper, a new blind scaling based watermarking approach is presented. The host signal is assumed to be stationary Gaussian with first-order autoregressive model. Partitioning the host signal into two separate parts, the data is embedded in one part and the other is kept unchanged for blind parameter estimation. Driving the distribution of the decision variable we have suggested a maximum likelihood decoding algorithm which is independent of the host signal distribution and can be applied for any transform domains. The proposed algorithm is applied to both artificial Gaussian autoregressive signals as well as various test images. Experimental results confirm the independence of the decoder performance to the host signal distribution and its great robustness against common attacks.
Keywords :
Gaussian distribution; autoregressive processes; maximum likelihood decoding; watermarking; artificial Gaussian autoregressive signals; blind parameter estimation; blind scaling; distribution independent blind watermarking; first-order autoregressive model; host signal distribution; maximum likelihood decoding; stationary Gaussian signal; Data encapsulation; Maximum likelihood decoding; Maximum likelihood detection; Maximum likelihood estimation; Parameter estimation; Partitioning algorithms; Robustness; Testing; Watermarking; Wavelet transforms; Gaussian Ratio distribution; Maximum likelihood decoder; Watermarking; scaling based embedding;
Conference_Titel :
Image Processing (ICIP), 2009 16th IEEE International Conference on
Conference_Location :
Cairo
Print_ISBN :
978-1-4244-5653-6
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2009.5414116
