caa a22 4500 60544806X CHVBK 20210128100135.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s11042-014-1904-7 doi (NATIONALLICENCE)springer-10.1007/s11042-014-1904-7 Scaling-based watermarking with universally optimum decoder [Elektronische Daten] [Mohammad Akhaee, Sayed Ebrahim Sahraeian] In this paper, we propose a blind scaling based image watermarking approach which is highly robust against the noise and gain attacks. Designed independent of the host signal distribution, the proposed scheme can universally be applied in various transform domains with arbitrary distributions. Partitioning the host signal into two patches, we embed the watermark code in one patch while keeping the other one unchanged for blind parameter estimation. We extract the watermark bits using a maximum likelihood decoder approach based on the ratio of the samples summation in each patch. Driving the distribution of the decision variable, we analytically study the performance of the proposed decoder. Employing the ratio of samples as the decision variable makes the proposed scheme invariant to the gain attack. The proposed algorithm is applied to both artificial Gaussian autoregressive signals as well as various test images. The robustness of the proposed decoder to various host signal distribution is verified. Experimental results confirm the validity of our model for low frequency components of natural images and its high robustness against common attacks. Springer Science+Business Media New York, 2014 Watermarking nationallicence Maximum likelihood detector nationallicence Gaussian distribution nationallicence Akhaee Mohammad School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran aut Ebrahim Sahraeian Sayed Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/15(2015-08-01), 5995-6018 1380-7501 74:15<5995 2015 74 11042 https://doi.org/10.1007/s11042-014-1904-7 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-014-1904-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Akhaee Mohammad School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran aut NATIONALLICENCE 700 1- Ebrahim Sahraeian Sayed Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/15(2015-08-01), 5995-6018 1380-7501 74:15<5995 2015 74 11042