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This paper presents an alternative semi-blind watermarking algorithm in gray scale images. The new watermarking system changes the coefficients of discrete cosine transform in homogeneous manner. This algorithm is based on the modification of the base using the concept of the norm or the distance. Experimental results introduced at the end of this paper, demonstrates the robustness of the proposed strategy against different attacks.

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Compared to state-of-the-art classifiers, the Gaussian process classifier (GPC) offers several attractive properties such as the possibility to estimate the hyperparameters or to learn the best input features in a fully automatic way. However, till now, the integration of spatial contextual information in a GPC model for classifying remote sensing imagery has not yet received a sufficient attention compared to other classification approaches for which it has been shown that the classification accuracy can benefit from the exploitation of such information. In this context, in order to improve the GPC capabilities, we propose here to reformulate the GPC learning model so as to integrate spatial contextual information. All the mathematical developments leading to the proposed spatial GPC (SGPC), which embeds iteratively the spatial contextual information in the classification process, is described. Experimental results show that the SGPC can help in improving the classification accuracy compared to the baseline GPC.