The state of the art steganalytic features for spatial domain, and to some extent for transfer domains (CDT) as well, are based on histogram of co-occurances of neighboring elements. The rationale behind is that neighboring pixels in digital images are correlated, which is caused by the smoothness of our world and by the usual image processing. The limitation of the histogram-based features is that they do not scale well with respect to the number of modeled neighboring elements, since the number of histogram bins (hence number of features) depends exponentially on this quanitity.
The remedy adopted by the prior art is to sum values of neighboring bins together, which can be seen as a vector quantization controlled by the position of the quantization centers. So far the quantization centers has been determined manually according to the intuition of the staganalyst. Heere we proposedto use Linde, Buso, and Gray algorithm in order to automatically find quantization centers maximizing the detection accuracy of resulting features. The quantization centers found by the proposed algorithm are experimentally compared to the ones used by the prior art on the steganalysis of Hugo algorithm. Tbhe results show a non-negligible improvements in the accuracy, especially when more complicated filters and higher-order histograms are used.