Matlab implementation of the low-complexity linear classifier as described in [1].
There is no need to install anything, you can start using the function LCLSMR.m right away.
The usage of the program is demonstrated in the attached tutorial file. All needed feature files are also included. We highly recommend spending the time to go through the tutorial as it shows how the program should be used for steganalysis experiments.
The program is available for public use. Please, remember to recognize our work by citing [1].
Thank you.
The ensemble classifier, based on Fisher Linear Discriminant base learners, was introduced specifically for steganalysis of digital media, which currently uses high-dimensional feature spaces. Presently it is probably the most used method to design supervised classifier for steganalysis of digital images because of its good detection accuracy and small computational cost. It has been assumed by the community that the classifier implements a non-linear boundary through pooling binary decision of individual classifiers within the ensemble. This paper challenges this assumption by showing that linear classifier obtained by various regularizations of the FLD can perform equally well as the ensemble. Moreover it demonstrates that using state of the art solvers linear classifiers can be trained more efficiently and offer certain potential advantages over the original ensemble leading to much lower computational complexity than the ensemble classifier. All claims are supported experimentally on a wide spectrum of stego schemes operating in both the spatial and JPEG domains with a multitude of rich steganalysis feature sets.
[1] R. Cogranne, V. Sedighi, J. Fridrich, and T. Pevný, Is Ensemble Classifier Needed for Steganalysis in High-Dimensional Feature Spaces?. IEEE WIFS, Rome, Italy November 16–19, 2015. [pdf]