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    filename: tutorial.m
    path of file: ./repos/audio_steganalysis_ml/train_test/ensemble_classifier/example
    
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% -------------------------------------------------------------------------
% Ensemble Classification | June 2013 | version 2.0 | TUTORIAL
% -------------------------------------------------------------------------
% Copyright (c) 2013 DDE Lab, Binghamton University, NY.
% All Rights Reserved.
% -------------------------------------------------------------------------
% Contact: jan@kodovsky.com | fridrich@binghamton.edu | June 2013
%          http://dde.binghamton.edu/download/ensemble
% -------------------------------------------------------------------------

% Short tutorial for the ensemble classifier (ver 2.0) developed for
% steganalysis in digital images.

% Load prepared cover/stego features. These features correspond to CC-PEV
% features extracted from nsF5 steganography at payload 0.2 bpac. Used JPEG
% quality factor = 75.
cover = load('example/cover.mat');
stego = load('example/stego.mat');

% Both loaded structures contain fields 'F' and 'names'. F is a feature
% matrix with individual samples in rows and individual features in
% columns, i.e., the number of columns corresponds to the feautre-space
% dimensionality. Field 'names' contains image filenames of the
% corresponding cover (or stego) images. Note that this format is
% consistent with the previous version of our ensemble implementation.

% As you can notice, filenames in cover.names and stego.names are
% de-synchronized. Furthermore, there is 2000 cover features but only 1994
% stego features. This simulates the real-life scenario where (for many
% steganographic methods) embedding may fail for some images and/or the
% features were extracted from images in a different order. Synchronization
% of cover/stego feature pairs (more details about this in the next
% paragraph) is an important pre-processing step in steganalysis and thus
% is part of this tutorial as well.  

% In steganalysis, it is important to train on the *pairs* of
% cover-features and the corresponding stego-features. When dividing the
% features into training/testing parts, for example, these pairs need to be
% preserved. But it is equally important to keep track of these pairs even
% within the training set itself! This is because when splitting the
% training data for the purposes of cross-validation (or bootstrapping),
% these pairs, again, need to be preserved. Therefore, our implementation
% of the ensemble training accepts only two *synchronized* matrices of
% features (cover & stego). While the implementation checks for the sizes
% of both matrices, the actual synchronization is the responsibility of a
% user. See the code below as an example of how to do this correctly.

% Restriction only to images that have both cover and stego features (only
% those will be considered)
names = intersect(cover.names,stego.names);
names = sort(names);

% Prepare cover features C
cover_names = cover.names(ismember(cover.names,names));
[cover_names,ix] = sort(cover_names);
C = cover.F(ismember(cover.names,names),:);
C = C(ix,:);

% Prepare stego features S
stego_names = stego.names(ismember(stego.names,names));

    
function tutorial()
% -------------------------------------------------------------------------
% Ensemble Classification | June 2013 | version 2.0 | TUTORIAL
% -------------------------------------------------------------------------
% Copyright (c) 2013 DDE Lab, Binghamton University, NY.
% All Rights Reserved.
% -------------------------------------------------------------------------
% Contact: jan@kodovsky.com | fridrich@binghamton.edu | June 2013
%          http://dde.binghamton.edu/download/ensemble
% -------------------------------------------------------------------------

% Short tutorial for the ensemble classifier (ver 2.0) developed for
% steganalysis in digital images.

% Load prepared cover/stego features. These features correspond to CC-PEV
% features extracted from nsF5 steganography at payload 0.2 bpac. Used JPEG
% quality factor = 75.
cover = load('example/cover.mat');
stego = load('example/stego.mat');

% Both loaded structures contain fields 'F' and 'names'. F is a feature
% matrix with individual samples in rows and individual features in
% columns, i.e., the number of columns corresponds to the feautre-space
% dimensionality. Field 'names' contains image filenames of the
% corresponding cover (or stego) images. Note that this format is
% consistent with the previous version of our ensemble implementation.

% As you can notice, filenames in cover.names and stego.names are
% de-synchronized. Furthermore, there is 2000 cover features but only 1994
% stego features. This simulates the real-life scenario where (for many
% steganographic methods) embedding may fail for some images and/or the
% features were extracted from images in a different order. Synchronization
% of cover/stego feature pairs (more details about this in the next
% paragraph) is an important pre-processing step in steganalysis and thus
% is part of this tutorial as well.  

% In steganalysis, it is important to train on the *pairs* of
% cover-features and the corresponding stego-features. When dividing the
% features into training/testing parts, for example, these pairs need to be
% preserved. But it is equally important to keep track of these pairs even
% within the training set itself! This is because when splitting the
% training data for the purposes of cross-validation (or bootstrapping),
% these pairs, again, need to be preserved. Therefore, our implementation
% of the ensemble training accepts only two *synchronized* matrices of
% features (cover & stego). While the implementation checks for the sizes
% of both matrices, the actual synchronization is the responsibility of a
% user. See the code below as an example of how to do this correctly.

% Restriction only to images that have both cover and stego features (only
% those will be considered)
names = intersect(cover.names,stego.names);
names = sort(names);

% Prepare cover features C
cover_names = cover.names(ismember(cover.names,names));
[cover_names,ix] = sort(cover_names);
C = cover.F(ismember(cover.names,names),:);
C = C(ix,:);

% Prepare stego features S
stego_names = stego.names(ismember(stego.names,names));
[stego_names,ix] = sort(stego_names);
S = stego.F(ismember(stego.names,names),:);
S = S(ix,:);

% At this point, we have the cover features C and the corresponding
% stego features S. They are correctly synchronized, i.e., the i-th row of
% the  stego matrix S comes from the stego image that was created from the
% cover image with features in the i-th row of the cover matrix C.

% Now we can prepare a training set and a testing set. 

% PRNG initialization with seed 1
RandStream.setGlobalStream(RandStream('mt19937ar','Seed',1));

% Division into training/testing set (half/half & preserving pairs)
random_permutation = randperm(size(C,1));
training_set = random_permutation(1:round(size(C,1)/2));
testing_set = random_permutation(round(size(C,1)/2)+1:end