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feature_MSUMFSD.m
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feature_MSUMFSD.m
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function featuresMSUMFSD = feature_MSUMFSD(iterCntMSU, varargin )
% clear all;
% close all;
% varargin = {'distortion', 'glcm', 'visualRhythmHori', 'visualRhythm'};
% varargin = {'distortion', 'glcm', 'diffusionWeightLbpU8'};
% varargin = {'lbpU8'};
% I = imread('attack.jpg');
%% init
setDir = fullfile(cd, 'imageDatabases', 'MSUMFSD');
numFrames = 30;
numTotalFeatures = 0;
numCurrentFeatures = 0;
distortionFeatureFlag = 0;
glcmFeatureFlag = 0;
visualRhythmHoriFlag = 0;
overlapJPGLBPFlag = 0; % lbp uniform 8 Flag
mappingU8=getmapping(8,'u2');
lbpU16Flag = 0; % lbp uniform 16 Flag
mappingU16=getmapping(16,'u2');
lbpU8FourierFlag = 0; % lbp uniform 8 for fourier spectrum Flag
diffusionWeightLbpU8Flag = 0;
diffusionWeightOverlapLBPFlag = 0;
ligRmvPpmLBPFlag = 0;
ligRmvPpmDistortionFlag = 0;
ligRmvPpmGlcmFlag = 0;
cutTop = 0;
cutBottom = 0;
paraStr = '';
%% parameter setting
for i = 1 : length(varargin),
paraStr = strcat(paraStr, '_', varargin{i});
end
if ~isempty(strfind(paraStr,'distortion'))
distortionFeatureFlag = 1;
numTotalFeatures = numTotalFeatures + 115;
end
if ~isempty(strfind(paraStr,'glcm'))
glcmFeatureFlag = 1;
numTotalFeatures = numTotalFeatures + 88;
end
if ~isempty(strfind(paraStr,'visualRhythmHori'))
visualRhythmHoriFlag = 1;
numTotalFeatures = numTotalFeatures + 3600;
end
if ~isempty(strfind(paraStr,'overlapJPGLBP'))
overlapJPGLBPFlag = 1;
numTotalFeatures = numTotalFeatures + 833;
end
if ~isempty(strfind(paraStr,'lbpU8Fourier'))
lbpU8FourierFlag = 1;
numTotalFeatures = numTotalFeatures + 59;
end
if ~isempty(strfind(paraStr,'lbpU16'))
lbpU16Flag = 1;
numTotalFeatures = numTotalFeatures + 243;
end
if ~isempty(strfind(paraStr,'diffusionWeightLbpU8'))
diffusionWeightLbpU8Flag = 1;
numTotalFeatures = numTotalFeatures + 59;
end
if ~isempty(strfind(paraStr,'diffusionWeightOverlapLBP'))
diffusionWeightOverlapLBPFlag = 1;
numTotalFeatures = numTotalFeatures + 833;
end
if ~isempty(strfind(paraStr,'ligRmvPpmLBP'))
ligRmvPpmLBPFlag = 1;
numTotalFeatures = numTotalFeatures + 833;
end
if ~isempty(strfind(paraStr,'ligRmvPpmDistortion'))
ligRmvPpmDistortionFlag = 1;
numTotalFeatures = numTotalFeatures + 115;
end
if ~isempty(strfind(paraStr,'ligRmvPpmGlcm'))
ligRmvPpmGlcmFlag = 1;
numTotalFeatures = numTotalFeatures + 88;
end
%% init according to the parameter
% testList = load(fullfile(setDir, 'test_sub_list.txt'));
testList = ['01'; '13'; '14'; '23'; '24'; '26'; '28'; '29'; '30'; '32'; '33'; '35'; '36'; '37'; '39'; '42'; '48'; '49'; '50'; '51' ];
trainList = ['02'; '03'; '05'; '06'; '07'; '08'; '09'; '11'; '12'; '21'; '22'; '34'; '53'; '54'; '55'];
% android * laptop * 50 (each video is selected only 50 frames)
trainGenuine = zeros( size(trainList, 1)*2*numFrames, numTotalFeatures );
% android * laptop * ipad * iphone * 50 (each video is selected only 50 frames)
trainReplay = zeros( size(trainList, 1)*4*numFrames, numTotalFeatures );
trainPrinted = zeros( size(trainList, 1)*2*numFrames, numTotalFeatures );
% android * laptop * 50 (each video is selected only 50 frames)
testGenuine = zeros( size(testList, 1)*2*numFrames, numTotalFeatures );
% android * laptop * ipad * iphone * 50 (each video is selected only 50 frames)
testReplay = zeros( size(testList, 1)*4*numFrames, numTotalFeatures );
testPrinted = zeros( size(testList, 1)*2*numFrames, numTotalFeatures );
if distortionFeatureFlag || glcmFeatureFlag || visualRhythmHoriFlag || overlapJPGLBPFlag || lbpU8FourierFlag || lbpU16Flag || diffusionWeightOverlapLBPFlag,
%% train feature extraction
for i = 1:size(trainList, 1),
realAndroid = strcat('real_client0', trainList(i,1), trainList(i,2), '_android_SD_scene01');
realLaptop = strcat('real_client0', trainList(i,1), trainList(i,2), '_laptop_SD_scene01');
attackAndroidIpad = strcat('attack_client0', trainList(i,1), trainList(i,2), '_android_SD_ipad_video_scene01');
attackAndroidIphone = strcat('attack_client0', trainList(i,1), trainList(i,2), '_android_SD_iphone_video_scene01');
attackAndroidPrinted = strcat('attack_client0', trainList(i,1), trainList(i,2), '_android_SD_printed_photo_scene01');
attackLaptopIpad = strcat('attack_client0', trainList(i,1), trainList(i,2), '_laptop_SD_ipad_video_scene01');
attackLaptopIphone = strcat('attack_client0', trainList(i,1), trainList(i,2), '_laptop_SD_iphone_video_scene01');
attackLaptopPrinted = strcat('attack_client0', trainList(i,1), trainList(i,2), '_laptop_SD_printed_photo_scene01');
%% real android & laptop
curDir1 = fullfile(setDir, realAndroid);
imglist1 = dir([curDir1 '\*.jpg']);
% imglist1 = dir([curDir1 '\*_LigRmv.ppm']);
curDir2 = fullfile(setDir, realLaptop);
imglist2 = dir([curDir2 '\*.jpg']);
% imglist2 = dir([curDir2 '\*_LigRmv.ppm']);
totalImgList = [imglist1; imglist2];
totalImgListSize = size(totalImgList, 1);
for j = 1: size(totalImgList, 1),
numCurrentFeatures = 0; % init numCurrentFeatures;
if ~isempty(strfind(totalImgList(j).name,'_android_'))
I = imread(fullfile(curDir1, totalImgList(j).name));
elseif ~isempty(strfind(totalImgList(j).name,'_laptop_'))
I = imread(fullfile(curDir2, totalImgList(j).name));
end
I = cutImage(I, cutTop, cutBottom);
% [ssr I msrcr ] = retinex(I);
if diffusionWeightOverlapLBPFlag,
ind=find(ismember(iterCntMSU(:, 1), strrep(totalImgList(j).name, '.jpg', '.ppm')));
ligRmvCnt = iterCntMSU{ind,2};
if ligRmvCnt <= 3,
iterCntDW = 8;
elseif ligRmvCnt <= 6,
iterCntDW = 7;
elseif ligRmvCnt <= 9,
iterCntDW = 6;
else
iterCntDW = 5;
end
end
if distortionFeatureFlag,
% blurriness features
blurriness = blurMetric(I);
nonRefBlurriness = noRefferencePerceptualBlurMetric(I(:, :, 1)); % using 2-d image
% Chromatic Moment Features
chromatic = chromaticMomentFeatures(I);
% color diversity features
diversity = colorDiversity(I);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(blurriness))) = blurriness;
numCurrentFeatures = numCurrentFeatures + length(blurriness);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(nonRefBlurriness))) = nonRefBlurriness;
numCurrentFeatures = numCurrentFeatures + length(nonRefBlurriness);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(chromatic'))) = chromatic';
numCurrentFeatures = numCurrentFeatures + length(chromatic');
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(diversity'))) = diversity';
numCurrentFeatures = numCurrentFeatures + length(diversity');
end
% glcm features
if glcmFeatureFlag,
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [0 1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [-1 1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [-1 0] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [-1 -1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [0 -1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [1 1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [1 0] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [1 -1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
end
% visual rhythm features
if visualRhythmHoriFlag,
visualRhythmFeatures = visualRhythm(I(:, :, 1)); % using 2-d image
horiReshape = reshape(visualRhythmFeatures.horizontalLine', 1, []);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(horiReshape))) = horiReshape;
numCurrentFeatures = numCurrentFeatures + length(horiReshape);
end
% lbp overlap 883 features
if overlapJPGLBPFlag,
%lbpFeatures = lbp(I,1,8,mappingU8,'h');
lbpFeatures = overlapLBP( I(:, :, 1), mappingU8, mappingU16 );
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(lbpFeatures))) = lbpFeatures;
numCurrentFeatures = numCurrentFeatures + length(lbpFeatures);
end
% lbp uniform 8, features with fourier spectrum
if lbpU8FourierFlag,
F = fourierSpectrum(I(:, :, 1), 'noiseMode', 'median', 7);
lbpFeatures = lbp(F,1,8,mappingU8,'h');
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(lbpFeatures))) = lbpFeatures;
numCurrentFeatures = numCurrentFeatures + length(lbpFeatures);
end
% lbp uniform 16, features
if lbpU16Flag,
lbpFeatures = lbp(I,2,16,mappingU16,'h');
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(lbpFeatures))) = lbpFeatures;
numCurrentFeatures = numCurrentFeatures + length(lbpFeatures);
end
% lbp diffusion weight uniform 8 features
% if diffusionWeightLbpU8Flag,
% lbpFeatures = diffusionWeightLBP(I, 1, 8, mappingU8, mappingU16);
% trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(lbpFeatures))) = lbpFeatures;
% numCurrentFeatures = numCurrentFeatures + length(lbpFeatures);
% end
if diffusionWeightOverlapLBPFlag,
lbpFeatures = diffusionWeightLBP(I, 1, 8, mappingU8, mappingU16, iterCntDW);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(lbpFeatures))) = lbpFeatures;
numCurrentFeatures = numCurrentFeatures + length(lbpFeatures);
end
end
%% attack replay
curDir1 = fullfile(setDir, attackAndroidIpad);
imglist1 = dir([curDir1 '\*.jpg']);
curDir2 = fullfile(setDir, attackAndroidIphone);
imglist2 = dir([curDir2 '\*.jpg']);
curDir3 = fullfile(setDir, attackLaptopIpad);
imglist3 = dir([curDir3 '\*.jpg']);
curDir4 = fullfile(setDir, attackLaptopIphone);
imglist4 = dir([curDir4 '\*.jpg']);
totalImgList = [imglist1; imglist2; imglist3; imglist4];
totalImgListSize = size(totalImgList, 1);
for j = 1: size(totalImgList, 1),
numCurrentFeatures = 0; % init numCurrentFeatures;
if ~isempty(strfind(totalImgList(j).name,'_android_'))
if ~isempty(strfind(totalImgList(j).name,'_ipad_'))
I = imread(fullfile(curDir1, totalImgList(j).name));
elseif ~isempty(strfind(totalImgList(j).name,'_iphone_'))
I = imread(fullfile(curDir2, totalImgList(j).name));
end
elseif ~isempty(strfind(totalImgList(j).name,'_laptop_'))
if ~isempty(strfind(totalImgList(j).name,'_ipad_'))
I = imread(fullfile(curDir3, totalImgList(j).name));
elseif ~isempty(strfind(totalImgList(j).name,'_iphone_'))
I = imread(fullfile(curDir4, totalImgList(j).name));
end
end
I = cutImage(I, cutTop, cutBottom);
% [ssr I msrcr ] = retinex(I);
if diffusionWeightOverlapLBPFlag,
ind=find(ismember(iterCntMSU(:, 1), strrep(totalImgList(j).name, '.jpg', '.ppm')));
ligRmvCnt = iterCntMSU{ind,2};
if ligRmvCnt <= 3,
iterCntDW = 8;
elseif ligRmvCnt <= 6,
iterCntDW = 7;
elseif ligRmvCnt <= 9,
iterCntDW = 6;
else
iterCntDW = 5;
end
end
if distortionFeatureFlag,
% blurriness features
blurriness = blurMetric(I);
nonRefBlurriness = noRefferencePerceptualBlurMetric(I(:, :, 1));
% Chromatic Moment Features
chromatic = chromaticMomentFeatures(I);
% color diversity features
diversity = colorDiversity(I);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(blurriness))) = blurriness;
numCurrentFeatures = numCurrentFeatures + length(blurriness);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(nonRefBlurriness))) = nonRefBlurriness;
numCurrentFeatures = numCurrentFeatures + length(nonRefBlurriness);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(chromatic'))) = chromatic';
numCurrentFeatures = numCurrentFeatures + length(chromatic');
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(diversity'))) = diversity';
numCurrentFeatures = numCurrentFeatures + length(diversity');
end
% glcm features
if glcmFeatureFlag,
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [0 1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [-1 1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [-1 0] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [-1 -1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [0 -1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [1 1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [1 0] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [1 -1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
end
% visual rhythm features
if visualRhythmHoriFlag,
visualRhythmFeatures = visualRhythm(I(:, :, 1));
horiReshape = reshape(visualRhythmFeatures.horizontalLine', 1, []);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(horiReshape))) = horiReshape;
numCurrentFeatures = numCurrentFeatures + length(horiReshape);
end
% lbp overlap 883 features
if overlapJPGLBPFlag,
%lbpFeatures = lbp(I,1,8,mappingU8,'h');
lbpFeatures = overlapLBP( I(:, :, 1), mappingU8, mappingU16 );
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(lbpFeatures))) = lbpFeatures;
numCurrentFeatures = numCurrentFeatures + length(lbpFeatures);
end
if lbpU8FourierFlag,
F = fourierSpectrum(I(:, :, 1), 'noiseMode', 'median', 7);
lbpFeatures = lbp(F,1,8,mappingU8,'h');
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(lbpFeatures))) = lbpFeatures;
numCurrentFeatures = numCurrentFeatures + length(lbpFeatures);
end
% lbp uniform 16 features
if lbpU16Flag,
lbpFeatures = lbp(I,2,16,mappingU16,'h');
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(lbpFeatures))) = lbpFeatures;
numCurrentFeatures = numCurrentFeatures + length(lbpFeatures);
end
% lbp diffusion weight uniform 8 features
if diffusionWeightOverlapLBPFlag,
lbpFeatures = diffusionWeightLBP(I, 1, 8, mappingU8, mappingU16, iterCntDW);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(lbpFeatures))) = lbpFeatures;
numCurrentFeatures = numCurrentFeatures + length(lbpFeatures);
end
end
%% attack print
curDir1 = fullfile(setDir, attackAndroidPrinted);
imglist1 = dir([curDir1 '\*.jpg']);
curDir2 = fullfile(setDir, attackLaptopPrinted);
imglist2 = dir([curDir2 '\*.jpg']);
totalImgList = [imglist1; imglist2];
totalImgListSize = size(totalImgList, 1);
for j = 1: size(totalImgList, 1),
numCurrentFeatures = 0; % init numCurrentFeatures;
if ~isempty(strfind(totalImgList(j).name,'_android_'))
I = imread(fullfile(curDir1, totalImgList(j).name));
elseif ~isempty(strfind(totalImgList(j).name,'_laptop_'))
I = imread(fullfile(curDir2, totalImgList(j).name));
end
I = cutImage(I, cutTop, cutBottom);
% [ssr I msrcr ] = retinex(I);
if diffusionWeightOverlapLBPFlag,
ind=find(ismember(iterCntMSU(:, 1), strrep(totalImgList(j).name, '.jpg', '.ppm')));
ligRmvCnt = iterCntMSU{ind,2};
if ligRmvCnt <= 3,
iterCntDW = 8;
elseif ligRmvCnt <= 6,
iterCntDW = 7;
elseif ligRmvCnt <= 9,
iterCntDW = 6;
else
iterCntDW = 5;
end
end
if distortionFeatureFlag,
% blurriness features
blurriness = blurMetric(I);
nonRefBlurriness = noRefferencePerceptualBlurMetric(I(:, :, 1));
% Chromatic Moment Features
chromatic = chromaticMomentFeatures(I);
% color diversity features
diversity = colorDiversity(I);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(blurriness))) = blurriness;
numCurrentFeatures = numCurrentFeatures + length(blurriness);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(nonRefBlurriness))) = nonRefBlurriness;
numCurrentFeatures = numCurrentFeatures + length(nonRefBlurriness);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(chromatic'))) = chromatic';
numCurrentFeatures = numCurrentFeatures + length(chromatic');
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(diversity'))) = diversity';
numCurrentFeatures = numCurrentFeatures + length(diversity');
end
% glcm features
if glcmFeatureFlag,
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [0 1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [-1 1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [-1 0] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [-1 -1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [0 -1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [1 1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [1 0] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [1 -1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
end
% visual rhythm features
if visualRhythmHoriFlag,
visualRhythmFeatures = visualRhythm(I(:, :, 1));
horiReshape = reshape(visualRhythmFeatures.horizontalLine', 1, []);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(horiReshape))) = horiReshape;
numCurrentFeatures = numCurrentFeatures + length(horiReshape);
end
% lbp overlap 883 features
if overlapJPGLBPFlag,
%lbpFeatures = lbp(I,1,8,mappingU8,'h');
lbpFeatures = overlapLBP( I(:, :, 1), mappingU8, mappingU16 );
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(lbpFeatures))) = lbpFeatures;
numCurrentFeatures = numCurrentFeatures + length(lbpFeatures);
end
if lbpU8FourierFlag,
F = fourierSpectrum(I(:, :, 1), 'noiseMode', 'median', 7);
lbpFeatures = lbp(F,1,8,mappingU8,'h');
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(lbpFeatures))) = lbpFeatures;
numCurrentFeatures = numCurrentFeatures + length(lbpFeatures);
end
% lbp uniform 16 features
if lbpU16Flag,
lbpFeatures = lbp(I,2,16,mappingU16,'h');
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(lbpFeatures))) = lbpFeatures;
numCurrentFeatures = numCurrentFeatures + length(lbpFeatures);
end
% lbp diffusion weight uniform 8 features
if diffusionWeightOverlapLBPFlag,
lbpFeatures = diffusionWeightLBP(I, 1, 8, mappingU8, mappingU16, iterCntDW);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(lbpFeatures))) = lbpFeatures;
numCurrentFeatures = numCurrentFeatures + length(lbpFeatures);
end
end
end
end
%% ppm extract training feature
if ligRmvPpmLBPFlag || ligRmvPpmDistortionFlag || ligRmvPpmGlcmFlag,
for i = 1:size(trainList, 1),
realAndroid = strcat('real_client0', trainList(i,1), trainList(i,2), '_android_SD_scene01');
realLaptop = strcat('real_client0', trainList(i,1), trainList(i,2), '_laptop_SD_scene01');
attackAndroidIpad = strcat('attack_client0', trainList(i,1), trainList(i,2), '_android_SD_ipad_video_scene01');
attackAndroidIphone = strcat('attack_client0', trainList(i,1), trainList(i,2), '_android_SD_iphone_video_scene01');
attackAndroidPrinted = strcat('attack_client0', trainList(i,1), trainList(i,2), '_android_SD_printed_photo_scene01');
attackLaptopIpad = strcat('attack_client0', trainList(i,1), trainList(i,2), '_laptop_SD_ipad_video_scene01');
attackLaptopIphone = strcat('attack_client0', trainList(i,1), trainList(i,2), '_laptop_SD_iphone_video_scene01');
attackLaptopPrinted = strcat('attack_client0', trainList(i,1), trainList(i,2), '_laptop_SD_printed_photo_scene01');
%% real android & laptop
curDir1 = fullfile(setDir, realAndroid);
imglist1 = dir([curDir1 '\*_LigRmv.ppm']);
% imglist1 = dir([curDir1 '\*_LigRmv.ppm']);
curDir2 = fullfile(setDir, realLaptop);
imglist2 = dir([curDir2 '\*_LigRmv.ppm']);
% imglist2 = dir([curDir2 '\*_LigRmv.ppm']);
totalImgList = [imglist1; imglist2];
totalImgListSize = size(totalImgList, 1);
for j = 1: size(totalImgList, 1),
numCurrentFeatures = 0; % init numCurrentFeatures;
if ~isempty(strfind(totalImgList(j).name,'_android_'))
I = imread(fullfile(curDir1, totalImgList(j).name));
elseif ~isempty(strfind(totalImgList(j).name,'_laptop_'))
I = imread(fullfile(curDir2, totalImgList(j).name));
end
I = cutImage(I, cutTop, cutBottom);
% [ssr I msrcr ] = retinex(I);
if ligRmvPpmDistortionFlag,
% blurriness features
blurriness = blurMetric(I);
nonRefBlurriness = noRefferencePerceptualBlurMetric(I(:, :, 1));
% Chromatic Moment Features
chromatic = chromaticMomentFeatures(I);
% color diversity features
diversity = colorDiversity(I);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(blurriness))) = blurriness;
numCurrentFeatures = numCurrentFeatures + length(blurriness);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(nonRefBlurriness))) = nonRefBlurriness;
numCurrentFeatures = numCurrentFeatures + length(nonRefBlurriness);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(chromatic'))) = chromatic';
numCurrentFeatures = numCurrentFeatures + length(chromatic');
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(diversity'))) = diversity';
numCurrentFeatures = numCurrentFeatures + length(diversity');
end
if ligRmvPpmGlcmFlag,
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [0 1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [-1 1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [-1 0] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [-1 -1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [0 -1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [1 1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [1 0] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [1 -1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
end
if ligRmvPpmLBPFlag,
lbpFeatures = overlapLBP( I, mappingU8, mappingU16 );
trainGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(lbpFeatures))) = lbpFeatures;
numCurrentFeatures = numCurrentFeatures + length(lbpFeatures);
end
end
%% attack replay
curDir1 = fullfile(setDir, attackAndroidIpad);
imglist1 = dir([curDir1 '\*_LigRmv.ppm']);
curDir2 = fullfile(setDir, attackAndroidIphone);
imglist2 = dir([curDir2 '\*_LigRmv.ppm']);
curDir3 = fullfile(setDir, attackLaptopIpad);
imglist3 = dir([curDir3 '\*_LigRmv.ppm']);
curDir4 = fullfile(setDir, attackLaptopIphone);
imglist4 = dir([curDir4 '\*_LigRmv.ppm']);
totalImgList = [imglist1; imglist2; imglist3; imglist4];
totalImgListSize = size(totalImgList, 1);
for j = 1: size(totalImgList, 1),
numCurrentFeatures = 0; % init numCurrentFeatures;
if ~isempty(strfind(totalImgList(j).name,'_android_'))
if ~isempty(strfind(totalImgList(j).name,'_ipad_'))
I = imread(fullfile(curDir1, totalImgList(j).name));
elseif ~isempty(strfind(totalImgList(j).name,'_iphone_'))
I = imread(fullfile(curDir2, totalImgList(j).name));
end
elseif ~isempty(strfind(totalImgList(j).name,'_laptop_'))
if ~isempty(strfind(totalImgList(j).name,'_ipad_'))
I = imread(fullfile(curDir3, totalImgList(j).name));
elseif ~isempty(strfind(totalImgList(j).name,'_iphone_'))
I = imread(fullfile(curDir4, totalImgList(j).name));
end
end
I = cutImage(I, cutTop, cutBottom);
% [ssr I msrcr ] = retinex(I);
if ligRmvPpmDistortionFlag,
% blurriness features
blurriness = blurMetric(I);
nonRefBlurriness = noRefferencePerceptualBlurMetric(I(:, :, 1));
% Chromatic Moment Features
chromatic = chromaticMomentFeatures(I);
% color diversity features
diversity = colorDiversity(I);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(blurriness))) = blurriness;
numCurrentFeatures = numCurrentFeatures + length(blurriness);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(nonRefBlurriness))) = nonRefBlurriness;
numCurrentFeatures = numCurrentFeatures + length(nonRefBlurriness);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(chromatic'))) = chromatic';
numCurrentFeatures = numCurrentFeatures + length(chromatic');
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(diversity'))) = diversity';
numCurrentFeatures = numCurrentFeatures + length(diversity');
end
if ligRmvPpmGlcmFlag,
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [0 1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [-1 1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [-1 0] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [-1 -1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [0 -1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [1 1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [1 0] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [1 -1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
end
if ligRmvPpmLBPFlag,
lbpFeatures = overlapLBP( I(:, :, 1), mappingU8, mappingU16 );
trainReplay((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(lbpFeatures))) = lbpFeatures;
numCurrentFeatures = numCurrentFeatures + length(lbpFeatures);
end
end
%% attack print
curDir1 = fullfile(setDir, attackAndroidPrinted);
imglist1 = dir([curDir1 '\*_LigRmv.ppm']);
curDir2 = fullfile(setDir, attackLaptopPrinted);
imglist2 = dir([curDir2 '\*_LigRmv.ppm']);
totalImgList = [imglist1; imglist2];
totalImgListSize = size(totalImgList, 1);
for j = 1: size(totalImgList, 1),
numCurrentFeatures = 0; % init numCurrentFeatures;
if ~isempty(strfind(totalImgList(j).name,'_android_'))
I = imread(fullfile(curDir1, totalImgList(j).name));
elseif ~isempty(strfind(totalImgList(j).name,'_laptop_'))
I = imread(fullfile(curDir2, totalImgList(j).name));
end
I = cutImage(I, cutTop, cutBottom);
% [ssr I msrcr ] = retinex(I);
if ligRmvPpmDistortionFlag,
% blurriness features
blurriness = blurMetric(I);
nonRefBlurriness = noRefferencePerceptualBlurMetric(I(:, :, 1));
% Chromatic Moment Features
chromatic = chromaticMomentFeatures(I);
% color diversity features
diversity = colorDiversity(I);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(blurriness))) = blurriness;
numCurrentFeatures = numCurrentFeatures + length(blurriness);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(nonRefBlurriness))) = nonRefBlurriness;
numCurrentFeatures = numCurrentFeatures + length(nonRefBlurriness);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(chromatic'))) = chromatic';
numCurrentFeatures = numCurrentFeatures + length(chromatic');
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(diversity'))) = diversity';
numCurrentFeatures = numCurrentFeatures + length(diversity');
end
if ligRmvPpmGlcmFlag,
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [0 1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [-1 1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [-1 0] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [-1 -1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [0 -1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [1 1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [1 0] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [1 -1] );
glcmFeatures = GLCMFeatures(glcm, 16);
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
end
if ligRmvPpmLBPFlag,
lbpFeatures = overlapLBP( I(:, :, 1), mappingU8, mappingU16 );
trainPrinted((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(lbpFeatures))) = lbpFeatures;
numCurrentFeatures = numCurrentFeatures + length(lbpFeatures);
end
end
end
end
if distortionFeatureFlag || glcmFeatureFlag || visualRhythmHoriFlag || overlapJPGLBPFlag || lbpU8FourierFlag || lbpU16Flag || diffusionWeightOverlapLBPFlag,
%% test feature extraction
for i = 1:size(testList, 1),
realAndroid = strcat('real_client0', testList(i,1), testList(i,2), '_android_SD_scene01');
realLaptop = strcat('real_client0', testList(i,1), testList(i,2), '_laptop_SD_scene01');
attackAndroidIpad = strcat('attack_client0', testList(i,1), testList(i,2), '_android_SD_ipad_video_scene01');
attackAndroidIphone = strcat('attack_client0', testList(i,1), testList(i,2), '_android_SD_iphone_video_scene01');
attackAndroidPrinted = strcat('attack_client0', testList(i,1), testList(i,2), '_android_SD_printed_photo_scene01');
attackLaptopIpad = strcat('attack_client0', testList(i,1), testList(i,2), '_laptop_SD_ipad_video_scene01');
attackLaptopIphone = strcat('attack_client0', testList(i,1), testList(i,2), '_laptop_SD_iphone_video_scene01');
attackLaptopPrinted = strcat('attack_client0', testList(i,1), testList(i,2), '_laptop_SD_printed_photo_scene01');
%% real android & laptop
curDir1 = fullfile(setDir, realAndroid);
imglist1 = dir([curDir1 '\*.jpg']);
curDir2 = fullfile(setDir, realLaptop);
imglist2 = dir([curDir2 '\*.jpg']);
totalImgList = [imglist1; imglist2];
totalImgListSize = size(totalImgList, 1);
for j = 1: size(totalImgList, 1),
numCurrentFeatures = 0; % init numCurrentFeatures;
if ~isempty(strfind(totalImgList(j).name,'_android_'))
I = imread(fullfile(curDir1, totalImgList(j).name));
elseif ~isempty(strfind(totalImgList(j).name,'_laptop_'))
I = imread(fullfile(curDir2, totalImgList(j).name));
end
I = cutImage(I, cutTop, cutBottom);
% [ssr I msrcr ] = retinex(I);
if diffusionWeightOverlapLBPFlag,
ind=find(ismember(iterCntMSU(:, 1), strrep(totalImgList(j).name, '.jpg', '.ppm')));
ligRmvCnt = iterCntMSU{ind,2};
if ligRmvCnt <= 3,
iterCntDW = 8;
elseif ligRmvCnt <= 6,
iterCntDW = 7;
elseif ligRmvCnt <= 9,
iterCntDW = 6;
else
iterCntDW = 5;
end
end
if distortionFeatureFlag,
% blurriness features
blurriness = blurMetric(I);
nonRefBlurriness = noRefferencePerceptualBlurMetric(I(:, :, 1));
% Chromatic Moment Features
chromatic = chromaticMomentFeatures(I);
% color diversity features
diversity = colorDiversity(I);
testGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(blurriness))) = blurriness;
numCurrentFeatures = numCurrentFeatures + length(blurriness);
testGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(nonRefBlurriness))) = nonRefBlurriness;
numCurrentFeatures = numCurrentFeatures + length(nonRefBlurriness);
testGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(chromatic'))) = chromatic';
numCurrentFeatures = numCurrentFeatures + length(chromatic');
testGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(diversity'))) = diversity';
numCurrentFeatures = numCurrentFeatures + length(diversity');
end
% glcm features
if glcmFeatureFlag,
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [0 1] );
glcmFeatures = GLCMFeatures(glcm, 16);
testGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [-1 1] );
glcmFeatures = GLCMFeatures(glcm, 16);
testGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [-1 0] );
glcmFeatures = GLCMFeatures(glcm, 16);
testGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [-1 -1] );
glcmFeatures = GLCMFeatures(glcm, 16);
testGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [0 -1] );
glcmFeatures = GLCMFeatures(glcm, 16);
testGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [1 1] );
glcmFeatures = GLCMFeatures(glcm, 16);
testGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [1 0] );
glcmFeatures = GLCMFeatures(glcm, 16);
testGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
glcm = graycomatrix( I(:, :, 1), 'NumLevels', 16, 'offset', [1 -1] );
glcmFeatures = GLCMFeatures(glcm, 16);
testGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(glcmFeatures'))) = glcmFeatures';
numCurrentFeatures = numCurrentFeatures + length(glcmFeatures');
end
% visual rhythm features
if visualRhythmHoriFlag,
visualRhythmFeatures = visualRhythm(I(:, :, 1));
horiReshape = reshape(visualRhythmFeatures.horizontalLine', 1, []);
testGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(horiReshape))) = horiReshape;
numCurrentFeatures = numCurrentFeatures + length(horiReshape);
end
% lbp overlap 883 features
if overlapJPGLBPFlag,
%lbpFeatures = lbp(I,1,8,mappingU8,'h');
lbpFeatures = overlapLBP( I(:, :, 1), mappingU8, mappingU16 );
testGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(lbpFeatures))) = lbpFeatures;
numCurrentFeatures = numCurrentFeatures + length(lbpFeatures);
end
if lbpU8FourierFlag,
F = fourierSpectrum(I(:, :, 1), 'noiseMode', 'median', 7);
lbpFeatures = lbp(F,1,8,mappingU8,'h');
testGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(lbpFeatures))) = lbpFeatures;
numCurrentFeatures = numCurrentFeatures + length(lbpFeatures);
end
% lbp uniform 16 features
if lbpU16Flag,
lbpFeatures = lbp(I,2,16,mappingU16,'h');
testGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(lbpFeatures))) = lbpFeatures;
numCurrentFeatures = numCurrentFeatures + length(lbpFeatures);
end
% lbp diffusion weight uniform 8 features
if diffusionWeightOverlapLBPFlag,
lbpFeatures = diffusionWeightLBP(I, 1, 8, mappingU8, mappingU16, iterCntDW);
testGenuine((i-1)*totalImgListSize+j, (numCurrentFeatures+1) : (numCurrentFeatures + length(lbpFeatures))) = lbpFeatures;
numCurrentFeatures = numCurrentFeatures + length(lbpFeatures);
end
end
%% attack replay
curDir1 = fullfile(setDir, attackAndroidIpad);
imglist1 = dir([curDir1 '\*.jpg']);
curDir2 = fullfile(setDir, attackAndroidIphone);
imglist2 = dir([curDir2 '\*.jpg']);
curDir3 = fullfile(setDir, attackLaptopIpad);
imglist3 = dir([curDir3 '\*.jpg']);
curDir4 = fullfile(setDir, attackLaptopIphone);
imglist4 = dir([curDir4 '\*.jpg']);
totalImgList = [imglist1; imglist2; imglist3; imglist4];
totalImgListSize = size(totalImgList, 1);
for j = 1: size(totalImgList, 1),
numCurrentFeatures = 0; % init numCurrentFeatures;
if ~isempty(strfind(totalImgList(j).name,'_android_'))
if ~isempty(strfind(totalImgList(j).name,'_ipad_'))
I = imread(fullfile(curDir1, totalImgList(j).name));
elseif ~isempty(strfind(totalImgList(j).name,'_iphone_'))