www.pudn.com > Block-Based_Image_Steganalysis_v1.zip > settings1.m, change:2010-09-01,size:1556b


function [ WINDOWS, MATLAB, BLOCK_SIZE, BLOCK_NO, CLASSIFIER_TYPE, R, S, ... 
           CLASS_NO, FIND_CODEWORDS, TRAIN_PROCESS, ITERATIONS, ... 
           image_number, TEST_IMAGE_NO, TOTAL_SAMPLE, ... 
           COVER_UCID, STEGO_UCID, COVER_INRIA, STEGO_INRIA ] = settings1(); 
% WINDOWS = 1    Windows XP = Laptop Computer 
% WINDOWS = 2    Windows Vista = Desktop Computer 
WINDOWS = 1; 
% MATLAB = 1    First Matlab Program 
% MATLAB = 2    Second Matlab Program 
MATLAB = 1; 
COVER_UCID = 'D:\Research\Multimedia\UCID\Cover\Feature64\'; 
STEGO_UCID = 'D:\Research\Multimedia\UCID\mbs\Feature64\'; 
COVER_INRIA = 'D:\Research\Multimedia\Holidays\Cover\Feature64\'; 
STEGO_INRIA = 'D:\Research\Multimedia\Holidays\OutGuess\Feature64\'; 
% BLOCK_SIZE = 16 or 32 
BLOCK_SIZE = 32; 
BLOCK_NO = 384*512/(BLOCK_SIZE*BLOCK_SIZE); 
% CLASSIFIER_TYPE = 1: Linear Classifier 
% CLASSIFIER_TYPE = 2: Support Vector Machine 
CLASSIFIER_TYPE = 1; 
% Regulation Parameter for Linear Classifier 
R = 0.0001; S = 0.0001; 
% CLASS_NO = 8, 16, 32 
CLASS_NO = 4; 
% FIND_CODEWORDS = 1    Find Codewords 
% FIND_CODEWORDS = 0    Skip Find Codewords 
FIND_CODEWORDS = 1; 
% TRAIN_PROCESS = 1    Execute Training  
% TRAIN_PROCESS = 0    Skip Training 
TRAIN_PROCESS = 1; 
% STEGANOGRAPHY = 1    Model-based Steganography 
% STEGANOGRAPHY = 2    Perturbed Quantization 
STEGANOGRAPHY = 2; 
ITERATIONS = 1; 
% Number of Images for Training 
image_number = 1338; 
% Number of Images for Testing 
TEST_IMAGE_NO = 1491; 
TOTAL_SAMPLE = 20000;

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