matlab.rar_DDE_MATLAB ddesd_S-UNIWARD

function stego = S_UNIWARD(coverPath, payload) % ------------------------------------------------------------------------- % Copyright (c) 2013 DDE Lab, Binghamton University, NY. % All Rights Reserved. % ------------------------------------------------------------------------- % Permission to use, copy, modify, and distribute this software for % educational, research and non-profit purposes, without fee, and without a % written agreement is hereby granted, provided that this copyright notice % appears in all copies. The program is supplied "as is," without any % accompanying services from DDE Lab. DDE Lab does not warrant the % operation of the program will be uninterrupted or error-free. The % end-user understands that the program was developed for research purposes % and is advised not to rely exclusively on the program for any reason. In % no event shall Binghamton University or DDE Lab be liable to any party % for direct, indirect, special, incidental, or consequential damages, % including lost profits, arising out of the use of this software. DDE Lab % disclaims any warranties, and has no obligations to provide maintenance, % support, updates, enhancements or modifications. % ------------------------------------------------------------------------- % Contact: [email protected] | [email protected] | October 2012 % http://dde.binghamton.edu/download/steganography % ------------------------------------------------------------------------- % This function simulates embedding using S-UNIWARD steganographic % algorithm. For more deatils about the individual submodels, please see % the publication [1]. % ------------------------------------------------------------------------- % Input: coverPath ... path to the image % payload ..... payload in bits per pixel % Output: stego ....... resulting image with embedded payload % ------------------------------------------------------------------------- % PAPER % ------------------------------------------------------------------------- sgm = 1; %% Get 2D wavelet filters - Daubechies 8 % 1D high pass decomposition filter hpdf = [-0.0544158422, 0.3128715909, -0.6756307363, 0.5853546837, 0.0158291053, -0.2840155430, -0.0004724846, 0.1287474266, 0.0173693010, -0.0440882539, ... -0.0139810279, 0.0087460940, 0.0048703530, -0.0003917404, -0.0006754494, -0.0001174768]; % 1D low pass decomposition filter lpdf = (-1).^(0:numel(hpdf)-1).*fliplr(hpdf); % construction of 2D wavelet filters F{1} = lpdf'*hpdf; F{2} = hpdf'*lpdf; F{3} = hpdf'*hpdf; %% Get embedding costs % inicialization cover = double(imread(coverPath)); wetCost = 10^8; [k,l] = size(cover); % add padding padSize = max([size(F{1})'; size(F{2})'; size(F{3})']); coverPadded = padarray(cover, [padSize padSize], 'symmetric'); xi = cell(3, 1); for fIndex = 1:3 % compute residual R = conv2(coverPadded, F{fIndex}, 'same'); % compute suitability xi{fIndex} = conv2(1./(abs(R)+sgm), rot90(abs(F{fIndex}), 2), 'same'); % correct the suitability shift if filter size is even if mod(size(F{fIndex}, 1), 2) == 0, xi{fIndex} = circshift(xi{fIndex}, [1, 0]); end; if mod(size(F{fIndex}, 2), 2) == 0, xi{fIndex} = circshift(xi{fIndex}, [0, 1]); end; % remove padding xi{fIndex} = xi{fIndex}(((size(xi{fIndex}, 1)-k)/2)+1:end-((size(xi{fIndex}, 1)-k)/2), ((size(xi{fIndex}, 2)-l)/2)+1:end-((size(xi{fIndex}, 2)-l)/2)); end % compute embedding costs \rho rho = xi{1} + xi{2} + xi{3}; % adjust embedding costs rho(rho > wetCost) = wetCost; % threshold on the costs rho(isnan(rho)) = wetCost; % if all xi{} are zero threshold the cost rhoP1 = rho; rhoM1 = rho; rhoP1(cover==255) = wetCost; % do not embed +1 if the pixel has max value rhoM1(cover==0) = wetCost; % do not embed -1 if the pixel has min value %% Embedding simulator stego = EmbeddingSimulator(cover, rhoP1, rhoM1, payload*numel(cover), false); %% -------------------------------------------------------------------------------------------------------------------------- % Embedding simulator simulates the embedding made by the best possible ternary coding method (it embeds on the entropy bound). % This can be achieved in practice using "Multi-layered syndrome-trellis codes" (ML STC) that are asymptotically aproaching the bound. function [y] = EmbeddingSimulator(x, rhoP1, rhoM1, m, fixEmbeddingChanges) n = numel(x); lambda = calc_lambda(rhoP1, rhoM1, m, n); pChangeP1 = (exp(-lambda .* rhoP1))./(1 + exp(-lambda .* rhoP1) + exp(-lambda .* rhoM1)); pChangeM1 = (exp(-lambda .* rhoM1))./(1 + exp(-lambda .* rhoP1) + exp(-lambda .* rhoM1)); if fixEmbeddingChanges == 1 RandStream.setGlobalStream(RandStream('mt19937ar','seed',139187)); else RandStream.setGlobalStream(RandStream('mt19937ar','Seed',sum(100*clock))); end randChange = rand(size(x)); y = x; y(randChange < pChangeP1) = y(randChange < pChangeP1) + 1; y(randChange >= pChangeP1 & randChange < pChangeP1+pChangeM1) = y(randChange >= pChangeP1 & randChange < pChangeP1+pChangeM1) - 1; function lambda = calc_lambda(rhoP1, rhoM1, message_length, n) l3 = 1e+3; m3 = double(message_length + 1); iterations = 0; while m3 > message_length l3 = l3 * 2; pP1 = (exp(-l3 .* rhoP1))./(1 + exp(-l3 .* rhoP1) + exp(-l3 .* rhoM1)); pM1 = (exp(-l3 .* rhoM1))./(1 + exp(-l3 .* rhoP1) + exp(-l3 .* rhoM1)); m3 = ternary_entropyf(pP1, pM1); iterations = iterations + 1; if (iterations > 10) lambda = l3; return; end end l1 = 0; m1 = double(n); lambda = 0; alpha = double(message_length)/n; % limit search to 30 iterations % and require that relative payload embedded is roughly within 1/1000 of the required relative payload while (double(m1-m3)/n > alpha/1000.0 ) && (iterations<30) lambda = l1+(l3-l1)/2; pP1 = (exp(-lambda .* rhoP1))./(1 + exp(-lambda .* rhoP1) + exp(-lambda .* rhoM1)); pM1 = (exp(-lambda .* rhoM1))./(1 + exp(-lambda .* rhoP1) + exp(-lambda .* rhoM1)); m2 = ternary_entropyf(pP1, pM1); if m2 < message_length l3 = lambda; m3 = m2; else l1 = lambda; m1 = m2; end iterations = iterations + 1; end end function Ht = ternary_entropyf(pP1, pM1) p0 = 1-pP1-pM1; P = [p0(:); pP1(:); pM1(:)]; H = -((P).*log2(P)); H((P<eps) | (P > 1-eps)) = 0; Ht = sum(H); end end end