1 簡介

提出了一種基于正交拉丁方置亂+混沌的圖像置亂加密算法.借助MATLAB2014軟件平臺(tái)編程實(shí)現(xiàn),并研究了加密算法的抗破損能力.實(shí)驗(yàn)結(jié)果表明:該算法的加密效果良好,圖像的抗破損能力強(qiáng).

2 部分代碼

% RegisterFourierMellin% This code is the result of my messing around with Matlab investigating % various image registration techniques. ?I came across the excellent % (although perhaps a little messy and buggy) fm_gui_v2 from Adam Wilmer% here:% http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=3000&objectType=file% Because my needs are essentially the algorithm itself in a neat and tidy% format to enable an easier conversion to C++, I've extracted what I think% is the essence of the Fourier Mellin method into this file. ?Obviously% I haven't included a GUI. ?In order to test it, you need to set the first% two statements to load in 2 image files of the same size, in 8 bit grayscale.% I took lena and then used Gimp to rotate/shift/crop at various angles.% It isn't sub-pixel accurate, although I'm aware of methods to achieve% this by extracting the peaks around the peak of the phase correlation and% finding the maxima (least squares perhaps). ?% The methods towards the end of the program are cribbed directly from% Adam's version. ?I'm new to Matlab (been playing with it for less than% a fortnight), so I wasn't able to get my head around his log polar transform% or the final "blending" of the two images together.% I'd like to thank Adam for publishing his version. ?Without it I'd never% have known I had to take the log polar transform of the magnitude of the% FFT, rather than the log polar transform of the original image!function combImage=RegnisterFourierMellin(I1,I2) ? ?% The procedure is as follows (note this does not compute scale) ? ?% (1) ? Read in I1 - the image to register against ? ?% (2) ? Read in I2 - the image to register ? ?% (3) ? Take the FFT of I1, shifting it to center on zero frequency ? ?% (4) ? Take the FFT of I2, shifting it to center on zero frequency ? ?% (5) ? Convolve the magnitude of (3) with a high pass filter ? ?% (6) ? Convolve the magnitude of (4) with a high pass filter ? ?% (7) ? Transform (5) into log polar space ? ?% (8) ? Transform (6) into log polar space ? ?% (9) ? Take the FFT of (7) ? ?% (10) ?Take the FFT of (8) ? ?% (11) ?Compute phase correlation of (9) and (10) ? ?% (12) ?Find the location (x,y) in (11) of the peak of the phase correlation ? ?% (13) ?Compute angle (360 / Image Y Size) * y from (12) ? ?% (14) ?Rotate the image from (2) by - angle from (13) ? ?% (15) ?Rotate the image from (2) by - angle + 180 from (13) ? ?% (16) ?Take the FFT of (14) ? ?% (17) ?Take the FFT of (15) ? ?% (18) ?Compute phase correlation of (3) and (16) ? ?% (19) ?Compute phase correlation of (3) and (17) ? ?% (20) ?Find the location (x,y) in (18) of the peak of the phase correlation ? ?% (21) ?Find the location (x,y) in (19) of the peak of the phase correlation ? ?% (22) ?If phase peak in (20) > phase peak in (21), (y,x) from (20) is the translation ? ?% (23a) Else (y,x) from (21) is the translation and also: ? ?% (23b) If the angle from (13) < 180, add 180 to it, else subtract 180 from it. ? ?% (24) ?Tada! ? ?% Requires (ouch): ? ?% 6 x FFT ? ?% 4 x FFT Shift ? ?% 3 x IFFT ? ?% 2 x Log Polar ? ?% 3 x Phase Correlations ? ?% 2 x High Pass Filter ? ?% 2 x Image Rotation ? ?% --------------------------------------------------------------------- ? ?% Load first image (I1) ? % I1 = imread('lena.bmp'); ? ?% Load second image (I2) ? % I2 = imread('lena_cropped_shifted.bmp'); ? ?% Convert both to FFT, centering on zero frequency component ? ?SizeX = size(I1, 1); ? ?SizeY = size(I1, 2); ? ?FA = fftshift(fft2(I1)); ? ?FB = fftshift(fft2(I2)); ? ?% Output (FA, FB) ? ?% --------------------------------------------------------------------- ? ?% Convolve the magnitude of the FFT with a high pass filter) ? ?IA = hipass_filter(size(I1, 1),size(I1,2)).*abs(FA); ? ? ?IB = hipass_filter(size(I2, 1),size(I2,2)).*abs(FB); ? ? ?% Transform the high passed FFT phase to Log Polar space ? ?L1 = transformImage(IA, SizeX, SizeY, SizeX, SizeY, 'nearest', size(IA) / 2, 'valid'); ? ?L2 = transformImage(IB, SizeX, SizeY, SizeX, SizeY, 'nearest', size(IB) / 2, 'valid'); ? ?% Convert log polar magnitude spectrum to FFT ? ?THETA_F1 = fft2(L1); ? ?THETA_F2 = fft2(L2); ? ?% Compute cross power spectrum of F1 and F2 ? ?a1 = angle(THETA_F1); ? ?a2 = angle(THETA_F2); ? ?THETA_CROSS = exp(i * (a1 - a2)); ? ?THETA_PHASE = real(ifft2(THETA_CROSS)); ? ?% ? ?combImage = plant; ? ?for p=1:total_height ? ? ? ?for q=1:total_width ? ? ? ? ? ?if (combImage(p,q)==0) ? ? ? ? ? ? ? ?combImage(p,q) = bleed(p,q); ? ? ? ? ? ?end ? ? ? ?end ? ?end ? ?% Show final image ? % imshow(combImage, [0 255]); % ---------------------------------------------------------------------% Performs Log Polar Transformfunction [r,g,b] = transformImage(A, Ar, Ac, Nrho, Ntheta, Method, Center, Shape)% Inputs: ? A ? ? ? the input image% ? ? ? ? ? Nrho ? ?the desired number of rows of transformed image% ? ? ? ? ? Ntheta ?the desired number of columns of transformed image% ? ? ? ? ? Method ?interpolation method (nearest,bilinear,bicubic)% ? ? ? ? ? Center ?origin of input image% ? ? ? ? ? Shape ? output size (full,valid)% ? ? ? ? ? Class ? storage class of Aglobal rho;theta = linspace(0,2*pi,Ntheta+1); theta(end) = [];switch Shapecase 'full' ? ?corners = [1 1;Ar 1;Ar Ac;1 Ac]; ? ?d = max(sqrt(sum((repmat(Center(:)',4,1)-corners).^2,2)));case 'valid' ? ?d = min([Ac-Center(1) Center(1)-1 Ar-Center(2) Center(2)-1]);endminScale = 1;rho = logspace(log10(minScale),log10(d),Nrho)'; ?% default 'base 10' logspace - play with d to change the scale of the log axis% convert polar coordinates to cartesian coordinates and centerxx = rho*cos(theta) + Center(1);yy = rho*sin(theta) + Center(2);if nargout==3 ?if strcmp(Method,'nearest'), % Nearest neighbor interpolation ? ?r=interp2(A(:,:,1),xx,yy,'nearest'); ? ?g=interp2(A(:,:,2),xx,yy,'nearest'); ? ?b=interp2(A(:,:,3),xx,yy,'nearest'); ?elseif strcmp(Method,'bilinear'), % Linear interpolation ? ?r=interp2(A(:,:,1),xx,yy,'linear'); ? ?g=interp2(A(:,:,2),xx,yy,'linear'); ? ?b=interp2(A(:,:,3),xx,yy,'linear'); ?elseif strcmp(Method,'bicubic'), % Cubic interpolation ? ?r=interp2(A(:,:,1),xx,yy,'cubic'); ? ?g=interp2(A(:,:,2),xx,yy,'cubic'); ? ?b=interp2(A(:,:,3),xx,yy,'cubic'); ?else ? ?error(['Unknown interpolation method: ',method]); ?end ?% any pixels outside , pad with black ?mask= (xx>Ac) | (xx<1) | (yy>Ar) | (yy<1); ?r(mask)=0; ?g(mask)=0; ?b(mask)=0;else ?if strcmp(Method,'nearest'), % Nearest neighbor interpolation ? ?r=interp2(A,xx,yy,'nearest'); ?elseif strcmp(Method,'bilinear'), % Linear interpolation ? ?r=interp2(A,xx,yy,'linear'); ?elseif strcmp(Method,'bicubic'), % Cubic interpolation ? ?r=interp2(A,xx,yy,'cubic'); ?else ? ?error(['Unknown interpolation method: ',method]); ?end ?% any pixels outside warp, pad with black ?mask= (xx>Ac) | (xx<1) | (yy>Ar) | (yy<1); ?r(mask)=0;end ?% ---------------------------------------------------------------------% Returns high-pass filterfunction H = hipass_filter(ht,wd)% hi-pass filter function% ...designed for use with Fourier-Mellin stuffres_ht = 1 / (ht-1);res_wd = 1 / (wd-1);eta = cos(pi*(-0.5:res_ht:0.5));neta = cos(pi*(-0.5:res_wd:0.5));X = eta'*neta;H=(1.0-X).*(2.0-X);

3 仿真結(jié)果

4 參考文獻(xiàn)

【圖像加密】基于正交拉丁方置亂+混沌圖像加密解密含Matlab源碼

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