【图像加密】基于正交拉丁方置乱+混沌图像加密解密含Matlab源码

 1 简介

提出了一种基于正交拉丁方置乱+混沌的图像置乱加密算法.借助MATLAB2014软件平台编程实现,并研究了加密算法的抗破损能力.实验结果表明:该算法的加密效果良好,图像的抗破损能力强.

2 部分代码

% 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 Transform
function [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 A
global rho;
theta = linspace(0,2*pi,Ntheta+1); theta(end) = [];
switch Shape
case '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]);
end
minScale = 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 center
xx = 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 filter
function H = hipass_filter(ht,wd)
% hi-pass filter function
% ...designed for use with Fourier-Mellin stuff
res_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 仿真结果

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4 参考文献

【图像加密】基于正交拉丁方置乱+混沌图像加密解密含Matlab源码

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