【图像隐藏】基于matlab混合DWT-HD-SVD数字图像水印方法技术【含Matlab源码 2007期】

一、SVD数字水印简介

1 水印嵌入算法
水印W和载体图像C是水印嵌入算法的输入，含水印图像C是输出。C、W、C的尺寸分别为M×M、N×N和M×M。此外，该水印方法可以容纳多个尺寸不同的水印，并对宿主图像进行r级DWT分解。水印嵌入的过程如图1所示，具体的嵌入步骤为：

(1)基于R级DWT，将C分解为LL、LH、HL、HH的分量，其中R=log2(M/N)。

(2)HD在LL上执行，显示为：
PHPT=HD(LL) (9)

(3)将SVD应用于H：
HUwHSwHVwT=SVD(H) (10)

(4)W与SVD结合使用：
UwSwVwT=SVD(W) (11)

接着使用Logistic映射产生的混沌系统对Uw、VwT的运算加密，加密后的两个组件标记为Uw1和VTw1。

(5)通过将HSw和Sw与缩放因子α的乘积相加，计算嵌入的奇异值HSw：
HSw=HSw+αSw (12)

(6)带水印的子带H是通过逆奇异值分解生成的，即：
H=HUwHSw*HVwT (13)

(7)基于给出的逆HD，重建一个新的低频近似子带LL*：
LL*=PH*PT(14)

(8)水印图像C*是通过执行逆r级小波变换获得的，水印嵌入流程图如图2所示。

图2 水印嵌入流程图

2 水印提取算法
在水印提取算法中，加水印的宿主图像C是输入，提取的水印W是输出。W*的大小为N×N。水印提取过程如图3所示，具体提取步骤为：

(1)经过水印的主图像C*通过r级DWT分解为四个子带，分别为LLw、LHw、HLw、HHw。

(2)对LLw执行HD
PwHwPwT=HD(LLw) (15)

(3)将SVD应用于Hw，
HUwHSbwHVw*T=SVD(Hw)(16)

(4)提取的奇异值Sw为：
Sw=(HSbw*-HSw*)/α (17)

(5)Uw1和Vw1T由混沌系统解密，解密的两个组件被标记为Uw2和Vw2T。提取的水印W通过逆奇异值分解(SVD)进行重构，公式为：
W=Uw2Sw*Vw2T(18)
水印提取流程图如图3所示。

图3 水印提取流程图

二、部分源代码

clc
clear
close all

%% Import image
cover_image=imread('lena512.bmp');
watermark_logo=imread('cameraman.tif');
%% Plot cover image and watermark image
figure
subplot(1,2,1);
imshow(cover_image);
title('Cover image: 512 x 512');
subplot(1,2,2);
imshow(watermark_logo);
title('Watermark image: 256 x 256');

%% Example: watermark embedding and exraction  alpha=0.1 Attack: Sharpening
method = 'DWT-HD-SVD';          % Apply 'DWT-HD-SVD Method
alpha = 0.1;
attack = 'Motion blur';   % You can choose other attacks
param = 0.5;                    % attack parameter
[watermarked_image, extracted_watermark] = watermark(cover_image,...
    watermark_logo,method,alpha,attack,param);

% Plot results
figure;
subplot(2, 2, 1);
imshow(cover_image);
xlabel('a) Cover image');
subplot(2, 2, 2);
imshow(watermarked_image);
xlabel('b) Watermarked image');
subplot(2, 2, 3);
imshow(watermark_logo);
xlabel('c) Watermark logo');
subplot(2, 2, 4);
imshow(extracted_watermark);
xlabel('d) Extracted watermark');
sgtitle(['DWT-HD-SVD method \alpha = '+string(alpha) attack]);

%% NC vs alpha DWT-HD-SVD figure 5
%  Plot normalized correlation  for different alpha
method = 'DWT-HD-SVD';
alpha =0.005:0.005:0.2;
attacks = {'No Attack'; 'Gaussian low-pass filter'; 'Median';...
    'Gaussian noise'; 'Salt and pepper noise';'Speckle noise';...
    'JPEG compression'; 'JPEG2000 compression'; 'Sharpening attack';...
    'Histogram equalization'; 'Average filter'; 'Motion blur'};

% Attack papameters
params = [0; 3; 3; 0.001; 0; 0; 50; 12; 0.8; 0; 0; 0];
NC = NC_alpha(cover_image,watermark_logo,method,alpha,attacks,params);
%%  plot NC vs alpha figure 5
NC_plot(alpha,NC,attacks);

%% PSNR vs alpha DWT-HD-SVD
method = 'DWT-HD-SVD';
alpha =0.005:0.005:0.2;
attacks = {'No Attack'; 'Gaussian low-pass filter'; 'Median'; 'Gaussian noise';...
    'Salt and pepper noise';'Speckle noise'; 'JPEG compression';...
    'JPEG2000 compression'; 'Sharpening attack'; 'Histogram equalization';...
    'Average filter'; 'Motion blur'};
params = [0; 3; 3; 0.001; 0; 0; 50; 12; 0.8; 0; 0; 0];
PSNR = PSNR_alpha(cover_image,watermark_logo,method,alpha,attacks,params);

%% plot PSNR vs alpha
PSNR_plot(alpha,PSNR,attacks);

%% SSIM vs alpha DWT-HD-SVD figure 7 paper (see README)
method = 'DWT-HD-SVD';
alpha =0.005:0.005:0.2;
attacks = {'No Attack'; 'Gaussian low-pass filter'; 'Median'; 'Gaussian noise';...
    'Salt and pepper noise';'Speckle noise'; 'JPEG compression';...
    'JPEG2000 compression'; 'Sharpening attack'; 'Histogram equalization';...
    'Average filter'; 'Motion blur'};
params = [0; 3; 3; 0.001; 0; 0; 50; 12; 0.8; 0; 0; 0];
SSIM = SSIM_alpha(cover_image,watermark_logo,method,alpha,attacks,params);

%% plot SSIM vs alpha
SSIM_plot(alpha,SSIM,attacks);

%% FIGURE 8. Invisibility performance: Watermarked images and corresponding extracted
%  watermarks with various sizes and their corresponding PSNRs, SSIMs and NCs.

method = 'DWT-HD-SVD';
alpha =0.05;
attack = 'No Attack';
param = 0;

figure
for i=1:3
    watermark_logoi = imresize(watermark_logo,2^(-i+1));
    [watermarked_image, extracted_watermark] = watermark(cover_image,watermark_logoi,method,alpha,attack,param);
    PSNR = psnr(watermarked_image, cover_image);
    SSIM = ssim(watermarked_image, cover_image);
    NC = nc(watermark_logoi,extracted_watermark);
    subplot(2,3,i);
    imshow(watermarked_image);
    title(['watermarked image';'watermark size '+string(length(watermark_logoi))+'x'+string(length(watermark_logoi))]);
    xlabel(['PSNR='+string(PSNR);'SSIM='+string(SSIM)]);
    subplot(2,3,i+3);
    imshow(extracted_watermark);
    title('extracted watermark');
    xlabel('NC='+string(NC));
end
sgtitle('DWT-HD-SVD: Invisibility performance: watermarks with various sizes; alpha='+string(alpha)+'; No Attack');

%% plot watermarked image for different attacks and watermark sizes

method = 'DWT-HD-SVD';
alpha =0.05;
attacks = {'No Attack'; 'Gaussian low-pass filter'; 'Median'; 'Gaussian noise';...
    'Salt and pepper noise';'Speckle noise'; 'JPEG compression';...
    'JPEG2000 compression'; 'Sharpening attack'; 'Histogram equalization';...
    'Average filter'; 'Motion blur'};
params = [0; 3; 3; 0.001; 0; 0; 50; 12; 0.8; 0; 0; 0];
for i=3:-1:1
watermark_logoi = imresize(watermark_logo,2^(1-i));
figure
for j=1:length(attacks)
    
    attack = string(attacks(j));
    param = params(j);
    [watermarked_image, extracted_watermark] = watermark(cover_image,watermark_logoi,method,alpha,attack,param);
    PSNR = psnr(watermarked_image, cover_image);
    SSIM = ssim(watermarked_image, cover_image);
    subplot(3,4,j);
    imshow(watermarked_image);
    xlabel(['PSNR='+string(PSNR);'SSIM='+string(SSIM)]);
    title(attack);
end
sgtitle(['DWT-HD-SVD: Attacked watermarked image; Size = '+string(length(watermark_logoi))+'x'+string(length(watermark_logoi))+'; \alpha = '+string(alpha)]);
end

  

 

  
 
 

  
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三、运行结果

四、matlab版本及参考文献

1 matlab版本
2014a

2 参考文献
[1]甘志超,刘丹.基于HD和SVD的DWT变换的数字图像水印[J].现代信息科技. 2022,6(01)

3 备注
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原文链接：qq912100926.blog.csdn.net/article/details/126085939