【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】

【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

%% Import imagecover_image=imread('lena512.bmp');watermark_logo=imread('cameraman.tif');%% Plot cover image and watermark imagefiguresubplot(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: Sharpeningmethod = 'DWT-HD-SVD'; % Apply 'DWT-HD-SVD Methodalpha = 0.1;attack = 'Motion blur'; % You can choose other attacksparam = 0.5; % attack parameter[watermarked_image, extracted_watermark] = watermark(cover_image,... watermark_logo,method,alpha,attack,param);% Plot resultsfigure;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 alphamethod = '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 papametersparams = [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 5NC_plot(alpha,NC,attacks);%% PSNR vs alpha DWT-HD-SVDmethod = '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 alphaPSNR_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 alphaSSIM_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;figurefor 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));endsgtitle('DWT-HD-SVD: Invisibility performance: watermarks with various sizes; alpha='+string(alpha)+'; No Attack');%% plot watermarked image for different attacks and watermark sizesmethod = '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:-1:1watermark_logoi = imresize(watermark_logo,2^(1-i));figurefor 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);endsgtitle(['DWT-HD-SVD: Attacked watermarked image; Size = '+string(length(watermark_logoi))+'x'+string(length(watermark_logoi))+'; \alpha = '+string(alpha)]);

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)

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