matlab-基于DWT-HD-SVD混合技术的数字图像水印方法测试了jpeg压缩,jpeg2000压缩,高斯滤波,中值滤波,高

clc; clear; close all; warning off; addpath(genpath(pwd)); %% 基于DWT-HD-SVD混合技术的数字图像水印方法 %% 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 %% FIGURE 10. Extracted watermarks from the attacked watermarked images % Evaluate robustness performance 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); NC = nc(watermark_logoi,extracted_watermark); subplot(3,4,j); imshow(extracted_watermark); xlabel([attack 'NC='+string(NC)]); end sgtitle(['DWT-HD-SVD: Extracted watermarks image from the attacked watermarked images; Size = '+string(length(watermark_logoi))+'x'+string(length(watermark_logoi))+'; \alpha = '+string(alpha)]); end %% FIGURE 11. NC values under different parameters suffering various attacks watermark_logo1 = watermark_logo; watermark_logo2 = imresize(watermark_logo,0.5); watermark_logo3 = imresize(watermark_logo,0.25); method = 'DWT-HD-SVD'; alpha =0.1; attacks = {'JPEG compression';'JPEG2000 compression';'Gaussian low-pass filter';... 'Median';'Gaussian noise';'Sharpening attack'}; figure for j = 1:length(attacks) attack = string(attacks(j)); params = 0; switch attack case 'JPEG compression' params = 10:10:90; y = [0.98 1]; x = [0 100]; ticks = string(params); label = 'Quality Factor (QF)'; case 'JPEG2000 compression' params = 4:4:36; y = [0.98 1]; ticks = string(params); label = 'Compression Ratio (CR)'; case 'Gaussian low-pass filter' params = 0.5:0.5:4.5; y = [0.9 1]; ticks = string(params); label = 'sigma'; case 'Median' params = 3:7; y = [0.86 1]; ticks = {'3x3','4x4','5x5','6x6','7x7'}; label = 'Window size'; case 'Gaussian noise' params = 0.001:0.002:0.009; y = [0.88 1]; ticks = string(params); label = 'Variance'; case 'Sharpening attack' params = 0.1:0.1:0.9; y = [0.96 1]; ticks = string(params); label = 'Strength (1-Threshold)'; end NC1 = 0; NC2 = 0; NC3 = 0; for i = 1:length(params) [watermarked_image1, extracted_watermark1] = watermark(cover_image,watermark_logo1,method,alpha,attack,params(i)); [watermarked_image2, extracted_watermark2] = watermark(cover_image,watermark_logo2,method,alpha,attack,params(i)); [watermarked_image3, extracted_watermark3] = watermark(cover_image,wate