使用MATLAB编写代码基于LSB数字水印方法完成图像的篡改检测和恢复的完整代码。1对于给定图像要求BMP格式512512大小将其划分成88的小块 嵌入水印信息篡改定位和恢复水印。2对嵌入水印信息的载体图像进行五种不同程度大小的篡改注意为修改图像内容但不要改变图像格式3对篡改之后的图像划分成88的小块每块提取嵌入的水印信息。1 如果某些图像块提取的水印与其他图像块的不同则判定为该图像块被

以下是基于LSB数字水印方法完成图像的篡改检测和恢复的完整MATLAB代码：

%% 嵌入水印信息 clear; clc; close all;

% 读取载体图像 img = imread('lena.bmp'); figure(1); imshow(img); title('原始图像');

% 将图像划分成8*8的小块 block_size = 8; [M, N] = size(img); num_blocks_row = floor(M/block_size); num_blocks_col = floor(N/block_size); num_blocks = num_blocks_row * num_blocks_col; img_blocks = zeros(block_size, block_size, num_blocks); for i = 1:num_blocks_row for j = 1:num_blocks_col img_blocks(:,:,i+(j-1)*num_blocks_row) = img((i-1)block_size+1:iblock_size, (j-1)block_size+1:jblock_size); end end

% 嵌入水印信息 watermark = randi([0, 1], num_blocks, 1); watermark_idx = 1; for i = 1:num_blocks block = img_blocks(:,:,i); lsb = mod(block, 2); if watermark(watermark_idx) ~= lsb(1) block(1) = block(1) + (watermark(watermark_idx)-lsb(1)); end img_blocks(:,:,i) = block; watermark_idx = watermark_idx + 1; end

% 合并小块为图像 watermarked_img = zeros(M, N); for i = 1:num_blocks_row for j = 1:num_blocks_col watermarked_img((i-1)block_size+1:iblock_size, (j-1)block_size+1:jblock_size) = img_blocks(:,:,i+(j-1)*num_blocks_row); end end

figure(2); imshow(watermarked_img); title('嵌入水印后的图像');

% 保存嵌入水印后的图像 imwrite(watermarked_img, 'watermarked_lena.bmp');

%% 篡改图像并提取水印信息 clear; clc; close all;

% 读取嵌入水印后的图像 img = imread('watermarked_lena.bmp'); figure(1); imshow(img); title('嵌入水印后的图像');

% 计算篡改后的图像 modified_imgs = cell(5,1); modified_imgs{1} = imnoise(img, 'gaussian', 0, 0.01); modified_imgs{2} = imnoise(img, 'gaussian', 0, 0.05); modified_imgs{3} = imnoise(img, 'salt & pepper', 0.01); modified_imgs{4} = imnoise(img, 'salt & pepper', 0.05); modified_imgs{5} = imresize(img, 0.5);

% 提取水印信息并检测篡改区域 block_size = 8; [M, N] = size(img); num_blocks_row = floor(M/block_size); num_blocks_col = floor(N/block_size); num_blocks = num_blocks_row * num_blocks_col; watermark = zeros(num_blocks, 1); for k = 1:length(modified_imgs) % 提取水印信息 for i = 1:num_blocks_row for j = 1:num_blocks_col block = modified_imgs{k}((i-1)block_size+1:iblock_size, (j-1)block_size+1:jblock_size); lsb = mod(block, 2); watermark((i-1)*num_blocks_col+j) = lsb(1); end end

% 检测篡改区域并彩色标注
diff_blocks = find(watermark ~= watermark(1));
diff_blocks_row = floor((diff_blocks-1)/num_blocks_col)+1;
diff_blocks_col = mod(diff_blocks-1, num_blocks_col)+1;
diff_blocks_img = repmat(modified_imgs{k}, [1,1,3]);
for i = 1:length(diff_blocks)
    diff_blocks_img((diff_blocks_row(i)-1)*block_size+1:diff_blocks_row(i)*block_size, (diff_blocks_col(i)-1)*block_size+1:diff_blocks_col(i)*block_size, :) = repmat([255, 0, 0], [block_size, block_size, 1]);
end

% 显示篡改图像和恢复图像
figure(2*k); imshow(diff_blocks_img); title(['篡改后的图像 ', num2str(k)]);
figure(2*k+1); imshow(modified_imgs{k}); title(['恢复后的图像 ', num2str(k)]);

end

%% 恢复篡改区域 clear; clc; close all;

% 读取嵌入水印后的图像 img = imread('watermarked_lena.bmp'); figure(1); imshow(img); title('嵌入水印后的图像');

% 计算篡改后的图像 modified_imgs = cell(5,1); modified_imgs{1} = imnoise(img, 'gaussian', 0, 0.01); modified_imgs{2} = imnoise(img, 'gaussian', 0, 0.05); modified_imgs{3} = imnoise(img, 'salt & pepper', 0.01); modified_imgs{4} = imnoise(img, 'salt & pepper', 0.05); modified_imgs{5} = imresize(img, 0.5);

% 提取水印信息并检测篡改区域 block_size = 8; [M, N] = size(img); num_blocks_row = floor(M/block_size); num_blocks_col = floor(N/block_size); num_blocks = num_blocks_row * num_blocks_col; watermark = zeros(num_blocks, 1); for k = 1:length(modified_imgs) % 提取水印信息 for i = 1:num_blocks_row for j = 1:num_blocks_col block = modified_imgs{k}((i-1)block_size+1:iblock_size, (j-1)block_size+1:jblock_size); lsb = mod(block, 2); watermark((i-1)*num_blocks_col+j) = lsb(1); end end

% 检测篡改区域并恢复内容
diff_blocks = find(watermark ~= watermark(1));
if ~isempty(diff_blocks)
    % 定位篡改区域
    diff_blocks_row = floor((diff_blocks-1)/num_blocks_col)+1;
    diff_blocks_col = mod(diff_blocks-1, num_blocks_col)+1;
    
    % 提取恢复水印信息
    recovered_watermark = zeros(num_blocks, 1);
    for i = 1:num_blocks_row
        for j = 1:num_blocks_col
            block = img((i-1)*block_size+1:i*block_size, (j-1)*block_size+1:j*block_size);
            lsb = mod(block, 2);
            recovered_watermark((i-1)*num_blocks_col+j) = lsb(1);
        end
    end
    
    % 利用恢复水印信息恢复篡改区域
    recovered_blocks = zeros(block_size, block_size, length(diff_blocks));
    for i = 1:length(diff_blocks)
        block_row = diff_blocks_row(i);
        block_col = diff_blocks_col(i);
        block_idx = diff_blocks(i);
        recovered_block = zeros(block_size);
        for k = 1:8
            for l = 1:8
                if k == 1 && l == 1
                    continue;
                end
                block = modified_imgs{k}((block_row-1)*block_size+1:block_row*block_size, (block_col-1)*block_size+1:block_col*block_size);
                recovered_block(k,l) = block(k,l) - mod(block(k,l), 2) + recovered_watermark(block_idx);
            end
        end
        recovered_blocks(:,:,i) = recovered_block;
    end
    
    % 合并恢复后的图像块为图像
    recovered_img = img;
    for i = 1:length(diff_blocks)
        block_row = diff_blocks_row(i);
        block_col = diff_blocks_col(i);
        recovered_img((block_row-1)*block_size+1:block_row*block_size, (block_col-1)*block_size+1:block_col*block_size) = recovered_blocks(:,:,i);
    end
    
    % 显示篡改图像和恢复图像
    diff_blocks_img = repmat(modified_imgs{k}, [1,1,3]);
    for i = 1:length(diff_blocks)
        diff_blocks_img((diff_blocks_row(i)-1)*block_size+1:diff_blocks_row(i)*block_size, (diff_blocks_col(i)-1)*block_size+1:diff_blocks_col(i)*block_size, :) = repmat([255, 0, 0], [block_size, block_size, 1]);
    end
    figure(2*k); imshow(diff_blocks_img); title(['篡改后的图像 ', num2str(k)]);
    figure(2*k+1); imshow(recovered_img); title(['恢复后的图像 ', num2str(k)]);
end

en