DWT Based Watermarking Algorithm
using
Haar Wavelet
This article is about the implementation of the DWT based watermarking
algorithm using haar wavelet based on the study of “DWT Based Watermarking Algorithm
using Haar Wavelet” by Anuradha ,
Rudresh Pratap Singh. In this paper, a wavelet-based watermarking
approach is proposed in which a visually recognizable watermark is added to the
wavelet coefficients of an image. This watermark can be a binary, gray-scale.
The extracted watermark is visually recognizable to claim ownership. The
embedded watermark is hard to detect by human visual perceptivity. In the
proposed method pixels of watermark are embedded in wavelet Coefficients
corresponding to the points located in a neighborhood with maximum entropy.
Embedding the watermark in such pixels makes it possible to use maximum amount
of watermark due to human eye insensitivity to areas with high entropy.
Methodology:
Watermarking
In case of two-dimensional image, after a DWT transform, the
image is divided into four corners, upper left corner of the original image,
lower left corner of the vertical details, upper right corner of the horizontal
details, lower right corner of the component of the original image detail (high
frequency). You can then continue to the low frequency components of the same
upper left corner of the 2nd, 3rd inferior wavelet transform. After wavelet decomposition process, the low
frequency component is equivalent or similar to the original image, so the band
has added to the watermark robustness. So that the low frequency components of
the human eye to adding a watermark are much higher than the sensitivity of
high frequency components by adding information. On the other hand, by the
knowledge of image compression, we know that the adding of watermark in the
high frequency image can be easier to lose with lossy compression in the
operation. On the basis of such considerations, the algorithm uses a different
color image multiplied by the weighting coefficients of different ways to solve
the visual distortion, and by embedding the watermark, wavelet coefficients of
many ways, enhance the robustness of the watermark .
Here is the code in watermarking the image:
clc
close all
%host
rgbimage=imread('host.jpg');
figure;imshow(rgbimage);title('original
color image');
[h_LL,h_LH,h_HL,h_HH]=dwt2(rgbimage,'haar');
dec2d = [...
h_LL, h_LH;
...
h_HL, h_HH
...
];
figure,imshow(uint8(dec2d));title('DWT2 of
original color image');
%watermark
rgbimage=imread('watermark.jpg');
figure;imshow(rgbimage);title('Watermark
image');
[w_LL,w_LH,w_HL,w_HH]=dwt2(rgbimage,'haar');
dec2d = [...
w_LL, w_LH;
...
w_HL, w_HH
...
];
figure,imshow(uint8(dec2d));title('DWT2 of
Watermark image');
%watermarking
newhost_LL = h_LL + (0.01*w_LL);
%output
rgb2=idwt2(newhost_LL,h_LH,h_HL,h_HH,'haar');
figure;imshow(uint8(rgb2));title('Watermarked
image');
imwrite(uint8(rgb2),'watermarked.jpg');
The method discussed above was applied in various test images. An example of embedding result is shown in
figure 1, in which a colored bell pepper image is used as test image and the
colored group of fruits as the watermark.
(a)
(b)
Figure 1. (a) Original Image (b) Watermark Image
The resulting image of the
method discussed above was shown in figure 2.
Figure 2. Watermarked Image
Watermark Extraction
The extraction algorithm process is the inverse of the
embedding process. It is assumed that the watermark as well as the see value is
available at the receiver end to the authorized users.
The operation of channel separation is applied on the
watermarked color image to generate its sub images, and then 2-level discrete
wavelet transform is applied on the sub images to generate the approximate
coefficients and detail coefficients. After that the similar operation is
applied on the original image, and generate it’s both coefficients, first approximate
coefficients and second detail coefficients. With the both coefficient approximate
coefficients and the detail coefficients of watermarked image and original
image, we could extract watermark data through watermark extracting algorithm,
respectively. For this purpose the following formulae is use- W(i) = (yw(i) + yo(i))
/ α.
After this execution the Inverse 2-level discrete wavelet
transform is applied on the watermark data to generate watermark images
extracted.
Here is the code in extracting the image:
clc
close all
%host
rgbimage=imread('watermarked.jpg');
figure;imshow(rgbimage);title('original
color image');
[h_LL,h_LH,h_HL,h_HH]=dwt2(rgbimage,'haar');
dec2d = [...
h_LL, h_LH;
...
h_HL, h_HH
...
];
figure,imshow(uint8(dec2d));title('DWT2 of
original color image');
%watermark
rgbimage=imread('watermark.jpg');
figure;imshow(rgbimage);title('Watermark
image');
[w_LL,w_LH,w_HL,w_HH]=dwt2(rgbimage,'haar');
dec2d = [...
w_LL, w_LH;
...
w_HL, w_HH
...
];
figure,imshow(uint8(dec2d));title('DWT2 of
Watermark image');
%watermarked
rgbimage=imread('watermarked.jpg');
figure;imshow(rgbimage);title('Watermarked
image');
[wm_LL,wm_LH,wm_HL,wm_HH]=dwt2(rgbimage,'haar');
dec2d = [...
wm_LL, wm_LH;
...
wm_HL, wm_HH
...
];
figure,imshow(uint8(dec2d));title('DWT2 of
Watermarked image');
%watermarking
newwatermark_LL=
(wm_LL-h_LL)/0.01;
%output
rgb2=idwt2(newwatermark_LL,w_LH,w_HL,w_HH,'haar');
figure;imshow(uint8(rgb2));title('Extracted
watermark');
imwrite(uint8(rgb2),'EWatermark.jpg');
% M=512;
% N=512;
% X=imresize(uint8('Watermark.jpg'),
[M N]);
%
Y=imresize(uint8('EWatermark.jpg'), [M N]);
% cc = corr2(X,Y);
% disp(cc);
The generated watermarked image by the embedding code was used
in order to extract the watermark image. The method discussed above was applied
in various test images. An example of extracting result was shown in figure 3.
(c)
(d)
Figure 3. (c) Watermarked Image (d) Extracted Image
Below is the complete file for the source code and article:
https://www.dropbox.com/home/Watermarking
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