Image Watermark Retrieval based on Majority Voting

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i.e. YCbCr and YIQ. Since the quality of watermark in blue components is sometimes better and worse than the one dispersed to the components in different ...
Signal Processing Image Processing Paper 101492

Image Watermark Retrieval based on Majority Voting from Different Color Spaces Thitiporn Pramoun, Narong Mettripun, and Thumrongrat Amornraksa

Multimedia Communications Laboratory, Computer Engineering Department, Faculty of Engineering, King Mongkut's University of Technology Thunburi, 126 Pracha-Utid, Toong-kru, Bangkok, Thailand, 10140 thitiporniO [email protected], n [email protected] _

Abstract- In this paper, a new watermark retrieval method is proposed to be used in the digital watermarking based on the modification of image pixels in order to improve the accuracy of the

retrieval

watermark

and

simultaneously

enhance

the

robustness of the embedded watermark. Conceptually, different color spaces usually display different discriminating energy. The watermark embedded in blue component of RGB color space can thus be retrieved from other components in different color spaces i.e.

YCbCr and YIQ. Since the quality of watermark in blue

components is sometimes better and worse than the one dispersed to the components in different color spaces, majority voting from three different color spaces is used to decide the true value of retrieved watermark bit. The experimental results on a set of standard testing images show the improved accuracy of the retrieved watermark compared to the watermarking scheme with the existing retrieval method. The enhanced robustness of the embedded watermark against various attacks is also evaluated and compared.

I.

INTRODUCTION

In digital communication, the information such as video, audio, image, and etc. can be easily distributed to the public without permission from the original owner/creator. To prevent such problem, digital watermarking has been developed and used to prove the ownership of digital information. In digital image watermarking, a secret signal called watermark is imperceptibly embedded into a host image, and can later be reliably retrieved back as a proof. Some basic requirements of digital image watermarking are the watermarked image should not be noticeable by human eye and the embedded watermark should be robust against all possible types of attacks [1]. For example, S. Rawat et al [2] proposed the image watermarking in YCbCr. They first converted the host image in RGB color space to the YCbCr one, and then transformed the result to coefficients in a transform domain using the DCT before adding the watermark bits to them. S. Qingtang et al [3] proposed another image watermarking based on Interger Wavelet transform (IWT) and YIQ color space. They transformed the host image by using the IWT, and then embedded the watermark bits into the luminance component Y in the IWT domain. The image watermarking based on pixels modification was also an interesting topic because the capacity of watermark signal that can be embedded into a host image is very large. For instance, M. Kutter et al [4] proposed a method to embed a watermark bit into an image pixel in blue component by modifying that pixel using either additive or subtractive, depending on the watermark bit, and proportional

The 8th Electrical Engineering! Electronics, Computer, Telecommunications and Information Technology (ECTI) Association of Thailand - Conference 2011

to the luminance of the embedding pixel. According to their method, the watermark retrieval was performed without the need of original pixel, i.e. by using an original pixel prediction technique from its neighborhoods. The experimental results demonstrated the robustness of their method against various types of common image processing attacks including geometrical ones. Subsequently, R. Puertpan et al [5] applied a Gaussian pixel weighing mask into the embedding process and used a linear combination of all nearby pixel values around the embedded pixels in the retrieval process to improve the watermark retrieval performance. Three different techniques were also proposed by [6] to enhance the entire performance of such watermarking scheme i.e. by balancing the watermark bits around the embedding pixels, tuning the strength of the embedding watermark in accordance with the nearby luminance, and reducing the bias in the process of predicting the original image pixel from the surrounding watermarked pixels. The authors also demonstrated how to embed a watermark image (logo) into a color image having the same size i.e. by embedding an m Xn watermark bits into an m Xn color image pixels. Other parameters in the scheme can be modified to obtain a better performance as well, for instance, by using luminance averaging [7] or by removing luminance component [8]. In this paper, we consider the image watermarking based on pixels modification, as described above, and propose a new watermark retrieval method in order to further increase the accuracy of retrieved watermark. In summary, majority voting of predicted watermark bits from three components in different color spaces is used to output the final retrieved watermark bit. We organize this paper as follows: section 2 describes the background of image watermarking based on pixels modification. Section 3 describes our proposed retrieval method. Section 4 describes the experimental setting, and demonstrates the experiments results. Section 5 concludes the contributions in this paper. II.

BACKGROUND

In the watermark embedding process, a two colors black and white image is consider as watermark w(i,j) E {O,I}. The watermark embedding is performed by modifying the blue component of a host color image, at a given coordinate (i,j), in a line scan fashion. The reason of modifying the blue component because it is the one that human eye is least

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sensitive to

[4].

The modifications of the blue component in

B(i,j) w 0),

each image pixel subtractive (if

are either additive (if

w

1)

=

L(i,j). The luminance 0.299R(i,j) + 0.587G(i,j)

display different discriminating energy, the watermark energy

Due to the fact that changes in high luminance

to other color components in different color spaces, and this

component is computed from

0.114B(i,j).

According to the fact that different color spaces usually

and proportional to the luminance

=

component in the embedding pixel, +

III. THE PROPOSED WATERMARK RETRIEVAL METHOD

or

L(i,j)

=

embedded in blue component ofRGB color space will disperse

pixels are less perceptible to the human eye, the luminance

part of watermark energy can somehow be retrieved from the

value is hence considered and used for tuning the strength of

other color components too. The following equations show

watermark, so that more energy of watermark can be added to

relationships between

[][ [] [

achieve a higher robustness. Note that the modification of luminance mask

[6].

L ri,j)

B'U,j) = B(i,j)+w(i,j)sL'U,j) where

s

(1)

is a watermark signal strength and considered as a

scaling factor applied to the whole image frame. In practice,

s

imperceptibility and robustness. Fig.

1

shows block diagram of

the watermark embedding process.

0.114 -0.322 0.312

B

0.114

Cb = -0.169

-0.331

0.500

Cr

-0.419

-0.081

0.299 0.500

Y

0.299

I

= 0.596

Q

must be carefully selected to obtain the best trade-off between

0.587 -0.275

][] ][]

0.587

Y

is obtained from a Gaussian pixel weighting

The watermarked pixel B ri,j) is expressed by

RGB, YCbCr and YIQ color spaces.

0.211 -0.523

From both

(4)

and

(5),

R

G

x

(4)

B

R

x

G

(5)

we can clearly see that the changes

in the blue component directly affect the color components in the other two color spaces. In the same way, the watermark

Pseudo- random bit-stream

embedded in the blue component can be retrieved back from s

L'(i,j)

the color components in different color spaces. For example, in

B(i,j)

YCbCr

the

embedded

w(i,j) --I�f-+-Il---.!

B'(t,j)

color space, we may retrieve a watermark bit in

considered and used. First, a pixel value at a given coordinate can be predicted by the average of surrounding neighbors

because any pixel value within an image is closed to its

(i,j)

w

component

from

the

Cb

color

(i,j)

w'Cb (i,j) = Cb'(i,j)- Cb" (i,j)

To retrieve the embedded watermark, two assumptions are

surrounding neighbors. Second, the summation of

blue

is

predicted by the following.

Figure I. Block diagram of the watermark embedding process

(i,j)

the

components. That is, the retrieved watermark bit at

around

is closed to zero, so that the retrieved watermark bit at

(i,j)

can be predicted by the following equation.

where

Cb

the

w cb(i,j)

represents the watermark bit predicted from

(2)

Cb nj)

color components,

represents the blue color

component converted from the watermarked image in RGB, and

Cb ri,j) '

represents the original blue color component

predicted from its surrounding neighbors as given in I

w'(i,j) = B'(i,j)-B"(i,j)

(6)

(7).

I

1 Cb"(i,j)= ( I ICb'(i+m,j+n)-Cb'(i,j)) g

(7)

m=-In=-I

From

(2), B li,j)

can be easily estimated from

the predicted watermark bit

w'(i,j)

consist of

2

(3). Note that values, i.e. if

w'(i,j) is positive (or negative) then the predicted value of w (i,j) is 1 (or 0). Fig. 2 shows block diagram of the watermark retrieval process. 1

The similar concept of watermark retrieval in the

w'Q (i,j) = Q'(i,j)-Q"(i,j) I

1

1 B"(i,j)= ( I IB'(i+m,j+n)-B'(i,j)) g

(3)

m=-I n=-I

Q

color

components can also be applied using the following equations.

(8)

1

1 Q"(i,j)= ( I IQ'U+m,j+n)-Q'U,j)) g

(9)

m=-ln=-l

From our observations, we found that some errors always occur during the step of watermark bit prediction in the

B'(i,j) --+

Prediction of original pixel

watermark retrieval process. This kind of errors come from the

B"(i,j) -

w '(i,j)

non-linear characteristic of image e.g. sudden changes of pixels value within the prediction area, and causes a wrong predicted

Figure 2. Block diagram of the watermark retrieval process

The 8th Electrical Engineering! Electronics, Computer, Telecommunications and Information Technology (ECTI) Association of Thailand - Conference 2011

value of the retrieved watermark bit, even if there is no attack

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Signal Processing Image Processing

applied. Therefore, instead of relying on the watermark retrieval from the blue component alone, the watermark retrievals from the other two components in different color spaces are considered and used to decide the true value of retrieved watermark bit. Moreover, in a situation where an unknown attack is applied to the watermarked image, the watermark embedded in one particular component may be partially destroyed and consequently not be able to realizably recovered. To reduce the risk of watermark unrealizable retrieval from all possible attacks, the watermark retrievals from three color components in different color spaces are carefully employed to generate the most accurate retrieved watermark bit. To achieve this, we simply apply a voting technique to the bits value predicted from three different color components, and use the result of majority voting to decide the true value of retrieved watermark bit. For example, if w'(i,J) 1, W cb(i,J) 0 and W Q(i,J) 1, the fmal value of retrieved watermark bit will be 1. Fig. 3 illustrates block diagram of the proposed watermark retrieval process. =

=

TABLE

VOlmg

Final

IV '(I,})

T Average NC value from different color components

Average Ne value from y Cb

PSNR

different color components Cr

J

35

0.715

0.876

0.510

0.384

Q 0.863

38

0.699

0.850

0.540

0.431

0.836

41

0.686

0.822

0.564

0.472

0.809

44

0.675

0.795

0.583

0.509

0.782

47

0.666

0.768

0.597

0.540

0.756

=

B'(i,})

Figure

First of all, we determined the best color component from the two color spaces, namely, YCbCr and YIQ. Then, instead of predicting the watermark bit from the blue component in the RGB color space alone [6], we predicted the watermark bit from three color components in different color spaces, and used the majority voting to decide the true value of watermark bit. The results in term of NC averaged from all testing images at various PSNR are given in table l. Obviously, the color components Cb and Q obtained the highest NC value, at all ranges of PSNR values, among all components within the same color spaces. Therefore, from now on, the predicted watermark bits obtained from B, Cb and Q color components will be used to determine the true retrieved watermark bit.

Next, we compared the accuracy of the retrieved watermark obtained from the ordinary retrieval method in [6] to our proposed method implementing the voting result from B, Cb and Q color components. Again, the results in term of average NC at various PSNR values between the two retrieval methods are presented and compared in Fig. 4. Clearly, the performance of our proposed retrieval method outperforms the previous one. Note that when increased the PSNR, the NC value is decreased.

3. Block diagram of the proposed watermark retrieval process 0.875

IV.

EXPERIMENTAL SETTING

A.

Color space selection In all experiments, six 256x256 pixels color images having various characteristics, 'Bird', 'Fish', 'Airplane', 'Baboon', 'Pepper', and 'Lena' were used as original testing images, while a 256x256 pixels two colors black and white image containing a logo '2010 CPE' was used as a watermark. In the watermarking process, the watermarked image quality was evaluated by PSNR (Peak Signal to Noise Ratio), while the retrieved watermark quality was evaluated by NC (Normalized Correlation), as given by M

N

II w(i,j)w'(i,j) i=1 j=1

NC=-r========r========= M

N

2

M

N

II w(i,j) II w'(i,j) i=1 j=1



___

�;::--

______

AND RESULTS

(10)

2

i=1 j=1

where M and N are the numbers of row and column in the images, respectively.

The 8th Electrical Engineering! Electronics, Computer, Telecommunications and Information Technology (ECTI) Association of Thailand - Conference 2011

-e-- Previous method [6]

I

� Proposed method

0.85 u Z

�O

O.825

�----"8::- �-----l

0.8

�----- =--�-- �;::-----l

0.775

�-----="""'�- -=- -l

..;:

0.75 +------.---�--�---l 35

Figure

38

41

PSNR(dB)

44

47

4. Performance comparison at various PSNRs

Finally, efficiency of the proposed watermark retrieval method against various types of attacks was evaluated and compared. The attacks we used in this research were image blurring at various blurring pixels ranging from 2 to 20 pixels, sharpness enhancement at various factors ranging from 0.1 to 0.9, addition of salt & pepper noise at various densities ranging from 0,01 to 0.06, brightness enhancement at various percentages ranging from 10 to 100%, and fmally contrast enhancement at various scaling factors ranging from 1 to 2.

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Note that in this experiment, the quality of the watermarked images was controlled to reach the PSRN of

40±O.Ol dB.

PSNR values, judged from a higher NC value, compared to the

The

existing retrieval method. The robustness of the embedded

results in term of average NC values are plotted and compared

watermark against various types of attack was significantly

in Fig. 5-9.

improved as well.

0.85

0.79

-e-- Previous method [6]

0.764 u

0.83

---A- Proposed method

I

-e-prcvious method

I--'=--==-;�=�==-::;:- ----II---A- Proposed

[6]

In cthod

-

uO.81 Z !h

�o

0.738



0.712 -j-----�....=_-.. -='_A,___ -------_l

0.77

0.686 �----------- -=::,,-