INTRODUCTION
The explosive growth of Internet and communication has led to the tremendous
use of multimedia data like image, audio and video. Furthermore, due to the
availability of tools to manipulate digital multimedia especially digital images,
tampering of such data has become very easy. In this context, it is important
to ensure the integrity of images and protection against unauthorized duplication
of images. A common technique for copyright protection is to embed a watermark
in to the image or video data to be transmitted. The important requirements
of such watermarks are imperceptibility, robustness and security. Watermark
imperceptibility means that the watermark should be hidden in cover image in
such a way that it can not been seen. So, it is necessary to exploit the characteristics
of the Human Visual System (HVS) in the watermark embedding process. Robustness
of a watermark is the ability to extract watermark correctly even if the intentional
or unintentional attacks are made of the watermarked image. To ensure security,
only the authorized user should be allowed to embed and extract the watermark.
Several digital watermarking algorithms have been reported in the literature.
Based on the domain in which the watermark is embedded, image watermarking techniques
can be divided into two categories namely spatial domain techniques and frequency
domain techniques. The watermark can be secret information or a content hash
or another image such as a logo. The watermark is added either in the spatial
domain or frequency domains (Mukherjee et al., 2004;
Wong et al., 2003a; Dong
et al., 2005; Kay and Izquierdo, 2001).
The theoretical models of the HVS have been vastly applied in image processing.
A main purpose of exploiting the characteristics of the HVS is to effectively
find the image information which can be removed without degrading the subjective
image quality of the visual perception. A JND profile of an image is a key concept
of the psychovisual properties of the HVS. Although some proposed watermarking
techniques employed the JND profile to enhance their transparency and robustness,
they still suffer from some drawbacks. First, the JND a wavelet transformed
image is not used in the design of their techniques during watermark embedding
(Wong et al., 2003b; Joo
et al., 2002). Second, the original images are required for the calculation
of the JND profile of the images during watermark extraction (Zhang
et al., 2003; Wang and Lin, 2004). Podilchuk
and Zeng (1998) proposed two imageadaptive watermarking techniques. The
first technique makes use of a DCT based visual mask and they employed Watson
Just Noticeable Difference (JND) and the second method is based on a visual
model using fourlevel wavelet decomposition. The original image decomposed
into 4level using a DWT. The algorithm then selects all the coefficients with
magnitude larger than JND as the significant coefficients for all sub bands,
except the base band and inserts the watermark into the selected coefficients.
However, since this algorithm selects the significant coefficients using a fixed
JND in each sub band, its robustness is decreased. Wong
et al. (2003b) modified Watson’s model to estimate the JND profile
for Discrete Wavelet Transform (DWT) coefficients and then used the JND profile
to develop a watermarking method. Unfortunately, the method required original
images while extracting watermarks. Seo et al. (2008)
proposed robust image watermarking and the watermark is embedded in DC coefficients
by using JND as watermark strength to improve the imperceptibility of watermark
system but the method is not robust even for higher JPEG quality. Reddy
and Varadarajan (2009) proposed wavelet based watermarking scheme using
HVS model and they used entropy value as texture characteristic of HVS and the
host image is decomposed by means of Haar wavelet transform with the lifting
scheme to obtain the four subbands LL, LH, HL, HH. They employed entropy masking
of HVS model in the selection of appropriate subband. Afterwards, the subbands
except LL are considered and the one with maximum entropy is chosen for watermark
embedding. But the quality of watermarked image is not very high, perhaps since
they used a fixed HVS model and the entropy value alone is not good enough to
be considered as characteristic of HVS which entropy value might differs for
different host image. To achieve higher robustness and imperceptibility, we
proposed two adaptive images watermarking algorithms based on DCT and DWT. In
DCT based algorithm, we employed Watson’s model to estimate the JND value
and improve robustness and the quality of watermarked image by controlling the
power of JND strength. In DWT domain, the visual model is designed to generate
a Just Noticeable Difference mask (JND) by analyzing image characteristics such
as textures and luminance of the cover image.
HUMAN VISUAL SYSTEM MODELS (HVS)
HVS in dct domain: To balance the transparency and robustness, an effective
watermarking method should exploit HVS masking characteristics. The JND models
used in this work employed Watson’s JND model. The visibility threshold
t^{F}_{u,v} as a function of spatial frequency response in specific
viewing conditions is usually derived by the model presented by Peterson
et al. (1993) The human visual system’s sensitivity to variations
in luminance is dependent on the local mean luminance. Luminance sensitivity
is estimated by the formula:
where, X_{b} is the DC coefficient of the DCT for block b, is
the DC coefficient corresponding to the mean luminance of the display and a
is the parameter which controls the degree of luminance sensitivity and its
suggested value 0.649. Considering all the above parameters, the JND (a contrast
masking threshold) is derived as:
where, w is a number between zero and one and can assume different value for each DCT basic function. A typical empirical derived value is 0.7. In our experiment we lower the value of w in order to increase the imperceptibility of watermarked image.
HVS in dwt domain: In order to design an effective robust watermarking,
it is necessary to take into account the visual effect of embedding a watermark
into a host image. Human eyes have different sensitivity to different luminance,
most sensitive to middle level luminance usually, Weber ratio keeps const 0.02
within a large range of middle level and sensitivity declines nonlinearly within
the low and high luminance range (Yang and Sun, 2007).
We can use the Eq. 3 and use ω (u, v) as contrast sensitivity
factor.
where, β denotes the maximum of contrast sensitivity, ave (u, v) is the average luminance of B_{u,v,} I_{1} and I_{2} the predetermined threshold value.
Human eyes are more sensitive to the noise in image smooth areas and less sensitive to the one in image texture areas. As for texture masking, we can use the local variance of wavelet sub block band because the variance is bigger at the textures and edges than at the smooth region so we use the variance of wavelet blocks v (u, v) as texture masking:
where, B (u, v) is wavelet sub blocks b and α is mean value of wavelet sub blocks. The effect of HVS masking characteristics is incorporated into the JND threshold value based on all above considerations as follows: Let ω (u, v) and v (u, v) be λ and δ, respectively.
where, Ψ and γ are predetermined threshold values and ρ is parameter which is used to control texture masking and Γ is the JND threshold value of wavelet sub blocks, max ( ) and min ( ) denotes the maximum and minimum set value respectively.
PROPOSED IMAGE ADAPTIVE WATERMARKING EMBEDDING IN DCT AND DWT DOMAIN
Watermarking embedding in dct domain: The proposed watermarking algorithm starts by partitioning the cover image I of size (MxN) into n by n nonoverlapping blocks. The watermark image (logo or trademark) w is of size m xn. The steps involved in the proposed DCT domain embedding is presented as follows:
•  Step
1: Partition the original image into n by n nonoverlapping blocks.
Obtained the transformed blocks B by applying DCT 
• 
Step 2: Generate two uncorrelated Pseudorandom Noise
sequences (PNsequences) PN_{0 }and PN_{1 }to embed the
watermark bits 0 and 1 respectively, using keys k_{1 }and k_{2} as the seed to the pseudorandom sequences generator. 
• 
Step
3: Compute the JND threshold value by using Eq. 2 
• 
Step
4: Let the chosen DCT coefficients be I_{k} 
• 
Step
5: Embed the watermark as follows 
• 
Step
6: Apply inverse DCT to get the watermarked image WI 
WATERMARK EMBEDDING IN DWT DOMAIN
The process of embedding the watermark image into the host image is presented in this subsection. The host image I is first partitioned into 8 x 8 blocks and then each block is decomposed into four subbands (LL, LH, HL and HH) by applying DWT in each block. The watermark is embedded into host image by the altering the coefficients of the vertical and horizontal detail sub bands of image blocks. The watermark embedding technique is given as follows:
• 
Step
1: Partitioned host image I into 8 by 8 blocks and then decomposed
each block into four subbands (LL, LH, HL and HH) by using DWT 
• 
Step 2: Modulate binary watermark image. To prevent
watermark from unauthorized access and increase the security, the watermark
is first mapped into a pseudorandom data. Let digital watermark W be a
binary image of size mxn and PN be a binary pseudorandom matrix of size
mxn generated by a secret key k. The binary image W and binary pseudorandom
matrix PN are represented as: 
W = {w(i, j) w(i, j) ε{0,1}, 0 = i = m1, 0 = j = n1}, PN = {pn(i, j)
pn(i, j) ε{0, 1}, 0 = i = m1, 0 = j = n1}. We employed (7) to modulate
the watermark W and then obtain the final watermark, W _{0 }which will
be embedded:
•  Step
3: Compute JND by using Eq. 5 
• 
Step
4: Let vertical (HL) and horizontal (LH) detail subbands be X and
Y, respectively 
• 
Step 5: Choose n coefficients from vertical and horizontal
detail subbands by using random sequence S_{v} and S_{h} which are generated by two secret keys, k_{3 }and k_{4 }and
these two keys are used to select the position of coefficients to embed
and extract the watermark image 
• 
Step
6: Compute the watermark strength by using the following formula: 
where, σ is the watermark strength, φ is the average value of detail subbands and x is the detail subbands coefficients in each block
• 
Step
7: We use the following formulas to embed the watermark respectively: 
If W_{0 }= 1 and if X>Y and if XY<Γ and else if Y+X<Γ,
we adopt Eq. 9 and 10 to hide the watermark:
• 
Step
8: Obtain watermarked image WI by applying inverse DWT in each block 
PROPOSED WATERMARKING EXTRACTION IN DCT AND DWT DOMAIN
Watermarking extraction in dct domain: To extract the watermark we first partition the original image OI into blocks of size (NxN). Then each block are computed by applying the DCT transformation on the original image and the watermarked image and the difference between the two are computed to extract the watermark. The correlation coefficient between the extracted watermark and the original watermark is computed to extract the watermark. The watermark extraction algorithm presented below:
•  Step
1: Partitioned the original image OI and the watermarked WI into (NxN)
non overlapping blocks. And then apply DCT in each block 
• 
Step
2: Regenerate the two pseudorandom sequences PN_{0} and PN_{1} using the same seed or key in the embedding process 
• 
Step
3: Compute: 
• 
Step 4: For each block, the correlation between PN_{k}*
and the pseudorandom sequences PN_{0} and PN_{1} are computed.
If the correlation with PN_{0} is higher than the correlation with
PN_{1} then the extracted bit is considered to be 0, otherwise the
extracted bit is considered to be 1 
• 
Step
5: Reconstruct the watermark W* using the extracted watermark bits 
Watermarking extraction in dwt domain: In this section we presented watermark extraction and it is the inverse procedure of watermark embedding. The steps are as follows:
•  Step
1: Apply DWT to the 8 by 8 blocks of watermarked image 
• 
Step 2: Regenerate two different random sequences k_{3} and k_{4} using the same key or seed as in the embedding techniques
to select the positions in vertical and horizontal subdetails of wavelet
subblocks respectively. Let X ^{w }and Y ^{w} be vertical
and horizontal detail subbands wavelet subblocks, respectively 
• 
Step
3: Select the position of coefficients by using step 2 
• 
Step
4: Extract the watermark as follows: 
• 
Step 5: Lastly, a binary pseudorandom PN is generated
as the same secret key as the watermark modulating. We adopt Eq.
13 to get the final demodulate extracted watermark W_{m}: 
EXPERIMENTAL RESULTS
The simulated experiments are carried out to demonstrate the effect of the
proposed schemes. In experiments, we adopt grayscale images with size 512x512
as the original image and use binary images with size 64x64 as the watermark
image. Figure 1ah displays different host images. Let β,
I_{1, }I_{2 }be 0.40, 60 and 120, ρ set to be 0.5 and Ψ
and γ are set to 60 and 20, respectively.
Invisibility: To evaluate the invisibility of the watermark, we test
the two proposed algorithms on series of standard images. Table
1 shows the PSNR values of the watermarked images. And we lower the value
of w and can been that the PSNR value is higher and the quality of watermarked
image is good and can be observed from Fig. 2b. And we take
Lena image as an example; Fig. 2a and d
show the original image, watermarked image and watermark images.
From Table 1 and Fig. 2, we can find that the watermarked images have high PSNR values and the watermark is invisible to human eyes. Even any visual difference was hardly noticeable on the watermarked image with lower PSNR value, because of taking advantage of HVS characteristics
Table 1: 
PSNR
values of watermarked images 


Fig. 1: 
(ah)
Host images 

Fig. 2:  (a
and b) Original image and watermarked image and (c and d) original watermark
image, respectively

Robustness: This section examines the robustness against attacks, including
JPEG compression with different quality factors, low pass filtering (LPF), sharpening
and Histogram equalization, salt and pepper noise, Gaussian noise, resizing
and so on. Figure 3ag and 4ag show extracted
watermark image for DCT based method.
As shown in Fig. 3 and 4 that the proposed DCT method is robust for various image distortions and we can observe that the extract watermark is very good and has a good similarity with the original watermark.
The proposed DWT based algorithm is also tested for various types of image
distortions. The distorted Lena image by noise and histogram equalization and
corresponding extracted watermarks are shown in Fig. 5ad
and Fig. 6cd, respectively.

Fig. 3:  Extracted
watermark (ac) JPEG compression with quality factor 85, 90 and 95% and
(dg) Gaussian noise (0.1, 0.2, 0.3 and 0.5%), respectively 
It can be seen that both the proposed methods are robust against JPEG compressions,
salt and pepper noise and low pass filtering and other image attacks and extract
watermark is good. Figure 3ac is shown that the extract watermark
from JPEG compression with different quality factors and Fig.
3dg extract watermark from Gaussian noise (0.1, 0.2 and 0.5%) respectively
and this indicated that the proposed DCT method is robust against JPEG and Gaussian
noise. And the watermarked image is attacked by low pass filtering, histogram
equalization, sharpening and resizing and salt and pepper noise and the extracted
watermark is shown in Fig. 4. As we can seen from the results
the proposed DCT method is also robust against the above mentioned attacks.

Fig. 4:  Extracted
watermark (ac) LPF (Gamma: 0.9, 0.8 and 0.5) and (dg) Histogram equalization,
sharpening, resizing and 1% salt and pepper noise, respectively 

Fig. 5: 
(ac)
Distorted watermark image with salt and pepper noise (10, 3 and 1%) and
(d) histogram equalization, respectively 
The proposed DWT method is tested against JPEG compression, salt and pepper
noise, low pass filtering and histogram equalization and the extracted watermark
is shown in Fig. 6ad and 7ad show the
extracted watermark from Gaussian noise, sharpening and resizing. As we can
observed from Fig. 6 and 7 the extracted
watermark can be identified easily and this shows that the proposed DWT method
is robust against various image distortions.

Fig. 6: 
Extracted watermark (a and b) JPEG compression with quality
factor 85 and 90% and (ce) salt and pepper noise (10, 3 and 1%) and (f
and g) LPF and histogram equalization, respectively 

Fig. 7: 
Extracted
watermark (a and b) Gaussian noise 0.1 and 0.3% and (c and d) sharpening
and resizing, respectively 
Table 2 displays the NC value between the original watermark and the extracted watermark from the attacked watermarked images. The experimental results demonstrate that the NC value is higher. The robustness of the proposed schemes is evident from the experimental evaluation. And Table 3 shows the robustness performance and both proposed methods show better performance than the existing method.
Table 2: 
Robustness
experimental results 

Table 3: 
Robustness
performance 

CONCLUSION
Two robust image adaptive watermarking methods were proposed for copyright protection. The watermark has been performed in DCT and DWT domain. The incorporation of HVS model into the proposed schemes has resulted in an efficient watermarking scheme for effective copyright protection of images. The experimental results show the effect of proposed schemes. The proposed methods are highly robust for different image distortions and have satisfied both the requirements of effective copyright protection scheme: imperceptibility and robustness
ACKNOWLEDGMENTS
This study was supported by National Natural Science Foundation of China (60736016, 60873198, 60973128 and 60973113), Scientific Research Fund of Hunan Provincial Education Department of China (08C018) and National Basic Research Program of China (2006CB303000, 2009CB326202).