 Research Article
 Open Access
A New Robust Watermarking Scheme to Increase Image Security
 Hossein Rahmani^{1},
 Reza Mortezaei^{1} and
 Mohsen Ebrahimi Moghaddam^{1}Email author
https://doi.org/10.1155/2010/428183
© Hossein Rahmani et al. 2010
 Received: 12 December 2009
 Accepted: 16 October 2010
 Published: 20 October 2010
Abstract
In digital image watermarking, an image is embedded into a picture for a variety of purposes such as captioning and copyright protection. In this paper, a robust private watermarking scheme for embedding a grayscale watermark is proposed. In the proposed method, the watermark and original image are processed by applying blockwise DCT. Also, a Dynamic Fuzzy Inference System (DFIS) is used to identify the best place for watermark insertion by approximating the relationship established between the properties of HVS model. In the insertion phase, the DC coefficients of the original image are modified according to DC value of watermark and output of Fuzzy System. In the experiment phase, the CheckMark (StirMark MATLAB) software was used to verify the method robustness by applying several conventional attacks on the watermarked image. The results showed that the proposed scheme provided high image quality while it was robust against various attacks, such as Compression, Filtering, additive Noise, Cropping, Scaling, Changing aspect ratio, Copy attack, and Composite attack in comparison with related methods.
Keywords
 Watermark Image
 Watermark Scheme
 JPEG Compression
 Watermark Algorithm
 Nonoverlapping Block
1. Introduction
Owing to the recent advances in network and multimedia techniques, digital images may be transmitted over the nonsecure channels such as the Internet. Therefore, the enforcement of multimedia copyright protection has become an important issue in literature.
 (1)
Readability. A watermark should convey as much information as possible, statistically detectable, enough to identify ownership and copyright unambiguously.
 (2)
Security. Only authorized users gain access to the watermark data.
 (3)
Imperceptibility. The embedding process should not introduce any perceptible artifacts into original image and not degrade the perceived quality of image.
 (4)
Robustness. The watermark should be able to withstand various attacks while can be detected in the extraction process.
The most important watermarking schemes are invisible where are secure and robust. Moreover, in the invisible watermarking, the embedding locations are secret, and only the authorized persons who have the secret keys can extract the watermark.
On the other hand, the watermarking algorithms are classified also as: the methods which require the original information and secret keys for extracting watermark are called private watermark algorithms. The methods which require the watermark information and secret keys are called semiprivate or semiblind algorithms, and ones which need secret keys rather than the original information are called blind watermark algorithms.
In another classification, digital watermarking algorithms can be divided into two groups: spatial domain [5–7] and frequency domain [8–12] methods according to the processing domain of the host image. The spatial domain algorithms are simple and the watermark can be damaged easily, but the frequency domain algorithms can resist versus intensity attack and watermark information cannot be damaged easily [13].
However, in all frequency domain watermarking schemes, there is a conflict between robustness and transparency. If the watermark is embedded in the lowerfrequency bands, the scheme would be robust to attacks but the watermark may be difficult to hide. On the other hand, if the watermark is embedded in the higherfrequency bands, it would be easier to hide the watermark but the scheme has less resistant to attacks. Therefore, finding a proper place to embed the watermark is very important.
In 1996, Cox et al. [14] advised that the watermark should be embedded in the lowfrequency coefficients of DCT domain to ensure the robustness. To improve this method, Lu et al. [15] used a cocktail watermark to increase robustness and HVS to maintain high fidelity of the watermarked image. Barni and Hsu [16, 17], respectively, recommended that the watermark should be embedded in the middle frequency coefficients to reduce the distortion. But Huang et al. in [18] points out that the DC coefficient is more proper to be used for embedding watermark, and this conclusion is obtained based on his robustness test between the DC coefficient and two lowfrequency coefficients.
Also, DWT as another frequency transform technique has been used by many researchers such as Xie and Arce for digital image watermarking [19]. The proposed method by Zhao et al. in [20] is a sample of DCT/DWT domainbased method which uses a dual watermarking scheme exploiting the orthogonality of image subspaces to provide robust authentication. As other examples, in [21, 22], the proposed DCT/DWT methods embed a binary visual watermark by modulating the middlefrequency components. These two methods are robust to common image attacks; but geometric attacks are still challenges. In [23], another approach to combine DWT and DCT has been proposed to improve the performance of the DWTbased watermarking algorithms. In this method, watermarking is done by embedding the watermark in the first and second level of DWT subbands of the host image, followed by the application of DCT on the selected DWT subbands. The combination of these two transforms improved the watermarking performance considerably in comparison with DWTonly watermarking approach.
Most of the existing watermarking methods use a pseudorandom sequence or binary image as a watermark. However, using grayscale images as watermarks has drawn much attention for copyright protection since many logos are grayscale in nature. One of the methods that hide a grayscale watermark image in original image was proposed by Mahanty and Bhargava [24]. In this method, at first, based on Human Visual System (HVS), the most perceptually important region of original image is found. Then, a compound watermark is created to insert in this region of the original image. For creation of compound watermark, the synthetic image is created by Gaussian and Laplacian random number generator. The choice of these two distributions for modeling the DC and AC coefficients of image DCT is motivated by empirical results presented in Reininger and Gibson [25] and Mohanty et al. [26]. Next, the original watermark is embedded in insensitive area of synthetic image using any DCTbased visible watermarking algorithm. Asatryan proposed another method that combines spatial and frequency domain to hide a grayscale watermark in grayscale original image by mapping the values of DCT coefficients of compressed watermark image to the interval [0,255] (max and min value of grayscale image) by a fixed linear transform and inserts these values in the original image [27]. But, this method introduces perceptible artifacts into original image and degrades the perceived quality of image.
In this paper, we have proposed a new robust watermarking method in frequency domain to insert a gray level watermark in an image. The proposed method is more robust and makes image with higher quality than related ones. The basic idea of the proposed method is based on this fact that most of the signal energy of the DCT block is compacted in the DC component and the remaining energy is distributed reductively in the AC components in zigzag scan order [4]. Also, for most images, the main characteristics of the DCT coefficients in one block have high correlation with the adjacent blocks. Gonzales et al. [3] described a technique which estimates the first five AC coefficients precisely. In this method, DC values of a neighborhood of blocks are used to estimate the AC coefficients for the center block. They did not consider variations in the image in AC coefficients estimation, but Veeraswamy and Kumar in [4] proposed a new method that considered the variation in the image and accordingly AC coefficients have been estimated with different equations. This method is better than Gonzales method in terms of reduced blocking artifacts and improved PSNR value. Based on these ideas, here, at first, a grayscale watermark image is created by applying DCT on each nonoverlapping block of original grayscale watermark image and setting all AC coefficients of each one to zero. Then, the original image is divided into nonoverlapping blocks and DCT is applied on each block. Next, a Dynamic Fuzzy Inference System (DFIS) is used to select the number of original image blocks for embedding watermark. Finally, DC value of each DCT block of watermark image is embedded in DC value of DCT block of original image by using the output of the DFIS. In the extraction process, DCT is applied on the test image to extract the DC coefficients of each DCT block of watermark and the AC coefficients of each DCT block of extracted watermark are estimated based on proposed technique by Veeraswamy and Kumar [4] to construct the watermark with higher quality. The proposed method was tested on several bench mark images using StirMarkMATLAB software and its results were satisfactory. The results showed that the proposed method created the highquality watermarked images while they were more robust against attacks such as JPEG compression, additive noise, filtering, cropping.
The rest of paper has been organized as follows: In Section 2, the proposed approach has been introduced and in Section 3, the proposed method has been motivated and structurally compared with related ones. Section 4 describes the experimental results and in Section 5, the paper has been concluded.
2. Proposed Algorithm
In this section, the proposed algorithm is describedin detail. The algorithm is divided into four parts: block selection, watermark creation,watermark embedding and watermark extraction, which are described in Sections 2.1–2.4, respectively.
2.1. Block Selection
 (i)Luminance Sensitivity( ). The brighter the background, the lower the visibility of the embedded watermark. It is estimated by following relation:
where is the DC coefficient of th block and is the mean value of DC coefficients of an original image.
 (ii)Texture Sensitivity( ). The stronger the texture, the lower visibility of embedded watermark. It can be estimated by quantizing the DCT coefficients of a block using the JPEG quantization table . The latter results are then rounded to the nearest integers. The number of nonzero coefficients is then computed. This number presents the texture of that block:
where are coefficients and counts nonzero coefficients in th block.
 (iii)
where is the number of pixels of the th block lying in the center quarter (25%) of the image.
When these parameters are computed, they can be used to select blocks and determine weighting factor for embedding. In the proposed method, a Fuzzy Inference System (FIS) for calculating the relationship established between all properties of the HVS model is used because FIS provides simple mapping from a given set of inputs to another set of outputs without the complexity of mathematical modeling concepts.
Figure 3(b) shows a plot of this function. In (8), and are two constant values that should be specified heuristically; for example, the best values that we found for not center curve were and . The same curves for all images have been used.
2.2. Watermark Creation
The formulas to estimate the first five AC coefficients of each DCT block (1).
Estimation formulas (Gonzales's method)  Estimation formulas for smoother blocks (Veeraswamy's method)  Estimation formulas for featured blocks (Veeraswamy's method) 
















The formulas to estimate the first five AC coefficients of each DCT block (2).
Featured blocks surrounded by horizontal featured blocks (Veeraswamy's method)  Featured blocks surrounded by vertical featured blocks (Veeraswamy's method)  Featured blocks surrounded by horizontal and vertical featured blocks (Veeraswamy's method) 
















Based on this idea, only DC coefficients are needed to estimate the AC coefficients of each block [4]. Therefore, the estimating formulas (as shown in Tables 1 and 2 for DCT block) are employed to find these coefficients. Figure 5(d) shows a sample image that created by this method when the size of block is .
In the proposed method, this created watermark image is inserted in the original image. In the extraction process, the estimating formulas (as shown in Tables 1 and 2 for DCT block) are employed to reconstruct the watermark.
2.3. Watermark Insertion
To describe the proposed method, we supposed that the original image ( ) and created watermark image ( ) are grayscale images with size and , respectively.
Algorithm 1 (The watermark embedding).
We have the following.
Input:
An original image , watermark ( ).
Output:
A watermarked image .
Step 1.
Divide the original image , into nonoverlapping blocks and apply DCT on each block. Next, compute the HVS model properties as said in Section 2.1 and compute and values of each block with Fuzzy Inference System as described in Section 2.1. Finally, sort blocks in descending order of value of each block.
Step 2.
Create used watermark ( ) from original watermark ( ) as described in Section 2.2.
Step 3.
Select first ( ) blocks of sorted blocks which is computed in Step 1 for embedding process ( is size of created watermark).
Step 4.
where and are DC coefficients in th block of watermarked image and original image, respectively and is DC coefficient of th block in created watermark. The parameter is a weighting factor that controls the tradeoff between invisibility, robustness, and detection fidelity of watermarked image which is computed by DFIS as described in Section 2.1. The parameter is a pseudorandom (1, −1) bit pattern that determines the addition or subtraction involved at each position which can be any arbitrarily chosen pseudorandom sequence. This parameter is just used for security purpose.
Step 5.
Use inverse DCT on each block to obtain watermarked image .
2.4. Watermark Extraction
Finally, the values and estimation formulas as described in Section 2.2 are used to create the DCT blocks of watermark then by performing Blockwise inverse DCT, watermark in spatial domain is created. The following steps are used for watermark Extraction.
Algorithm 2 (The watermark extraction).
We have the following.
Input:
An original image ( ) and watermarked image ( ).
Output:
An extracted watermark ( ).
Step 1.
Divide the original image into nonoverlapping blocks and compute the DCT on each block. Then compute the HVS model properties as said in Section 2.1 and compute and values of each block with fuzzy approach (DFIS). Finally, sort blocks in descending order of value of each block.
Step 2.
Select first ( ) blocks of sorted blocks which is computed in Step 1 for extracting process. is number of blocks in watermark and is size of watermark.
Step 3.
Divide the watermarked image into nonoverlapping blocks and compute the DCT on each block.
Step 4.
Extract the watermark from selected blocks use (10).
Step 5.
Estimate AC coefficients of each block in extracted watermark from Step 4, then use Blockwise inverse DCT to create extracted watermark in spatial domain ( ). If the input watermark image is present in the extracted image, then the ownership is approved.
3. Structural Comparison of Proposed Method with Related Ones
The employed techniques in proposed method make it more robust and its results with more quality. In this section, the differences and excellences of proposed method with two related methods [24, 27] are introduced in four conventional different steps of watermarking methods: (1) selecting embedding area procedure, (2) watermark creation procedure, (3) inserting procedure, (4) extracting procedure. Also, the motivation of proposed method is implied in subsections.
3.1. Selecting Embedding Area Procedure
Mohanty's method [24], at first, finds the most perceptually important subimage of original image, where the size of subimage is equal to size of watermark ( ) to embed the watermark in it. To find this subimage, the properties of Human Visual System (HVS) such as Luminance, Edginess, Contrast, Location and Texture are calculated for each subimage of original image and the high score one is selected as most perceptually important region of original image and watermark is embedded in it. As result, this method is not robust to geometrical attacks such as Tampering, Data block removal and Cropping; because the watermark is embedded in consecutive blocks (subimage) of original image. For example if this region of watermarked image is cropped or tampered, the whole watermark is removed and the extraction procedure cannot find any watermark in the test image (see Section 4.3). But, in the proposed method, blocks of the watermark are not embedded in consecutive blocks of original image and embedded in nonconsecutive blocks of original image. As result, the proposed method is more robust versus many geometrical attacks such as Tampering, Data block removal, and Cropping (see Sections 4.2 and 4.3).
In Asatryan's method [27] that inserts the watermark in spatial domain, all pixels of original image are used to embed the watermark. Therefore, the quality of watermarked image in this method is degraded and artifact is produced in watermarked image (see Section 4.3).
3.2. Watermark Creation Procedure
Mohanty's method create synthetic image by using DCT coefficients of selected subimage of original image and Gaussian, Laplacian distributions for DC, AC coefficients, respectively. Then, the original watermark is embedded in the created synthetic image using any DCTbased visible watermarking algorithm to create used watermark.
In the Asatryan's method, the used watermark is created by compressing the original watermark that the rate of compression is defined by user.
In the proposed method the used watermark is created by dividing the original watermark into DCT blocks and changing the AC coefficients of each block to zero. The parameter provides a tradeoff between quality of watermarked image and extracted watermark. The proposed creation watermark procedure is acceptable because the watermark image that creates by only DC coefficients of each (where ; e.g., ) DCT block of original watermark is perceptually similar as original one. Also, the AC coefficients estimating formulas that propose in [4] can be used to increase the quality of created watermark.
3.3. Inserting Procedure
In the Mohanty's method the used watermark is embedded into the original image by fusing the DCT coefficients of used watermark blocks with the corresponding blocks of the selected subimage. In the other hand, the DCT coefficients of each DCT block of used watermark is embedded in corresponding DCT block of selected subimage. As result, the robustness of mohanty's method decreases because the AC coefficients of DCT block is not robust to many attacks such as Low Pass Filtering, Compression, Median Filtering. Therefore, the many of embedded AC coefficients of used watermark are degraded after such attacks. To solve this drawback, in the proposed method, the coefficients of (where ) DCT blocks of used watermark are embedded only in DC coefficients of each DCT block of original image. As result, the robustness of proposed method is higher than mohanty's method,because the DC coefficients of DCT block is robust than AC coefficients of one.
The Asatryan's method works in spatial domain to embed the watermark in original image. In this method, the values of block DCT coefficients of compressed watermark are mapped to the interval [0,255] by fixed linear transform and the mapped values of DCT coefficients are embedded in pixel values of each block of original image. As result, because the embedding is done in special domain, the robustness of this method is decreased and the quality of watermarked image is low (see Section 4.3). Also, mapping the DCT coefficients to the interval [0,255] may be caused distortion in the extracted watermark.
The weighted factor (β) is used in all three methods. The value of this parameter is 0.02 for DC and 0.1 for AC coefficients in Mohanty's method and 0.07 for all pixels in Asatryan's method. But, in the proposed method, the value of this parameter for each DCT block is based on Texture and Luminance of this block. It is based on idea that modification inside a highlytextured block is unnoticeable to the human eye and the brighter the background is the lower the visibility of the embedded watermark. Therefore, the proposed method produces a watermarked image with higher quality than two related methods.
3.4. Extracting Procedure
The Mohanty's method use a reverse embedding procedure to extract the DCT coefficients of each DCT block of watermark and applied IDCT to create watermark in spatial domain. But in proposed method a reverse embedding procedure is performed to extract the only DC coefficients of each DCT block of watermark. Then the estimation formulas are used to evaluate the AC coefficients of each DCT block (e.g., first five AC coefficients when ) of watermark and applied IDCT to create watermark in spatial domain.
Watermarking methods  

Block size of original image  Mohanty's method [24]  Asatryan's method [27]  Proposed method 


 
Selection embedding area procedure  (1) Find a most perceptually significant set of blocks constituting a subimage (equal to size of watermark) with respect to human perception such as Texture, Location, Contrast, Luminance, and Edginess in original image.  (1) All blocks of original image are used to embed a watermark  (1) Calculate the Texture, Luminance, and Location of each DCT block in original image. (2) Use proposed Fuzzy Interface System to calculate the suitabilityfactor of each block. (3) Select ( ) blocks with higher suitability factor to embed a watermark. 
Block size of watermark 



Watermark creation procedure  (2) Create synthetic image by using DCT coefficients of most perceptually important subimage of original image and Gaussian/Laplacian distribution for DC, AC coefficients, respectively. (3) Embed the watermark in the synthetic image using any DCTbased visible watermarking algorithm.  (2) Compression was performed on watermark image until the number of chosen DCT coefficients of each 32×32 DCT block was significantly smaller than the number of pixels of the original watermark. (3) The values of DCT coefficients are mapped to the interval [0,255] by fixed linear transform.  (4) Change the AC coefficients of each DCT block to zero and apply IDCT to create used watermark. 
Inserting procedure  (4) The used watermark is now invisibly embedded into the original image by fusing the compound watermark blocks with the corresponding blocks of the selected perceptually important subimage of the original.  (4) Embed each mapped DCT coefficient of watermark in each pixel of block of original image.  (5) Embed DC coefficient of each DCT block of used watermark in DC coefficient of selected DCT block of original image. 
Value of β coefficient (weighing factor) 
 for all pixels  is different for each DCT block of original image and is computed by proposed Fuzzy Interface System based on Texture and Luminance of selected block. 
Extracting procedure  (1) Select subimage of original image where the watermark was embedded in it. (2) Use the reverse embedding procedure to extract the DCT coefficients of watermark. (3) Apply IDCT on each extracted DCT block to create watermark in spatial domain.  (1) Use the reverse embedding procedure to extract the mapped DCT coefficients of watermark. (2) The reverse of linear transform where used in embedding process is utilized to create the DCT coefficients of watermark. (3) Apply IDCT on each extracted DCT block to create watermark in spatial domain.  (1) Select blocks of original image where the watermark was embedded in them. (2) Use the reverse embedding procedure to extract the DC coefficients of each DCT block of used watermark. (3) The extracted DC coefficients are used to estimate the AC coefficients of each DCT block of watermark. (4) Apply IDCT on each estimated DCT block to create watermark in spatial domain. 
4. Experimental Results
where and are extracted watermark and inserted watermark images, respectively, and and are their pixels mean values, respectively. The subscripts , of or denote the index of an individual pixel of the corresponding image. The summations are over all the image pixels.
The other part of experiments involved testing the algorithm against many common attacks on watermarked image and fortunately the extracted watermark in almost cases was detectable and acceptable due to the original and inserted watermark. In these experiments, we used StirMarkMATLAB software that contains approximately 90 different types of image manipulations. But, in the following subsections, we will present only the experimental results for test images, and nongeometric and geometric attacks such as Compression, Noise addition, Filtering, Cropping, Changing Aspect Ratio, Tampering and Scaling on the watermarked images to evaluate the robustness of the proposed scheme.
4.1. Quality of Watermarked Image and Extracted Watermark before Attack
It is obvious that the PSNR value of the watermarked image had a higher value in comparison with other existing watermarking algorithms. The average PSNR value for the watermarked images was approximately 52 dB, where the size of watermark images is . Also, the average PSNR value for the watermarked images was approximately 49 dB, where the size of watermark images is . So, the watermark embedding process produced highquality watermarked images.
4.2. Quality of Watermarked Image and Extracted Watermark versus Various Attacks
In the following experiment, we used several image manipulations, including Compression, Noise addition, Filtering, Cropping, Changing aspect ratio, Tampering, Copy attack, Scaling and Composite attacks on the watermarked images to evaluate the robustness of the proposed scheme.
4.2.1. Compression
JPEG Compression
Wavelet Compression (JPEG2000)
4.2.2. Noise Addition
4.2.3. Filtering
4.2.4. Geometric Attacks
In the following experiments, different geometric attacks such as scaling, cropping, tampering and changing aspect ratio are performed on the watermarked images to test the robustness of proposed method.
Scaling
Cropping
Changing Aspect Ratio
Tampering and Data Blocks Removal
Copy Attack
The copy attack has been used to create the false positive problem and operated as follow: (1) a watermark is first predicted from watermarked image, (2) the predicted watermark into a target image to create counterfeit watermarked image, (3) from the counterfeit image, a watermark can be detected that wrongly claims rightful ownership.
4.2.5. Composite Attacks
Therefore, the experimental results presented on the quality and recognize ability demonstrates the performance of our method under various attacks.
4.3. Comparison with Other Related Methods
In this subsection, the results of proposed method are compared with two related ones which have been presented by Mahanty and Bhargava [24] and D. Asatryan and N. Asatryan [27]. The comparison is based on four metrics: (1) average execution time for watermark insertion (2) PSNR value of watermarked image, (3) PSNR or correlation value ( ) value of extracted watermark and (4) error rate of detecting watermark.
These three methods were implemented on a personal computer with 1.66 GHZ of CPU and 2 GB of RAM and the average execution time of proposed method for watermark insertion was approximately 2 sec for an image with size 512 × 512 pixels and watermark image with size pixels. The execution time for Mohanty method was 4 sec that is approximately 50% higher in time than proposed algorithm and 1 sec for Asatryan method that is approximately 50% lower than proposed algorithm.
Based on experiments, in the proposed method, the average minimum value of was 0.4 when the extracted watermark was visually detectable. This value for Mohanty method and Asatryan method were 0.65 and 0.3, respectively.
Comparison of proposed method and two related methods.
Host image size  Watermark size  Average PSNR of watermarked Image  Average PSNR of extracted watermark after different attacks  

Proposed method  Mohanty method [24]  Asatryan method [27]  Proposed method  Mohanty method [24]  Asatryan method [27]  

 49.81 dB  39.44 dB  34.72 dB  19.69 dB  16.04 dB  18.10 dB 

 53.15 dB  42.31 dB  35.63 dB  18.68 dB  15.57 dB  17.96 dB 

 48.18 dB  37.98 dB  33.55 dB  20.70 dB  16.93 dB  18.79 dB 

 52.90 dB  41.09 dB  34.73 dB  18.74 dB  15.59 dB  18.52 dB 
Error rates of detecting watermark.
Attack type  Proposed method  Mohanty method [24]  Asatryan method [27] 

Attackfree  0/2500  0/2500  0/2500 
Blurring (2,3)  2/2500  2/2500  2/2500 
Sharpening(1)  3/2500  4/2500  1/2500 
Median Filter ( , )  0/2500  3/2500  5/2500 
Gaussian Noise (0.001, 0.002)  3/2500  3/2500  2/2500 
Gaussian Low Pass Filter ( , )  0/2500  0/2500  1/2500 
Cropping (40%, 50%, 60%)  1/2500  20/2500  1/2500 
Scaling (1/2, 1/4)  2/2500  3/2500  7/2500 
JEPG Compression (10,20,30,40)  3/2500  6/2500  1/2500 
Tampering  0/2500  8/2500  2/2500 
Data Block Removal  0/2500  5/2500  1/2500 
Composite Attack  3/2500  13/2500  21/2500 
Total  16  67  44 
PSNR value of watermarked image ( ) by several methods.
PSNR of watermarked image  

Image  Watermark  Watermark size  Mohanty method [24]  Asatryan method [27]  Proposed method 
Lena  Figure 11(b) 
 43.35 dB  36.10 dB  52.33 dB 
Lena  Figure 11(b) 
 40.71 dB  36.06 dB  48.27 dB 
Lena  Figure 11(d) 
 44.46 dB  35.78 dB  52.92 dB 
Lena  Figure 11(d) 
 40.98 dB  35.82 dB  48.84 dB 
Baboon  Figure 11(b) 
 43.09 dB  36.42 dB  52.44 dB 
Baboon  Figure 11(b) 
 41.01 dB  36.46 dB  48.47 dB 
Baboon  Figure 11(d) 
 44.19 dB  35.91 dB  53.08 dB 
Baboon  Figure 11(d) 
 41.17 dB  35.92 dB  49.11 dB 
Peppers  Figure 11(b) 
 43.27 dB  35.18 dB  51.72 dB 
Peppers  Figure 11(b) 
 39.03 dB  35.20 dB  47.94 dB 
Peppers  Figure 11(d) 
 43.18 dB  34.90 dB  52.27 dB 
Peppers  Figure 11(d) 
 40.80 dB  34.86 dB  48.66 dB 
Crowd  Figure 11(b) 
 43.69 dB  35.33 dB  51.38 dB 
Crowd  Figure 11(b) 
 40.30 dB  35.37 dB  47.63 dB 
Crowd  Figure 11(d) 
 44.12 dB  35.10 dB  52.04 dB 
Crowd  Figure 11(d) 
 40.25 dB  35.04 dB  48.26 dB 
The quality of extracted watermark from Lena image ( ) with watermark size (Figure 11(b)) versus several attacks in different methods.
Attack type  Watermarked image PSNR  of extracted watermark  

Mohanty method [24]  Asatryan method [27]  Proposed method  
No Attack  48.27 dB  0.9975  0.9988  0.9975 
AF( ) + JP(50)  29.49 dB  0.6533  0.6898  0.7008 
S(1/2) + B(3) + JP(60)  28.91 dB  0.5611  0.5709  0.5770 
SH(1) + MF( )  29.25 dB  0.5753  0.7128  0.6389 
MF( ) + S(1/2) + JP(60)  30.25 dB  0.5901  0.4866  0.7178 
GN(0,0.002) + MF( ) + JP(50)  30.03 dB  0.4569  0.5684  0.5360 
GL( ) + CAR(1,1.2) + JP(50)  26.38 dB  0.7072  0.6079  0.8984 
WF( ) + B(2) + JP(40)  31.20 dB  0.5310  0.6404  0.6696 
B(2) + GN(0,0.002) + JP(50)  29.38 dB  0.4085  0.4402  0.5535 
C(40%)  18.01 dB  0.1708  0.3508  0.4506 
S(1/4) + JP(40)  28.59 dB  0.4598  0.2222  0.6341 
The quality of extracted watermark from Baboon image ( ) with watermark size (Figure 11(b)) versus several attacks in different methods.
Attack type  of extracted watermark  

Watermarked image PSNR  Mohanty method [24]  Asatryan method [27]  Proposed method  
No Attack  48.47 dB  0.9961  0.9986  0.9966 
AF( ) + JP(50)  24.32 dB  0.3857  0.5036  0.4550 
S(1/2) + B(2) + JP(60)  25.59 dB  0.3973  0.4318  0.5109 
SH(1) + MF( )  23.86 dB  0.3128  0.6555  0.5246 
MF( ) + S(1/2) + JP(60)  24.37 dB  0.3033  0.4202  0.4293 
GN(0, 0.002) + MF( ) + JP(50)  24.34 dB  0.3048  0.5439  0.4032 
GL( ) + CAR(1,1.2) + JP(50)  27.68 dB  0.4485  0.3643  0.6176 
WF( ) + B(2) + JP(40)  26.48 dB  0.4079  0.4459  0.5079 
B(2) + GN(0,0.002) + JP(50)  25.13 dB  0.4239  0.5226  0.5020 
C(40%)  20.29 dB  0.1279  0.3880  0.4183 
S(1/4) + JP(40)  23.45 dB  0.2017  0.1212  0.4027 
The quality of extracted watermarked from Peppers image ( ) with watermark size (Figure 11(b)) versus several attacks in different methods.
of extracted watermark  

Attack type  Watermarked image PSNR  Mohanty method [24]  Asatryan method [27]  Proposed method 
No Attack  51.72 dB  0.9974  0.9990  0.9979 
AF( ) + JP(50)  29.80 dB  0.4233  0.6118  0.5943 
S(1/2)+B(3) + JP(60)  29.35 dB  0.3711  0.4999  0.5492 
SH(1) + MF( )  29.52 dB  0.4277  0.6256  0.5750 
MF( ) + S(1/2) + JP(60)  30.66 dB  0.4405  0.3304  0.5490 
GN(0, 0.002) + MF( ) + JP(50)  30.34 dB  0.2832  0.4481  0.4021 
GL( ) + CAR(1,1.2) + JP(50)  31.79 dB  0.6008  0.4568  0.7637 
WF( ) + B(2) + JP(40)  31.13 dB  0.5343  0.5928  0.6697 
B(2) + GN(0,0.002) + JP(50)  27.60 dB  0.3900  0.5222  0.5152 
C(40%)  17.06 dB  0.2080  0.3710  0.6544 
S(1/4) + JP(40)  29.06 dB  0.3827  0.3535  0.6415 
The quality of extracted watermarked fromCrowd image ( ) with watermark size (Figure 11(b)) versus several attacks in different methods.
of extracted watermark  

Attack type  Watermarked image PSNR  Mohanty method [24]  Asatryan method [27]  Proposed method 
No Attack  51.38 dB  0.9969  0.9985  0.9974 
AF( ) + JP(50)  25.19 dB  0.3432  0.4803  0.4346 
S(1/2) + B(3) + JP(60)  24.30 dB  0.3040  0.4584  0.4490 
SH(1) + MF( )  24.53 dB  0.2400  0.4056  0.3132 
MF( ) + S(1/2) + JP(60)  25.79 dB  0.3029  0.3505  0.4132 
GN(0, 0.002) + MF( ) + JP(50)  25.90 dB  0.2306  0.4034  0.3389 
GL( ) + CAR(1,1.2) + JP(50)  29.24 dB  0.4064  0.4708  0.5851 
WF( ) + B(2) + JP(40)  27.91 dB  0.3666  0.4660  0.5051 
B(2) + GN(0,0.002) + JP(50)  26.06 dB  0.3099  0.5306  0.4455 
C(40%)  14.70 dB  0.1818  0.3195  0.7337 
S(1/4) + JP(40)  23.86 dB  0.2030  0.2344  0.4182 
5. Conclusion
In this paper, a grayscale watermark insertion and extraction schemes were proposed. The proposed method works by modifying the DC value of the original image in frequency domain to create the watermarked image. The embedding procedure is based on fuzzy inference system to locate the best place of watermark insertion. The algorithm was tested with several standard test images and the experimental results demonstrated that it created highquality images and it was robust versus different attacks. In the future, we are going to change the proposed method such that it can support all attacks by developing a blind method that uses similar idea.
Authors’ Affiliations
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