A Novel Digital Image Watermarking Using Visual Cryptography and ...

CiiT International Journal of Digital Image Processing, Vol 3, No 17, November 2011

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A Novel Digital Image Watermarking Using Visual Cryptography and Cluster Computing Sawsan M. Gharghory and Hanan H. Elazhary

Abstract---Digital image watermarking is a technique in which a secret watermark is embedded into the host image to make an assertion about the host image ownership. A practical image watermarking algorithm should be transparent, robust, secure, blind, and fast. Unfortunately, there is hardly any algorithm in the literature with all these characteristics. In this paper we propose a novel algorithm that satisfies all these properties of digital image watermarking. The proposed technique is a grayscale digital image watermarking using novel visual cryptography and cluster computing. In contrast to other algorithms in the literature, the proposed technique constructs two new shares, the size of each share is equal to that of the watermark and their generation is straightforward and fast. The algorithm embeds a given watermark into the Discrete Cosine Transform (DCT) domain. It is robust since it utilizes the relative values of the low frequency components rather than the absolute values. It is transparent since the watermark bits are not physically embedded into the host image. The algorithm is secure due to the use Torus Automorphism (TA) permutation and a pseudo random number generator. It is also blind since few keys and two shares are needed for watermark extraction. Finally, the algorithm relies on cluster computing for significant speedup. The experimental results prove the robustness of the proposed technique against several types of unintentional attacks. Besides, the results prove the practicality of our approach comparing with other techniques in literature. Keywords---Digital Image Watermarking, Visual Cryptography, Security, Cluster Computing, High Performance Computing. I. INTRODUCTION

T

HE Internet has become the most popular channel for transmitting various forms of multimedia digital data. Multimedia data in digital format can be illegally modified and used with ease. Thus, the copyright protection of digital images transmitted over the Internet has become an important research topic in recent years. Digital image watermarking is used for ownership assertion and copyright protection [1]. In such a technique, the watermark is embedded into the host image such that the embedded watermark can be later extracted to make an assertion about the host image ownership. Digital image watermarking could be done in the spatial domain [2],[3] or in Manuscript received on October 13, 2011, review completed on October 29, 2011 and revised on November 03, 2011. Sawsan M. Gharghory is with the Computers and Systems Department, Electronics Research Institute, Cairo, Egypt. E-Mail: [email protected], [email protected] Hanan H. Elazhary is with the Computers and Systems Department, Electronics Research Institute, Cairo, Egypt. Also she works as an assistant professor at Akhbar Elyom Academy, 6 October City, Egypt. E-Mail: [email protected] Digital Object Identifier No: DIP112011006.

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a transform domain including Discrete Cosine Transform (DCT) [4]-[6], Discrete Wavelet Transform (DWT) [7], [8] or a combination of such domains [9] ,[10]. There are some essential requirements for embedding a watermark into the host image. The first requirement is the invisibility or transparency of the embedded watermark. In other words, the embedded watermark should not be perceived by human eyes and should not degrade the quality of the watermarked host image. The second requirement is the robustness and security of the embedded watermark. In other words, the embedded watermark should be able to resist both intentional and unintentional attacks. The third requirement is speedup that makes the algorithm practically feasible. According to whether the original media is required or not during the watermark extraction process, digital image watermarking algorithms can be classified as follows: Non-blind algorithms that require both the secret key(s) for watermark embedding and the original host image. Semi-blind algorithms that require both the secret key(s) for watermark embedding and the embedded watermark bit sequence. Blind algorithms that require only the secret key(s) for watermark embedding. Neither the original host image nor the embedded watermark bit sequence is needed. It is clear that a practical watermarking algorithm should also be blind. Unfortunately, not all the algorithms in the literature achieve all these characteristics. Thus, in this paper, we propose a novel practical digital-image watermarking algorithm used for embedding a monochrome watermark into a grayscale host image. The proposed algorithm is designed to incorporate the important characteristics discussed above (robustness against intentional and unintentional attacks, transparency, security, blindness, and speedup). The algorithm which is based on visual cryptography and cluster computing is discussed in the rest of the paper. The paper is organized as follows: Section II discusses the related research in the literature and the shortcomings that call for developing the algorithm proposed in the paper. Section III describes the proposed algorithm in terms of the embedding and the extraction processes and emphasizes its advantages. Section IV explains the implementation of the algorithm using cluster computing. Section V provides experimental results that demonstrate speedup using cluster computing and the robustness of the proposed algorithm against unintentional

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CiiT International Journal of Digital Image Processing, Vol 3, No 17, November 2011 attacks. Finally, Section VI provides the discussion and conclusions of the paper. II. RELATED RESEARCH Most of the current watermarking approaches are not strongly robust to different types of attacks, not blind, unsecure, time consuming, and some of them change some pixels of the host image which results in decreasing its image quality. Recently, digital image watermarking based on visual cryptography for more robustness and security has been proposed [11]-[18]. Visual Cryptography (VC) is basically a secret sharing scheme extended for images. It has the ability to restore a secret without the use of computations. The work in [11] and [12] fully employ the visual decryption ability of VC. They first convert the original gray-level host image into a half-tone image. Two random shares of the watermark are then generated. One share is embedded into the half tone image. The other share is kept secret by the owner. Further, watermark can be extracted by simply superimposing the secret share over the half-tone-image. The drawback of this technique is that the host image is altered and that the size of the watermark is restricted, and it is not robust to many attacks. Hwang [13] demonstrated a direct method of hiding binary watermarks into gray-level images without converting them into half-tone images. This technique overcomes the above drawbacks but, doesn’t guarantee the security always. Hence it is unsuitable for digital image copyright protection. Both the introduced work in [14] and [15] overcome the security drawbacks of Hwang’s scheme, but are not robust to some attacks such as jitter, histogram equalization, cropping and rotations. Naor and Shamir scheme [17] replaced each pixel of a given host image by 2×2 pixels. Hence, a host image with M by N pixels can be divided into two sharing images with 2M by 2N pixels. The problem with these techniques is that generating the shares is always time consuming. Besides, the size of each share is usually too large. In our proposed technique, we introduce for a method that is quickly generate a different kind of shares with much smaller size as long as they have the same advantage: helping in obtaining ownership verification information. Several research studies in the literature aimed at developing watermarking techniques to be robust against intentional attacks, secure and have good image quality. The work in [19], and [22]–[24] use the Torus Automorphism (TA) permutation for scrambling the watermark before embedding into the host image. In [20] a pseudo-random number generator has also been proposed to select arbitrary host image blocks and/or watermark bits for embedding. Our proposed technique combines both Torus Automorphism (TA) permutation and a pseudo-random number generator in addition to visual sharing scheme to increase robustness against intentional attacks. Another research trend has been proposed for digital image watermarking in transform domains to achieve more robustness and higher resistance against unintentional attacks. 0974-9691/CIIT–IJ-2465/08/$20/$100 © 2011 CiiT

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DCT domain is one of these transform domains that has been frequently used in the literature. Many techniques embedded watermarks in the most significant components (DC or low frequencies) of DCT domain to achieve more robustness against unintentional attacks. Saryazdi et al. [21] embedded a given watermark bit in the five first low AC coefficients. Similarly, Lin et al [19] embedded two watermark bits in the seven low frequency locations. Patra et al. [20] randomly selected a low frequency location for embedding a given watermark bit. However, the disadvantage of these techniques is that they physically embed the watermark bits which result in changing some pixels of the host image and degrading its quality. Also, another disadvantage of the above techniques is that they utilize the absolute values of the DCT coefficients which can easily change under unintentional attacks. Thus, some techniques have been proposed for non physical embedding of the watermark to achieve 100% transparent watermarking and watermarked image with no distortion as described in [25], [26]. Our proposed method is superior to the above related method in terms of transparency and robustness. The proposed technique does not change any pixel value of the DCT-transformed host image and also attempts to utilize the more robust relative values of the DCT coefficients instead of the absolute values. Elazhary et al. [27] designed a robust blind transparent secure algorithm but unfortunately their algorithm is very slow. In additional to the disadvantages of the above techniques in literature, they all suffer from the long processing time as the majority of the above techniques have been designed to run on sequential processors. Thus, we attempt to tackle this problem in the current work. Our proposed technique has been designed with parallel processing in mind to achieve speedup. Thus, it was easily implemented using cluster computing. The proposed algorithm is described in the following section. III. THE PROPOSED ALGORITHM The proposed algorithm can be described in terms of the watermark embedding process and the watermark extraction process. A. The Watermark Embedding Process The steps of the proposed watermark embedding process can be described as follows: Step 1: Divide the host image into non-overlapping 4 * 4 blocks. Step 2: Use Torus Automorphism (TA) permutation [17] to disarrange the watermark bits using the following equation: i

*

j

*

1

1

k

k

* 1

i

mod m

j

(1)

Equation (1) indicates that each bit of the watermark at location (i, j) will be moved to a new location (i*, j*).


CiiT International Journal of Digital Image Processing, Vol 3, No 17, November 2011 Parameters k and m are secret keys needed for disarranging and rearranging the watermark bits. These permuted watermark bits will be embedded bit by bit and line by line into the host image. Step 3: Arrange the host image blocks sequentially one by one and row by row. Use a pseudo-random number generator to determine the sequence of host image blocks used for embedding the permuted watermark bits. A pseudo-random number generator with a given seed always generates the same sequence of random numbers. Thus, the seed of the pseudo-random number generator is a secret key needed to embed and extract the permuted watermark bits. Steps 2 and 3 offer cryptographic protection against intentional attacks because the secret keys of the pseudo-random number generator and TA permutation are necessary for extracting and rearranging the embedded watermark for subsequent destruction. Step 4: DCT-transform the next host image block from the spatial domain to the frequency domain. Each block is DCT-transformed using the following equation [21]: DCT ( i , j )

* cos

1

N 1

N

x 0

y 0

C (i ) * C ( j ) *

(2 x

1) i

cos

2N

Where: C ( i ), C ( j )

pixel ( x , y ) (2 y

(2)

1) j 2N

1 N

for i , j 0

2 N

otherwise

In equation (2), DCT (i, j) represents the value at location (i, j) in the DCT-transformed block, while pixel (x, y) represents the value at location (x, y) in the original block. N is the number of locations (pixels) in each block. The operation domain of the algorithm is the DCT domain rather than the spatial domain since embedding a watermark into a transform domain is more robust, and has higher resistance to various attacks [5]. Step 5: Embed the next permuted watermark bit into the low frequencies of the DCT-transformed block. To increase the robustness of the embedded watermark against unintentional attacks, as explained in Section II, the locations for embedding a permuted watermark bit are restricted to the low frequency locations 0, 1, 2, and 3 as shown in Figure 1. The embedding location with the largest DCT coefficient is selected as the embedding location for a permuted watermark bit with value 1, while the embedding location with the smallest DCT coefficient is selected as the embedding location for a permuted watermark bit with value 0. It should be noted that the largest and the smallest values of the DCT coefficients are relative values that are unlikely to change under attacks. This algorithm, thus, attempts to

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utilize the more robust relative values of the DCT coefficients instead of the absolute values that can easily change under attacks. It should be also noted that we do not need to physically embed a permuted watermark bit. We only need to remember its embedding location. This implies that in the proposed algorithm the embedded watermark is 100% transparent. Since there are only 4 possible embedding locations, we need only two bits to represent each embedding location. For example, (0, 0), (0, 1), (1, 0), and (1, 1) can be used to represent embedding locations 0, 1, 2, and 3 respectively. Thus, a permuted watermark bit is replaced by two bits representing its embedding location. Steps 4 and 5 are repeated until all the permuted watermark bits have been embedded in the DCT-transformed host image blocks. Since each permuted watermark bit is represented by its embedding location and since an embedding location is represented by two bits, the final result is an array with double the size of the watermark array. Step 6: The resultant array of the embedding locations of the permuted watermark bits is decomposed into two arrays of the same size. The first array stores the first bit representing each permuted watermark bit embedding location, while the second array stores the second bit. The two arrays are of the same size as the watermark array and are considered to be the watermark shares or transparencies [23]. For example, suppose that the embedding location of the permuted watermark bit (a, b) is location 1 as shown in Figure 1. This embedding location is represented by (0, 1). Thus, the corresponding value of bit (a, b) in share or transparency 1 is 0 and in share or transparency 2 is 1. One of the generated transparencies or shares is registered to the Certified Authority (CA) for additional security and protection against intentional attacks (that attempt to extract the embedded watermark for subsequent destruction) as mentioned before. Step 7: Finally, inverse DCT-transform each watermarked block from the frequency domain to the spatial domain forming the watermarked host image using the following equation [21]: N 1

N 1

j 0

j 0

pixel ( x , y )

* cos

(2 x

C ( i ) * C ( j ) * DCT ( i , j ) 1) i

cos

(2 y

2N

Where: C ( i ), C ( j )

1) j

(3)

2N

1 N

for i , j 0

2 otherwise N

It is worth noting that in the proposed algorithm, this step does not need to be explicitly performed. This is because the


CiiT International Journal of Digital Image Processing, Vol 3, No 17, November 2011 watermark bits are not physically embedded into the host image blocks as explained above. B. The Watermark Extraction Process The steps of the proposed watermark extraction process are the reverse of the steps of the watermark embedding process. They can be described as follows: Step 1: Divide the watermarked host image into non-overlapping 4 * 4 blocks. Step 2: Reassemble the two transparencies or shares to generate the permuted watermark bits embedding locations. Remember that the two values in each two corresponding locations in the two transparencies are the two values representing the embedding location of the corresponding bit of the permuted watermark. For example, suppose that the value of bit (a, b) in share 1 is 0 and that of bit (a, b) in share 2 is 1. This implies that the embedding location of bit (a, b) of the permuted watermark is that represented by (0, 1), which is location 1 shown in Figure 1. Step 3: Arrange the watermarked host image blocks sequentially one by one and row by row. Use a pseudo-random number generator (with the same seed as the embedding pseudo-random number generator) to determine the sequence of host image blocks used for embedding (and thus extracting) the permuted watermark bits. Step 4: DCT-transform the next watermarked host image block from the spatial domain to the frequency domain. Step 5: Extract the next permuted watermark bit from the corresponding embedding location in the low frequencies of the DCT-transformed block. The corresponding bits in the reassembled transparencies are used for specifying the embedding location in the DCT-transformed block as explained above. If the embedding location has the largest value among the four low frequency locations shown in Figure 1, the embedded permuted watermark bit is a 1. Otherwise, it is a 0. Steps 4 and 5 are repeated until all the embedded permuted watermark bits have been extracted. 0

1

2

3

Fig. 1 Possible Embedding Locations in a 4*4 DCT-Transformed Block

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be preserved well for watermark extraction and ownership verification and to increase the robustness of the embedded watermark against intentional attacks (that attempt to extract the embedded watermark for subsequent destruction). These secret keys are as follows: The two shares or transparencies needed for reassembling the embedding locations of the permuted watermark bits. The size of the transparencies is the same as that of the watermark. The seed of the pseudo-random number generator needed for determining the sequence of host image blocks used for embedding (and thus extracting) the permuted watermark bits. The k and m parameters of Torus Automorphism (TA) permutation. These are needed to disarrange the watermark bits before embedding and thus to rearrange the extracted permuted watermark bits. It is clear that the proposed algorithm is blind. This is because the watermark extraction process requires only the secret key(s) for watermark embedding in addition to the two transparencies representing the embedding locations of the permuted watermark bits. Neither the original host image nor the embedded watermark bit sequence is needed. IV. CLUSTER COMPUTING Cluster computing is the technique of linking a number of computers (usually through a local area network) in order to take advantage of the parallel processing power of those computers. The organization of the cluster hardware used in the parallel implementation of the proposed algorithm is shown in Figure 2. It is formed of a master and several slaves. Each node of the cluster is an Intel Pentium 4 3.2 GHz processor with 1 MB cache running Scientific Linux operating system and MPI for communication. A 10/100 M bit network switch is used for connecting the master and the slaves. The parallel version of the proposed algorithm can be described in terms of the watermark embedding process and the watermark extraction process.

Fig. 2 The Organization of the Cluster Hardware

A. The Watermark Embedding Process Step 6: After extracting all the permuted watermark bits, use Torus Automorphism (TA) permutation to rearrange the watermark bits (using the same keys used for disarranging the watermark bits before embedding). C. The Secret Keys Several kinds of secret keys are utilized in the proposed algorithm. These secret keys are necessary in the proposed watermark embedding and extracting processes. They should

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It may seem natural to let the master divide the host image into blocks and the permuted watermark image into bits and distribute these among the slaves to embed the watermark bits into the host image blocks. But, experimental results showed that dividing the two images by the master takes about 21 minutes, which is a relatively long time. Thus, a better solution is to send the host image and the permuted watermark image to all the slaves and let each extract its block(s) and watermark


CiiT International Journal of Digital Image Processing, Vol 3, No 17, November 2011 bit(s) according to its rank. This can be explained by assuming the following scenario: The host image which is of size 512*512 is divided into blocks of size 4*4. In other words, it is divided into 16384 blocks; 128 blocks in each row. The blocks are numbered sequentially one by one and row by row. The blocks in the first row are numbered 1 through 128, the blocks in the second row are numbered 129 through 256, and so on. The watermark image which is of size 128 * 128 is divided into 16384 bits; 128 bits in each row. This implies that each block in the host image has a corresponding similarly numbered bit in the watermark image. The host image blocks and the watermark bits are divided among 64 slaves. Slave 1 selects block 1 and watermark bit 1. Incrementing by the number of slaves, it selects block 65 and watermark bit 65. This continues until the number of the blocks and the watermark bits exceeds 16384. Also, slave 2 does the same, but starting from block 2 and so on for the other slaves. The equation for specifying the selected blocks for each slave is as follows: Si

 Bi

j

for

i

1 : 1 : 64

j

0 : 64 : n

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Following the scenario explained in Section IV.A, 16384 bits are received from all the slaves and are used to reconstruct the extracted watermark image.

(a) (b) (c) Fig. 3 The Three Host Images (a) Boat, (b) Baboon, and (c) F-16

Fig. 4 The Rose Watermark Image

(4)

Where B is the selected block and n is the number of blocks in host image and in this case equal to 16384. Each block in the host image is of size 4*4. Thus, it contains four bits in each row. The indices of the upper left corner bit of block 1 are (0, 0). Incrementing by 64*4, the indices of the upper left corner bit of block 65 are (0, 256). Similarly, those of block 128 are (0, 508). Since the number of blocks in each row is 128, indices (0, 512) do not exist. Block 129 is the first block in the second row. The indices of the upper left corner of this block are (4, 0). The indices of all the other remaining blocks can be deduced similarly. Finally, each slave sends to the host image 2 bits corresponding to each embedded watermark bit. These two bits represent the embedding position of the corresponding watermark bit and are used to compose the two watermark shares as explained in Section III.A. B. The Watermark Extraction Process The master sends the watermarked image and the two shares to each slave. Each slave extracts the corresponding block(s) and permuted watermark bit(s) embedding location as explained in Section IV.A. It then extracts the corresponding permuted watermark bit(s) and sends it to the master.

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Fig. 5 Processing Time during Watermark Embedding for Different Numbers of Cluster Nodes

Fig. 6 Processing Time during Watermark Extraction for Different Numbers of Cluster Nodes

V. EXPERIMENTAL RESULTS This section provides experimental results that demonstrate speedup due to cluster computing and the robustness of the proposed algorithm against unintentional attacks. Three host images have been used in the experiments. These are the Boat image, the Baboon image, and F-16 image shown in Figure 3. Each of these images is of size 512*512. The Rose watermark shown in Figure 4 is of size 128*128 and is used for watermarking the host images.


CiiT International Journal of Digital Image Processing, Vol 3, No 17, November 2011 A. Speedup Due to Cluster Computing In the experiments, the host image is divided into blocks of size 4*4. In other words, 16834 blocks are being processed. Figures 5 and 6 show the dramatic reduction in the processing time of the blocks during watermark embedding and extraction respectively when running the algorithm on different number of cluster nodes. It is clear that increasing the number of processors improves the speedup since communication is done only between the master and the slaves. In other words, there is no inter-slave communication. B. Robustness against Unintentional Attacks

watermark embedding and extraction are both demonstrated using three test images under several attacks: median filtering, blurring, sharpening, Gaussian noise addition, salt and pepper noise addition, and JPEG compression with different quality factors 30, 50, and 70. REFERENCES [1]

[2] [3]

Figures 7 to 9 show the extracted watermarks from the Boat, the Baboon, and F-16 images respectively under different types of unintentional attacks. It is clear that the watermarks, embedded using the proposed algorithm, are robust to these different types of attacks. This is not only due to embedding the watermark bits in the low frequencies of the DCT-transformed blocks of the host image, but also due to intelligently utilizing the more robust relative values of the low frequency components instead of the absolute values as explained in Section III.

[4] [5] [6]

[7] [8]

VI. SUMMARY AND CONCLUSIONS This paper is concerned with digital image watermarking for ownership assertion and copyright protection. A novel practical algorithm has been developed and discussed. The algorithm is robust to unintentional attacks, not only due to utilizing the low frequency DCT components of the host image blocks, but also due to intelligently utilizing the more robust relative values of these low frequencies instead of the absolute values. The algorithm is 100% transparent since the watermark bits are not physically embedded into the host image. This has the advantage of preserving the host image quality. To achieve blindness, the algorithm is designed such that it only requires few keys and two transparencies for watermark extraction. For security, the algorithm is based on visual cryptography to produce two shares and register one to the Certified Authority (CA). In the literature, the size of the shares is usually double the size of the host image and the process of generating the shares is usually time-consuming. In the proposed algorithm, generating the shares is straightforward and fast and the size of each share is exactly equal to the watermark size. The algorithm also utilizes Torus Automorphism (TA) permutation to disarrange the watermark bits before embedding into the host image (and reassemble them after extraction) and a pseudo-random number generator to select a random sequence of host image blocks for embedding the scrambled watermark bits. To speedup digital image watermark embedding and extraction, the algorithm has been designed with parallel processing in mind. Thus, it was easily implemented using cluster computing. The robustness of the proposed algorithm against unintentional attacks and the dramatical speedup during both

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[10]

[11]

[12]

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[17] [18]

[19]

[20]

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CiiT International Journal of Digital Image Processing, Vol 3, No 17, November 2011 [21] S. Saryazdi and M. Demehri, "A blind DCT domain digital watermarking," in Proc. the 3rd International Conference on Sciences of Electronic, Technologies of Information and Telecommunications, 2005 [22] G. Voyatzis and I. Pitas, "Applications of toral automorphisms in image watermarking," in Proc. the Image Processing International Conference, 1996. [23] C. Chang, J. Hsiao, and C. Chiang, "An image copyright protection scheme based on torus Automorphism," in Proc. of the 1st International Symposium on Cyber Worlds, pp. 217–224, 2002. [24] M. Engedy, V. Munaga, and A. Saxena, "A robust wavelet based digital watermarking scheme using chaotic mixing," in Proc. of the 1st International Conference on Digital Information Management, pp. 36–40, 2006. [25] C. Chang and J. Chuang, "An image intellectual property protection scheme for gray-level images using visual secret sharing strategy," Pattern Recognition Letters, vol. 23, pp. 931–941, 2002. [26] M. Hua, D. Loub, and M. Chang, "Dual-wrapped digital watermarking scheme for image copyright protection," Computers & Security, vol. 26, pp. 319-330, 2007. [27] Elazhary H., and Gharghory S. "Blind Robust Transparent Based Digital Image Watermarking for Copyright Protection" International Journal of Computer Science and Information Security IJCSIS, Vol. 8, No. 7, pp. 183-188, 2010.

Sawsan M. Gharghory received her degree of B.Sc. in Electronics and Communications Engineering, also the degree of M.Sc. and Ph.D. in Electronics and Communications Engineering from the faculty of Engineering, Cairo University, Egypt. Currently, she is a researcher at the Electronics Research Institute, Dokki, Cairo, Egypt. Her research interests include image processing, pattern recognition, and the applications of artificial intelligence, and evolutionary computation. Hanan H. Elazhary received her B.Sc. degree in Electronics andCommunications Engineering and her M.Sc. degree in Computer Engineering from the faculty of Engineering, Cairo University, Egypt. She received her Ph.D. degree in Computer Science and Engineering from the University of Connecticut, USA. Currently, she is working as a researcher at the Electronics Research Institute, Cairo, Egypt. Her research interests include High Performance Computing (HPC), image and video processing, software engineering, artificial intelligence, and computer networks

Fig 7 The Extracted Watermarks from Boat Image under Different Attacks

Fig 8 The Extracted Watermarks from Baboon Image under Different Attacks

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Fig 9 The Extracted Watermarks from F-16 Image under Different Attacks

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