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A Robust Image Watermarking Technique Based On Image Interlacing

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As a result to ensure and facilitate data authentication, security and copyright protection of digital media, digital watermarking techniques are widely used in these domains. The non-blind watermarking systems have a main problem that for extracting
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   31 st  National Radio Science Conference(NRSC2014) April 28 – 30, 2014, Faculty of Engineering, Ain Shams University, Egypt 92  C3. A Robust Image Watermarking Technique Based On Image Interlacing  Mohamed M. Ibrahim 1  , Neamat S. Abdel Kader 2  , M. Zorkany 3   1,3  National Telecommunications Institute, Cairo, Egypt, 1 mohamed_mahmoud1980@yahoo.com 2 Faculty of Engineering, Cairo University, Cairo, Egypt, nemat2000@hotmail.com, 3 m_zorkany@nti.sci.eg A BSTRACT As a result to ensure and facilitate data authentication, security and copyright protection of digital media, digital watermarking techniques are widely used in these domains. The non-blind watermarking systems have a main problem that for extracting the watermark image from the watermarked image, the srcinal image is required. This means an overhead over the system resources like memory in both sender and receiver and  bandwidth in communications channel. In this paper a solution and more effective non-blind image watermarking for this problem is proposed. The proposed method depends on using image interlacing as a new way in the watermarking systems. The performance of this method is compared with the ordinary non-blind watermarking technique over a wide number of images. Simulation results show the effectiveness of the proposed method and robustness against attacks.  Keywords : watermarking, non-blind,   interlacing, sub-images, de-interlacing and DWT    I.   I NTRODUCTION   As the rapid development in the information and communication technology the copyright protection of the digital multimedia like image, audio and video is very necessary. Digital watermarking plays an important role in this field by hiding secret information into the srcinal media called watermark. This watermark is then extracted out at receiver to represent the ownership and/or the identity of this digital media [1]. Digital image watermarking is categorized according to the srcinal image domain into two types: Spatial domain and Frequency or Transform domain. In spatial domain the watermark embedding is performed by modifying the srcinal image pixels themselves such as LSB (Least Significant Bit), SSM (Spread-Spectrum Modulation). In Frequency or Transform domain the watermark embedding is performed by modifying the frequency components of the srcinal image such as DFT (Discrete Fourier Transform), DCT (Discrete Cosine Transform), DWT (Discrete Wavelet Transform),… The transformation from spatial domain to frequency domain and the inverse operation makes the frequency domain algorithms more complex than the spatial domain algorithms but more robust [1]. In this paper Three Level DWT is used as an example of frequency domain algorithms as shown in section II. Digital image watermarking is also categorized according to the need of the srcinal image in the watermark recovery operation into two types: Blind and Non-blind watermarking. Non-blind watermarking needs the srcinal image in the watermark recovery operation while blind watermarking doesn’t need it [2]. For more robustness in the both types, the watermark image is encrypted before embedding it in the srcinal image. The encryption key may be symmetric or asymmetric [3]. This encryption key and the watermarking operations parameters formed a secret key between sender and receiver. In this paper Arnold Transform is used as one of popular Symmetric key image encryption methods as shown in section II.   The availability of the srcinal image in the watermark recovery operation gives the non-blind watermarking two advantages over the blind watermarking, but also gives one disadvantage. For the advantages: First, more simplicity in watermark embedding and recovery, Second, more robustness against attacks because the extracted watermark is more similar to the srcinal one [2]. For the disadvantage: Double storage capacity in memory in  both sender and receiver [1] and double communication traffic per time (Bandwidth) in the communication channel between them. The main goal of this paper is how to use image interlacing to solve this disadvantage of the non-blind image watermarking. More details about the definition of the image interlacing and its history in section II, and the proposed method in section III. Many tools are used to evaluate the performance of any watermarking systems. PSNR [4] (Peak Signal to Noise Ratio): used to measure imperceptibility of the watermarking system by measuring the quality of the watermarked image. NC [4] (Normalized Correlation) or the 978-1-4799-3821-6/14/$31.00 ©2014 IEEE   31 st  National Radio Science Conference(NRSC2014) April 28 – 30, 2014, Faculty of Engineering, Ain Shams University, Egypt 93  similarity factor: used to measure the robustness of the watermarking system against attacks by measuring the similarity between the srcinal watermark image and the recovered one. The simulation results are given in section IV. II.   T ECHNIQUES THAT THE P ROPOSED M ETHOD D EPEND ON  A.   Arnold Transform The idea of the Arnold Transform [5] as an encryption method for any image of size N x N is the changing of the location of each pixel according to the following equation: (1) Where (x, y) is the current location and (x’, y’) is the new location, after a certain number of iterations called Arnold Period the image is returned to its original state, this period is changing according to the image size, bigger image size bigger Arnold Period (for image of size 32 x 32 the Arnold Period = 48, for image of size 64 x 64 the Arnold Period = 96 and so on). To use Arnold Transform as an encryption method, a number of iterations less than the Arnold period is required, this number of iterations is used as a key of type symmetric which means that the same number of iterations is used in decryption operation but using the inverse of the encryption formula. B.   Three Level DWT Watermarking In DWT the image is passed through a series of low pass and high pass filters which decompose the image into sub-bands of different resolutions [4]. These decompositions can be done at different levels, at the first level, the image is decomposed into four non-overlapping multi-resolution sub-bands: LL1 (Approximate), HL1 (Horizontal), LH1 (Vertical) and HH1 (Diagonal). Here, LL1 is the low frequency component where HL1, LH1 and HH1 are the high frequency (detail) components. At the higher levels, the sub band LL (n) is decomposed into LL (n+1), LH (n+1), HL (n+1) and HH (n+1), where n is equal to 1, 2, 3, ... The low frequency components like LL3 sub-band contains the most significant portions of the image in which any alterations like watermark embedding will make a distortion in the srcinal image. The high frequency components like HH1 sub-band contains the least significant portions of the image that image processing operations like compression will eliminate them. The best areas for watermark embedding are the mid frequency components like HL3, LH3 and HH3 sub-bands. But HH3 sub-band contains edges and textures of the srcinal image; hence it will be excluded. The rest choices are HL3 and LH3 sub-bands. In the Human Visual System (HVS) model the human eye is less sensitive to the horizontal than the vertical components, which means that the watermark embedding in the HL3sub-band is more imperceptible than the LH3 sub-band, hence the target sub-band is HL3 [4]. C.   Image Interlacing Image interlacing (also known as interleaving) is an operation in which the srcinal image is divided into sub-images. Each sub-image has the same number of unrepeated pixels. The interlacing algorithm indicates the contents of each sub-image. The reverse operation called de-interlacing, in which these sub-images are combined together to generate back the srcinal image [6]. The first use of the image interlacing is how to solve the problem of communicating the internet over a slow communications link. Before image interlacing, the image pixels are loaded in order left-to-right and top-to-bottom which means that the complete loading of the image will take a long time. By image interlacing, the srcinal image is divided into a number of sub-images and these sub-images are loaded in sequence. As a result, the viewer can partially see a degraded copy of the image until it will be a  perfectly clear copy. This operation helps the viewer to decide more quickly whether to abort or continue the transmission. Lots of image formats support interlacing, including GIF (Graphics Interchange Format), JPEG (Joint Photographic Experts Group), and PNG (Portable Network Graphics). Each of them has its own interlacing algorithm [7], [8]. Another way to use the image interlacing is presented in paper [9], where the srcinal image is interlaced by rows (One level interlacing) into two sub-images (even rows and odd rows), and two different watermark images are embedded, one in each sub-image (Multiple Watermarking System). It is noticed that the two sub-images are very similar and from this similarity the idea of this paper is gotten.     31 st  National Radio Science Conference(NRSC2014) April 28 – 30, 2014, Faculty of Engineering, Ain Shams University, Egypt 94  III.   P ROPOSED M ETHOD   In the non-blind watermarking two identical copies of the srcinal image I and I*are used. At the sender, one of them (I) is watermarked by a watermark image W, scaled by a factor α  and then sent to the receiver to be I'. The other one I* is sent as it. During the communications channel, the watermarked image I' is attacked to be I''. At the receiver, the watermark image is recovered to be W'. The following equations present the watermark embedding and extracting operations in order [10]:  I’ = I + α W    (2) W’ = (I’’ - I*)/  α   (3) The proposed method is a new way to use the image interlacing in digital watermarking, but in this case in single non-blind watermarking. In this method the srcinal image is interlaced into sub-images, then by calculating the similarity factor (NC) between each pair of them. The pair which gives the biggest NC value contains the most similar two sub-images (close to be identical). These two sub-images can play the same role of the two identical copies of the srcinal image in the ordinary non-blind watermarking. Instead of embedding the watermark image in the whole srcinal image, the embedding is performed in sub-image of it as shown in figure 1. As a result, there is no need to another copy of srcinal image in the watermark image recovery (the main goal of this paper). For the pervious equations (2, 3): I and I* will be replaced by the most similar two sub-images, I' by the watermarked sub-image and I'' by the attacked watermarked sub-image. Fig. 1: Proposed method. (a) At the sender, and (b) At the receiver. In the proposed method, the watermarking operation is performed after the interlacing operation directly which means that both operations must be performed on the same colour band (Red or Green or Blue). Also means that our work must be done in two steps: The first step is the selecting of this colour band (interlacing only) and the second step is the whole algorithm (interlacing and watermarking) performed on it. The selected colour band must  be the colour band that gives the most similar two sub-images (biggest NC value) for number of test images. Finally to present our solution a comparison is made between an ordinary non-blind watermarking algorithm without image interlacing and the same algorithm with interlacing . IV.   E XPERIMENTAL R  ESULTS   In this paper eight famous standard testing images are used, each of size 512 x 512: “Lena, Baboon, Peppers, F-16, House, Sailboat, Splash &Tiffany”, and a gray scale image “Evil Inside” [11] of size 32 x 32 is used as a watermark image as shown in figure 2. A.   First Step (Interlacing Only) In this step there are two levels of interlacing (two MATLAB codes) are proposed: 1)   One Level Interlacing:  For any image of size (rows x columns), there are two types of one level interlacing: By rows only and By columns only. By rows only produces pair of sub-images (Even Rows (ER) and Odd Rows (OR)) each of size (rows/2 x columns) as shown in figure 3 (a). By columns only  produces also pair of sub-images (Even Columns (EC) and Odd Columns (OC)) each of size (rows x columns/2) as shown in figure 3 (b). Table 1 presents the NC values between each two sub-images in each  pair in different colour bands, the values in bold font indicates the biggest values for each image.   31 st  National Radio Science Conference(NRSC2014) April 28 – 30, 2014, Faculty of Engineering, Ain Shams University, Egypt 95  2)   Two Level Interlacing  :  Interlacing an image of size (rows x columns) by rows first then by columns (or in the opposite order) will produce four sub-images: Even Rows Even Columns (EE), Even Rows Odd Columns (EO), Odd Rows Even Columns (OE), and Odd Rows Odd Columns (OO) each of size (rows/2 x columns/2) as shown in figure 3 (c). Also, Table 2 presents the NC values between each two sub-images. Fig. 2: (a) Test images, and (b) Watermark image. Fig. 3: Interlacing. (a) One level by rows, (b) One level by columns and (c) Two level. Table 1: One Level Interlacing From the one level interlacing results (Table 1) there are two notes: First, for each test image, the NC values  between each two sub-images in each pair are different in the same colour band and in different colour bands, but in general, all values are very close to one (Max = 0.9924 ≈  1, Min = 0.7579  and Avg = 0.945021 ≈  1) which indicates that all sub-images in all pairs are very similar to each other. Second, the biggest NC values (bold font) for both test images do not exist in the same colour band. For five test images from the selected eight test images the biggest NC values exists in the red colour band (Lena,Baboon,F-16, Splash, and Tiffany), and for other two test images (Peppers and Sailboat) in the green colour band, and for the rest test image (House) in the blue colour  band. So, the colour band that gives the most similar two sub-images in almost test images is the red.   Color Band Sub-Images   NC    Lena Baboon Peppers F-16 House Sailboat Splash Tiffany Red ER - OR 0.9894  0.8597 0.9646 0.9432 0.9533 0.9512 0.9918 0.9186 EC - OC 0.9798 0.9228 0.9585 0.9719 0.9544 0.9533 0.9924 0.9542 Green ER - OR 0.9824 0.7579 0.9774 0.9652 0.9279 0.9622 0.9837 0.9200 EC - OC 0.9690 0.8657 0.9780 0.9447 0.9341 0.9701 0.9762 0.8809 Blue ER - OR 0.9578 0.8777 0.9608 0.9150 0.9591 0.9686 0.9707 0.9100 EC - OC 0.9329 0.9072 0.9648 0.9625 0.9751 0.9697 0.9785 0.8961   31 st  National Radio Science Conference(NRSC2014) April 28 – 30, 2014, Faculty of Engineering, Ain Shams University, Egypt 96  Table 2: Two Level Interlacing  From the two level interlacing results (table 2) there are the same notes as table 1: First, all NC values are very close to one (Max = 0.9973 ≈  1, Min = 0.7203  and Avg = 0.935341 ≈  1). Second, the colour bands that give the  biggest NC values for each test image are the same, which indicates that if the one level interlacing for any image gives the most similar two sub-images in specified colour band, the two level interlacing for this image will give the most similar two sub-images in the same colour band. As a summary from both tables, the selected colour  band is the red. More than two levels of interlacing can be done, but there is a limitation: More levels of interlacing means more small size sub-images. In order to embed any watermark image in any image (or sub-image of it, or sub- band of sub-image of it as in this paper), the watermark image size must be equal to the size of this image (or smaller than it and then resized to be equal to it) but not bigger than it. B.   Second Step (Watermarking Algorithm With and Without Interlacing) In this step there are three Matlab codes are proposed: 1)   Ordinary Non-Blind watermarking algorithm (without interlacing) . 2)   The same algorithm with one level interlacing.  3)   The same algorithm with two level interlacing.  Both three cases are divided into the following parts according to the sequential operations: Fist: At the Sender: 1.   Read the cover image, separate the RGB color bands then select the red color band. 2.   Read the watermark image and encrypt it using Arnold Transform (equation 1) by number of iterations less than the Arnold Period according to the watermark image size. This number will be the first part of the secret key between the sender and the receiver. 3.   For both second and third codes, interlace the cover image and get the most similar pair of sub-images. This pair will be the second part of the secret key. 4.   For both second and third codes, select first sub-image from the selected pair and convert it using three level DWT then embed the encrypted watermark image in HL3 sub-band according to equation 2. For the first code, this operation is performed on the cover image directly. The scaling factor α  will be the third part of the secret key. 5.   For both second and third codes, de-interlace the red color band of the watermarked image from the watermarked sub-image and other sub-images. Color Band Sub-Images   NC    Lena Baboon Peppers F-16 House Sailboat Splash Tiffany Red EE - EO 0.9797 0.9215 0.9589 0.9715 0.9520 0.9533 0.9925 0.9518 EE -OE 0.9865 0.8653 0.9636 0.9585 0.9557 0.9541 0.9973 0.9385 EE -OO 0.9670 0.8541 0.9486 0.9357 0.9210 0.9399 0.9905 0.9043 EO -OE 0.9748 0.8508 0.9506 0.9360 0.9227 0.9352 0.9903 0.9122 EO-OO 0.9866  0.8654 0.9630 0.9586 0.9562 0.9546 0.9973 0.9378 OE-OO 0.9799 0.9241 0.9582 0.9723 0.9569 0.9534 0.9924 0.9564 Green EE - EO 0.9690 0.8640 0.9782 0.9446 0.9329 0.9703 0.9762 0.8773 EE - OE 0.9811 0.7604 0.9767 0.9672 0.9150 0.9629 0.9824 0.9235 EE -OO 0.9543 0.7322 0.9605 0.9201 0.8609 0.9483 0.9617 0.8381 EO -OE 0.9636 0.7203 0.9630 0.9171 0.8641 0.9467 0.9617 0.8438 EO-OO 0.9809 0.7609 0.9770 0.9684 0.9179 0.9633 0.9826 0.9343 OE-OO 0.9690 0.8673 0.9779 0.9449 0.9354 0.9699 0.9762 0.8847 Blue EE - EO 0.9326 0.9061 0.9653 0.9610 0.9751 0.9696 0.9784 0.8920 EE - OE 0.9554 0.8788 0.9628 0.9190 0.9548 0.9688 0.9696 0.9164 EE -OO 0.9170 0.8387 0.9426 0.8974 0.9336 0.9513 0.9513 0.8397 EO -OE 0.9233 0.8391 0.9429 0.8897 0.9352 0.9496 0.9525 0.8604 EO-OO 0.9560 0.8788 0.9631 0.9188 0.9550 0.9689 0.9694 0.9194 OE-OO 0.9332 0.9083 0.9642 0.9640 0.9753 0.9698 0.9787 0.9000
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