A Robust Digital Watermarking Scheme using BTC-PF in Wavelet Domain

A Robust Digital Watermarking Scheme using BTC-PF in Wavelet Domain Chinmay Maiti a *, Bibhas Chandra Dhara b a Department of Computer Science & Engineering, College of Engineering & Management, Kolaghat, West Bengal, India, chinmay@cemk.ac.in b Department of Information Technology, Jadavpur University, Kolkata, West Bengal, India, bibhas@it.jusl.ac.in Abstract In this paper, a non-blind watermarking technique has been proposed where a grayscale watermark is embedded. In the embedding process, first the host image is decomposed by wavelet transform and a middle frequency subband is selected where the watermark is embedded. Then, both the watermark and the selected subband are encoded by BTC-PF method. For each block, BTC-PF method returns quantization levels and an index of the selected pattern. To enhance the security level the watermark information is permuted randomly. The quantization levels of the subband are modified with the watermark and subband is reconstructed with the modified quantization levels and indices of the watermark. A watermarked image is produced by applying inverse wavelet transform. The experimental result shows that the proposed watermarking technique is a robust against different type of attacks, like blurring, motion blurred, salt and pepper noise, Gaussian filter, cropping and JPEG compression. Keywords: Grayscale watermarking, wavelet transform, BTC-PF coding, copyright protection, image attacks. 1. Introduction Fast development of the multimedia technology has made it possible to create duplicate copy, transmit and distribute digital data by any one easily and unlimitedly. Thus, the question of copyright is associated with digital data that faces a severe threat from unauthorized users. Many techniques have been devised to protect the copyright of digital data. The technique of digital watermarking provides a reliable and secure copyright protection of digital data. In digital watermark, a secret code or image is hidden inside the host image, so as to claim for the copyright of host image. In order to be effective, a watermarking should at least satisfy the following requirements. 1. The watermark should be undetectable by the unauthorized users. 2. The watermark should be detectable by authorized users easily and securely. 3. The watermarked image and the host image could not be differentiated by the naked eye. 4. The watermark should be extracted from the attacked watermarked image (different image processing techniques applied on watermarked image). The design of a watermark technique to fulfill all of the above requirements is not an easy task. The classification of watermark techniques is done based on different criteria. Firstly, watermark techniques can be grouped into three categories based on the type of information needed during extraction of watermark: (i) non-blind (host image is required), (ii) semi-blind (watermark image is required) and (iii) blind (no information is required). Secondly, watermark techniques can be grouped into two categories based on the processing domain: (i) spatial domain approach where watermark is embedded directly by modifying the pixels of the host image and (ii) transform domain approach where watermark is embedded by modifying the frequency coefficients. One of the simplest spatial domain watermarking approaches is to modify the least significant bit (LSB) of the image. In [1], a binary watermark is embedded in spatial domain. The host image is divided into variable block size. Depending on the block * Corresponding author. E-mail: chinmay@cemk.ac.in 476

variance, a block may be partition into four sub-blocks like a quad tree partition. When partition is over, the watermark bit is embedded into the blocks by adjusting the pixels intensity with the help of block statistics (block mean, block minimum, block maximum, etc.). During extraction, the host block is compared with the watermarked block. A binary non-blind watermark technique is described in [2]. Here, the watermark is divided into four different parts and each part is embedded into different regions of the host image. During embedding, a pixel intensity of a block (of the selected region) is incremented (or decremented) by amount α (embedding strength) depending on the watermark 1 (or 0). In the extraction process, watermarked block is compared with host block. Recently, in [3] [4] BTC-PF based watermark methods have been proposed. In [3], quantization levels of the block are used to embed the watermark bit. In [4], two different patternbooks are used to encode each block and depending on the bit value of the watermark, the patternbook is selected to encode the block. Transform domain watermarking techniques like those based on the discrete cosine transform (DCT) [5] [6]. In [5], watermark of length n is embedded into n largest AC coefficients. The watermark is a sequence of real numbers chosen independently from N (0, 1). The proposed method is a non-blind as host image is required during extraction. Liang et al. [6] proposed a watermarking scheme in which watermark is embedded in the middle frequency band. In [7], blind watermark technique is proposed where DC coefficients are modified to embed the watermark. The embedding strength α depends on the texture of the block. Discrete wavelet transform (DWT) typically provides higher image imperceptibility and is much more robust in image processing. DWT has been used more frequently in digital image watermarking [8] due to its time/frequency decomposition characteristics, which resemble to the theoretical models of the human visual system. In [9], a binary watermark is embedded by modulating the mean of a set of wavelet coefficients. The watermark bit is embedded into both low and high frequency components, i.e., watermarks are embedded in two different frequency components. In [10], the watermark is embedded at the large coefficients of the high and middle frequency bands of the DWT. Ganic et al. [11] proposed a non blind watermark technique to embed a grayscale watermark. The host image is decomposed by DWT which gives 4 subbands: LL, HL, LH and HH. The watermark is embedded into each subband. Each subband and grayscale watermark is decomposed by singular value decomposition (SVD). The watermark singular values are linearly combined with the subband singular values. To extract the watermark, both the host image and original watermark are needed. The proposed methods are: Block-based, Non Block-based and Block-based with shifting. The host image is decomposed by haar wavelet upto 3-levels and the watermark is embedded by modifying all middle frequency subband. Reddy et al. [12] proposed a DWT based scheme in which a grayscale image is embedded into the host image. To improve the performance of DWT based watermarking scheme, some researchers have proposed hybrid schemes in which they combine DWT with DCT or singular valued decomposition (SVD) for embedding the watermark [13]. In this work, a hybrid combination of DWT and BTC-PF method has been proposed to embed a grayscale watermark. Here, the host image is passes through wavelet transform and the watermark is embedded in a middle frequency subband. In the embedding process, both the subband of the host image and watermark are encoded by BTC-PF method [14]. BTC-PF is a block based lossy image coding technique, which returns an index (of best fit pattern) and quantization levels. To enhance the security, the encoded information of the watermark is randomly permuted. The quantization levels of the subband are linearly modified using the quantization levels of the watermark. Finally, watermarked image is constructed using inverse wavelet transform. The experimental results established the robustness of the proposed watermarking technique against different attacks. This paper is organized as follows. In section 2, the wavelet decomposition of the host image and selection of subband is presented. The BTC-PF method is described in section 3. The proposed watermarking technique (both embedding and extraction) is presented in section 4. The experimental results are reported in section 5. Finally, conclusion is drawn in section 6. 2. Wavelet Decomposition Fig. 1: Two level wavelet decomposition of the host image, the selected subband HL LL is marked with bold line The DWT has been used more frequently in digital image watermarking due to its time/frequency decomposition characteristics, which resemble to the theoretical models of the human visual system. 2D wavelet transforms decompose an image into subband as shown in Fig. 1. Many watermarking schemes [9] choose low frequency subband for embedding watermark because it shows more robust to different attacks that have low-pass character such as mean, median filter and lossy compression. But, embedding watermark in low frequency may degrade the 477

image quality significantly. On the other hand, some watermarking schemes choose the high frequency subband to embed the watermark. Because, high frequency subband includes the edges and textures of the image and the human eyes are not sensitive to changes in this subband. Watermarks embedded into high frequency subband are less robust to low-pass filter and lossy compression. From the analysis, it is obvious that the advantages and disadvantages of low and high frequency subband [9] are complementing each other. Here, we propose a scheme to embed the watermark in the middle frequency subband where acceptable performance of imperceptibility and robustness could be achieved. In the present, method the middle frequency subband(hl) is further decomposed and the watermark is embedded in the HL LL subband, shown in Fig. 1. 3. BTC-PF Method The BTC-PF [14] is a lossy spatial domain based compression method that uses a Q-level quantizer to quantize a local region of the image. This method encompasses a hybrid combination of BTC and VQ method. In this method, to encode an image, it is divided into blocks of size n n (n = 2 k ) and each block is quantized by Q different quantization levels. The quantization is executed using Q-level patterns of size n n. The patterns are stored as a patternbook of size M, say. The quantization levels are determined respect to the best fit pattern. The method of selection of the best fit pattern for block B i is as follows. 11 53 110 117 0 1 1 1 10 64 139 147 0 1 1 1 10 63 134 141 0 1 1 1 09 63 132 137 0 1 1 1 (a) (b) 1 0 1 1 0 1 1 1 1 0 1 1 1 0 1 1 0 1 1 1 1 0 1 1 0 1 1 1 0 1 1 1 (c) (d) 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 (e) (f) 1 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 1 1 1 1 1 0 1 0 1 1 1 1 1 (g) (h) 0 0 0 0 10 108 108 108 1 1 0 0 10 108 108 108 0 0 1 1 10 108 108 108 1 1 1 1 10 108 108 108 (i) (j) Fig. 2: An illustration of BTC-PF method. (a) Image block, (b)-(i) the set of binary patterns { P 1, P 2, P 3,..., P 8 }, (j) Reconstructed block by fitting P 1 Let the pixel coordinates of the image block B i are x j (j = 1, 2,..., n 2 ) and corresponding pixel intensity is f(x j ). Let the patterns in the patternbook are P t (t = 0, 1,..., M 1) and the value at position x j of P t is r, i.e., P t (x j ) = r where 0 r Q 1. To select the best fit pattern, each pattern is fit to the block B i and error is computed for this. The error between the block B i and the pattern P t is defined as Q 1 e t = e tr r = 0 where e tr = m Q 1 ( P t ( x j ) = r = 1 2 f ( x j ) m r ) Q 1 f ( x j ) and r n r P t ( x j ) = r Where, n r is the number of positions with level r in the pattern P t. The pattern with minimum error is considered as the best fit pattern and the index of the best fit pattern is P i = arg{min{et} t=0, 1,..., M 1}. The image block B i can be decoded using P i and corresponding means m ir (r = 0, 1,..., Q 1), i.e., the tuple t i = (P i, m i0, m i1,..., m i(q-1) ) can be used to reconstruct B i as B i. The pattern selection processes is described with example shown in Fig. 2. Let us consider an image block of size 4 4 (i.e., n=4) shown in Fig. 2(a) and eight binary patterns, P 1, P 2,..., P 8 (i.e., Q=2 and M=8) shown in Fig. 2(b)-(i). These patterns are used to encode the given image block. As a result of fitting the first pattern (P 1, Fig. 2(b)), the mean values are m 0 =10.00 and m 1 =108.3333, respectively and corresponding errors e 10 478

and e 11 are 2 and 14759 respectively. The total error, e 1 =14761. Similarly, the total errors due to the other patterns {P 2, P 3,..., P 8 } are e 2 =30569, e 3 =31988, e 4 =41833, e 5 =43757, e 6 =43765, e 7 =43679, e 8 =43669, respectively. Thus, the best pattern to fit the given image block is P 1, which gives the minimum error 14761. The quantization levels are m 0 =10.00 and m 1 =108.3333. The reconstructed block is shown in Fig. 2(j). 4. Proposed Method Here, we describe a hybrid watermarking scheme based on DWT and BTC-PF. The embedding and extraction process are described in the following subsections. 4.1 Embedding Method In the present method, a grayscale image is embedded inside another grayscale image. The block diagram of the proposed embedding method has been shown in Fig. 3. The host image (H) is decomposed by using wavelet decomposition. This gives a number of subband {S 1 S 2,..., S 3p+1 }. Here, we select a middle frequency subband S i, same size as the watermark, where watermark will be embedded. The subband S i and the watermark image (W) are encoded by the BTC-PF method. To encode a block, the BTC-PF method returns {P i, m i0, m i1,..., m i(q-1) } (see section 3), i.e., the encoded information for an image can be considered as the collection of sets like P, M 0, M 1,..., M (Q-1). Let us denote this collection of the subband of the host image as P h, M h 0, M h 1,..., M h (Q-1) and for watermark as P w, M w 0, M w 1,..., M w (Q-1). The watermarked image is reconstructed using P w (in place of P h ) and a combination of the quantization levels of the subband and watermark image. To enhance the security level the encoded data of the watermark is randomly permuted using Π, i.e., P w = Π(P w ), M w 0= Π(M w 0), M w 1,=Π (M w 1),..., M w (Q-1)= Π(M w (Q-1)). At this point, quantization levels are linearly combined, which results a new set of quantization levels, say, M h i, for i = 0 to Q 1. The actual watermarked image is reconstructed using P w, M h 0, M h 1,..., M h (Q-1). The algorithmic structure of the embedding process is presented below. Algorithm: Watermark embedding 4. Use the key K and generate a random permutation Π. 5. Permute encoded data of W as P w =Π(P w ), M w 0,= Π(M w 0), M w 1= Π(M w 1),..., M w (Q-1)= Π(M w (Q-1)). 6. The quantization levels are combined as M h i=(1-γ) M h i + γ, M w i, γ (0, 1)...(1) 7. Apply BTC-PF method to decode S i as S i with help of P w, M h i, M h i,..., M h (Q-1). 8. The inverse wavelet transform is applied on { S 1 S 2,..., S i-1, S i, S i+1,..., S 3p+1 }, and this returns the desired watermarked image H. 4.2 Extraction Method An important aspect of any watermarking scheme is its robustness against different types of attack. It is obvious that the unauthorized owners try to remove/destroy the watermark from the watermarked image so that the actual owner fails to prove the ownership of the image. To prove the ownership of the image, watermark must be extracted from the watermarked image even it is manipulated by watermarked image ( H ) (we also denote the watermarked image, H, as H ). In the present method, the original host image (H) is also required during extraction of the watermark, i.e., the proposed method is non-blind. The extraction process is shown in Fig. 4. The extraction process is described below. Fig. 3: Block diagram of the watermark embedding process Input: Host image (H), Watermark image (W), Secret Key K, and γ (0, 1) Output: Watermarked Image (H ) 1. The host image H is decomposed into a number subband { S 1 S 2,..., S 3p+1 }, using 2D wavelet. 2. The selected subband S i (say), is encoded by BTC- PF method which returns P h, M h 0, M h 1,..., M h (Q-1). 3. The watermark image W is also encoded by BTC- PF which gives P w, M w 0, M w 1,..., M w (Q-1). Algorithm: Watermark extraction Input: Attacked image ( H ), Host image ( H ), Secret Key K, and γ Output: Extracted watermark (W ) 1. Both H and H are decomposed by wavelet transform. 479

2. The selected subband S i and S i are encoded by BTC-PF method which returns P h, M h 0, M h 1,..., M h (Q-1) and P h, M h 0, M h 1,..., M h (Q-1), respectively. 3. Combine the quantization levels using inverse of the equn. (1) M w i= (M h i - (1-γ) M h i ) / γ...(2) 4. Using the key, K, compute inverse permutation Π (i.e., Π = Π 1 ). Permute P h, M w 0, M w 1,..., M w (Q-1) using Π, which gives p P h = Π (P h ), pm w 0= Π (M w 0), p M w 1= Π (M w 1),..., p M w (Q-1)= Π (M w (Q-1)). 5. The extracted watermark image (W ) is constructed by applying BTC-PF on p P h, p M w 0, p M w 1,...,., pm w (Q-1). (b) (c) (d) Fig. 5: An illustration of embedding process. (a) Host image (H), (b) HL LL subband obtained from H after 2-level wavelet decomposition, (c) watermark image (W), (d) reconstructed subband and (e) watermarked image (H ) Fig.4: Block diagram of the watermark extraction process (a) The embedding process is illustrated in Fig. 5. Let us consider a host image of size 16 16 shown in Fig. 5(a). The host image is passed through 2-level wavelet decomposition and the selected subband of size 4 4 is shown in Fig. 5(b). The watermark image (grayscale) of size 4 4 is given in Fig. 5(c). Both, the selected subband and watermark image are encoded by BTC-PF using the patterns {P 1, P 2,..., P 8 } shown in Fig. 2(b)-(i). For subband image, BTC-PF returns P 5, with quantization levels m 0 h =1.1875, m 1 h =0.3125 and for watermark image, P 1, with quantization levels m 0 w =10.00, m 1 w =108.3333. The quantization levels m 0 h =1.1875, m 1 h =0.3125, m 0 w =10.00, and m 1 w =108.3333 are combined using equation 1, with γ equals to 0.1. The subband is reconstructed using the pattern P 1, combined quantization levels m 0 h =2.0688, m 1 h =11.1146. Finally, the watermarked image is obtained by applying inverse wavelet transform, shown in Fig. 5(e). To illustrate the extraction process, we consider the host image of size 16 16 shown in Fig. 4(a) and the watermarked image of size 16 16 shown in Fig. 4(e). Both, the host image and the watermarked image are passed 480

through 2-level wavelet decomposition and the selected subbands of size 4 4 are encoded by BTC-PF method using the patterns {P 1, P 2,..., P 8 } shown in Fig. 2(b)-(i). For the subband of the host image, BTC-PF returns the pattern P 5 with quantization levels m 0 h =1.1875, m 1 h =0.3125. For the subband of the watermarked image, BTC-PF returns the pattern P 1 with quantization levels m 0 h = 3.2500, m 1 h = 11.2500. The quantization levels of both the subbands are then combined using equation 2. The combined quantization levels are m 0 w = 21.8125, m 1 w = 109.6875. Finally, the watermark is reconstructed using the pattern P 1 and the quantization levels m 0 w = 21.8125 and m 1 w = 109.6875. 5. Experimental Results In this experiment, we have used grayscale images Airplane, Lena, Splash and Zelda as the host image of size 512 512, shown in Fig. 6(a)-(d). The original watermark image (grayscale) of size 128 128 and the permuted one are shown in Fig. 7(a) and 7(b) respectively. For implementation purpose, we have used MATLAB 7.8.0.347. The quality and robustness of the proposed method depends on the control parameter γ. When, the value of γ is greater, then more priority on the watermark (see equ. (1)), which results poor quality images. Again, for small value of γ, output quality is good but under different attacks it is very hard to extract the watermark. The value of γ is set to 0.1, we set this value experimentally. Due to the insertion of the watermark, the host images will not remain same as the original one, their quality will be degraded. Under different attacks, the quality of the extracted watermark will also be degraded. In the present work, to measure the quality of images we have relied on error based function, PSNR, and structure based metric, SSIM [15]. In Fig. 8, the watermarked images with PSNR and SSIM values are shown. The PSNR values are above 31 db and SSIM values are very close to 1.0, we can conclude that watermarked images are good enough for general applications. The error function is given below. 2 255 PSNR = 10 log 10 ( ) MSE And 1 2 MSE = [ f O ( i, j ) f W ( i, j )] M N Where M is height and N is width of the image, f O (i, j) and f W (i, j) are the pixel intensity values at (i, j) th position of host and watermarked image respectively. We evaluate the robustness of the proposed method under different attacks like blurred (with disk radius 0.6), motion blurred (with an angle of degree 9), salt and pepper noise (with density 0.005), Gaussian filter (3 3, σ=0.5), cropping (middle 9% and 16% of the image set to 255) and JPEG compression using Adobe Photoshop (version 8.0) with varying image quality. (a) (b) (c) (d) Fig. 6: Original host images. (a) Airplane, (b) Lana, (c) Splash and (d) Zelda Fig. 7: Watermark image (grayscale). (a) Original and (b) Permuted (a) Airplane (32.34, 0.9938) (b) Lena (35.92, 0.9993) (c) Splash (35.46, 0.9981) Fig. 8: Watermarked images with (PSNR, SSIM) values (d) Zelda (36.76, 9998) Table 1: PSNR and SSIM values of the extracted watermark from Watermarked images (Airplane, Lena, Splash and Zelda) under different attacks Attacks Airplane Lena Splash Zelda Blurring PSNR 18.05 20.48 20.14 21.11 SSIM 0.5194 0.7069 0.6941 0.7917 Motion PSNR 18.13 23.44 22.29 24.94 Blurred SSIM 0.3903 0.5677 0.5721 0.6628 Salt/pepper PSNR 16.21 16.31 15.90 15.91 noise SSIM 0.3447 0.3309 0.3199 0.3099 Gaussian PSNR 16.97 16.80 16.88 16.46 filter SSIM 0.3166 0.3106 0.2959 0.2858 Cropping PSNR 16.73 17.02 17.12 17.02 (9%) SSIM 0.6386 0.6490 0.6460 0.6489 Cropping PSNR 14.85 14.98 14.87 15.23 (16%) SSIM 0.4896 0.5061 0.4965 0.5057 JPEG-4 PSNR 16.32 16.03 15.97 15.84 (QF=4) SSIM 0.3291 0.3384 0.3270 0.3509 JPEG-6 PSNR 20.84 21.34 21.14 21.00 (QF=6) SSIM 0.4700 0.5047 0.5002 0.5018 JPEG-8 PSNR 22.39 22.84 22.68 22.66 (QF=8) SSIM 0.5329 0.5675 0.5509 0.5624 JPEG-10 PSNR 30.14 30.51 29.76 30.12 (QF=10) SSIM 0.8175 0.8187 0.8131 0.8170 JPEG-12 (QF=12) (a) (b) PSNR 34.02 35.15 33.58 34.90 SSIM 0.9118 0.9204 0.9142 0.9211 (a) Blurred 18.05, 0.5194 (b) M. Blurred 18.13, 0.3903 (c)salt/pepper 16.21, 0.3447 (d) G. filter 16.97, 0.3166 481

(e) Crop (9%) 16.73, 0.6386 (f) Crop (16%) 14.85, 0.4896 (c)salt/pepper 15.90, 0.3199 (d) G. filter 16.88, 0.2959 (g) JPEG-4 16.32, 0.3291 (h) JPEG-6 20.84, 0.4700 (e) Crop (9%) 17.12, 0.6460 (f) Crop (16%) 14.87, 0.4965 (i) JPEG-8 22.39, 0.5329 (j) JPEG-10 30.14, 0.8175 (g) JPEG-4 15.97, 0.3270 (h) JPEG-6 21.14, 0.5002 Fig.9: Extracted watermark with (PSNR, SSIM) from the watermarked Airplane under different attacks (i) JPEG-8 22.68, 0.5509 (j) JPEG-10 29.76, 0.8131 (a) Blurred 20.48, 0.7069 (b) M. Blurred 23.44, 0.5677 Fig.11: Extracted watermark with (PSNR, SSIM) from the watermarked Splash under different attacks (c)salt/pepper 16.31, 0.3309 (d) G. filter 16.80, 0.3106 (a) Blurred 21.11, 0.7917 (b) M. Blurred 24.94, 0.6628 (e) Crop (9%) 17.02, 0.6490 (f) Crop (16%) 14.98, 0.5061 (c)salt/pepper 15.91, 0.3099 (d) G. filter 16.46, 0.2858 (g) JPEG-4 16.03, 0.3384 (h) JPEG-6 21.34, 0.5047 (e) Crop (9%) 17.02, 0.6490 (f) Crop (16%) 15.23, 0.5057 (i) JPEG-8 22.84, 0.5675 (j) JPEG-10 30.51, 0.8187 (g) JPEG-4 15.84, 0.3509 (h) JPEG-6 21.00, 0.5018 Fig.10: Extracted watermark with (PSNR, SSIM) from the watermarked Lena under different attacks (i) JPEG-8 22.66, 0.5624 (j) JPEG-10 30.12, 0.8170 (a) Blurred 20.14, 0.6941 (b) M. Blurred 22.29, 0.5721 Fig.12: Extracted watermark with (PSNR, SSIM) from the watermarked Zelda under different attack 482

Different quality factors (QF) are 4, 6, 8, 10 and 12 (in the range of values 0 to 12). The Table 1 and Fig. 9-12 show that the quality (visual and structural) of the extracted watermark is good enough to establish the ownership and our method is robust against different attacks. 6. Conclusions In this paper, we have proposed a digital image watermarking technique based on DWT and BTC-PF method. Here, a grayscale watermark has been embedded into the host image. Experimental results show that the imperceptibility of the watermarked image is acceptable and the proposed technique is also robust against different attacks. We have used a secret key to scramble the watermark for the enhancement of security level. The present method is non-blind one, and our future target is to extend the method as a blind. This method can also be extended to embed a colour watermark. References [1] S. Kimpan, A. Lasakul, and S. Chitwong, Variable block size based Adaptive watermarking in spatial domain, in Proc. of the IEEE International Symposium on Communications and Information Technologies, pp. 374 377, 2004. [2] I. Nasir, Y. Weng, J. Jiang, and S. Ipson, Multiple spatial watermarking technique in color images, Signal, Image and Video Processing, vol. 4, pp. 145 154, 2010. [3] C. Maiti and B. C. Dhara, A binary watermarking scheme using quantization levels of btc-pf method, in Proc. of IEEE International Conference on Communications, Devices and Intelligent Systems, pp. 604-607, 2012. [4] C. Maiti and B. C. Dhara, A robust binary watermarking scheme Using btc-pf technique, in Proc. of International Conference on Ecofriendly Computing and Communication Systems, pp. 178 185, Springer, 2012. [5] I. J. Cox, J. Kilian, T. Leighton, and T. Shamoon, Secure spread spectrum watermarking for multimedia, IEEE Trans. on Image Processing, vol. 6, pp. 1673 1687, 1997. [6] T. Liang and F. Zhi-jun, An adaptive middle frequency embedded digital watermark algorithm based on the dct domain, Pattern Recognition, vol. 40, pp. 2408 2417, 2008. [7] F. Deng and B. Wang, A novel technique for robust image watermarking in the dct domain, in Proc. of the IEEE Int. Conf. on Neural Networks and Signal Processing, pp. 1525 1528, 2003. [8] C. C. Lai and C. C. Tsai, Digital image watermarking using discrete wavelet transform and singular value decomposition, IEEE Trans. on Instrumentation and Measurement, vol. 59, no. 11, pp. 3060 3063, 2010. [9] L. H. Chen and J. J. Lin, Mean quantization based image watermarking, Journal of Image and Vision Computing, vol. 21, pp. 717 727, 2003. [10] M. Hsieh, D. Tseng, and Y. Huang, Hiding digital watermarks using Multiresolution wavelet transform, IEEE Trans. On Industrial Electronics, vol. 48, no. 5, pp. 875 882, 2001. [11] E. Ganic and A. M. Eskicioglu, Robust dwt-svd domain image watermarking: Embedding data in all frequencies, in Proc. Workshop Multimedia Security, Magdeburg, Germany, pp. 166 174, 2004. [12] A. Reddy and B. Chatterji, A new wavelet based logo-watermarking scheme, Pattern Recognition Letters, vol. 26, no. 7, pp. 1019 1027, 2005. [13] C. Song, S. Sudirman, and M. Merabti, A robust region-adaptive dual image watermarking technique, Journal of Visual Communication & Image Representation, vol. 23, pp. 549 568, 2012. [14] B. C. Dhara and B. Chanda, A fast progressive image transmission Scheme using block trunction coding by pattern fitting, Journal of Visual Communication & Image Representation, vol. 23, pp. 313 322, 2012. [15] Z. Wang, A. C. Bovik, H. Sheikh, and E. P. Simoncelli, Image Quality assessment: From error visibility to structural similarity, IEEE Trans.on Image Processing, vol. 13, no. 4, pp. 600 612, 2004. 483