An Improved Blind Watermarking Method in Frequency Domain for Image Authentication Md. Iqbal H. Sarker M. I. Khan Department of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong-4349, Bangladesh E-mail: [email protected] Department of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong-4349, Bangladesh E-mail: [email protected] Abstract—Digital watermarking has been widely used for copyright protection for multimedia data. This paper proposes a new watermarking method of image for copyright protection based on Hadamard transform. This method can embed or hide an entire image or pattern as a watermark such as a company’s logo or trademark directly into original image for copyright protection. This watermarking method deals with the extraction of the watermark information in the absence of original image, hence the blind scheme is obtained. The experimental results prove that our proposed watermarking method offers better image quality and more robustness under various attacks such as JPEG compression, cropping, sharpening, filtering and so on. Peak Signal to Noise Ratio (PSNR) and Normalized Correlation Coefficient (NCC) are computed to measure image quality and robustness. Finally, a comparative study is made against the previous technique. Keywords—Digital image watermarking; Best first search algorithm; Hadamard transform; Arnold Transform. I. INTRODUCTION Digital watermarking is a technique that embeds additional information called watermark into a multimedia object in order to secure it. A watermark is a hidden signal added to images that can be detected or extracted later to make some affirmation about the host image. When digital media is flourishing with notable advancement in recent years, the distribution of unauthorized copies of media content is also increasing day by day. Because of easy access to digital content the online purchasing and distribution become easier. Thus it is an urgent need to provide protection for digital content. One solution that gaining popularity in protecting the digital content is digital watermarking. A robust watermark withstands malicious attacks; such as scaling, rotation, filtering and compression. This kind of watermarking is used for copyright protection. Fragile watermark can detect any unauthorized modification in an image and therefore they are quite suitable for an authentication purpose. Watermarking can be classified in several ways. According to the need of original image for watermark extraction or detection, watermarking is classified to non-blind and blind watermarking techniques. In non-blind technique, it requires the original image when detecting the watermark whereas in blind technique no need the original image. There are different approaches of digital image watermarking like as spatial-domain and frequency domain technique. In frequency domain, the watermark is embedded by changing the frequency coefficients. The most used transforms are DWT, DCT, DFT and Hadamard Transformation (HT). In spatial domain, the watermark is embedded directly by modifying the intensity values of pixels. Spatial domain watermarking technique is easier and its computing speed is high than transform domain watermarking. But the disadvantage is that it is not robust against common image processing operations. In this paper, we propose a robust watermarking method based on Hadamard Transform. The simplicity of Hadamard transform offers a significant advantage in shorter processing time and ease of hardware implementation than most orthogonal transform techniques such as DWT and DCT. II. RELATED WORK Anthony T.S.Ho proposed a watermarking system for digital image in frequency domain using Fast Hadamard Transform [1]. Saeid Saryazdi and Hossein Nezamabadi-pour developed a new blind gray-level watermarking scheme. Hence the host image is first divided into 4×4 non-overlapping blocks. For each block, two first AC coefficients of its Hadamard transform are then estimated using DC coefficients of its neighbor blocks [2]. K. Deb, M.S. Al-Seraj, M.M. Hoque, M.I.H. Sarker proposed a combined DWT and DCT based watermarking technique with low frequency watermarking with weighted correction [3]. F. Hosain presented a survey of various digital watermarking techniques for multimedia data such as text, audio, image and video for copyright protection [4]. Ali Al-Haj described an imperceptible and a robust combined DWT-DCT digital image watermarking algorithm for copyright protection [5]. Zhao Rui-mei, Lian Hua, Pang Hua-wei, Hu Bo-ning proposed a blind watermarking algorithm based on DCT for digital image in which a 2-bit image embeds in a 8-bit gray image [6]. R. Kountchev, S. Rubin, M. Milanova,V. Todorov proposed a 978-1-4799-0400-6/13/$31.00 ©2013 IEEE new method where the still digital image is transformed using Complex Hadamard Transform (CHT) [7]. S.J. Lee, S. H. Jung proposed a survey of watermarking systems which are applied to Multimedia [8]. Dr. M. Sathik and S. S. Sujatha described the watermark construction process where scrambled version of watermark is obtained with the help of Arnold transform [9]. III. HADAMARD TRANSFORM The Hadamard transform (also known as the Walsh– Hadamard transform, Walsh transform or Walsh–Fourier transform) is an example of a generalized class of Fourier transforms. The Hadamard matrix H m represents a M × M m Hadamard matrix, where M = 2 , m = 1, 2, 3… with element values either +1 or -1, the rows and columns of H m are orthogonal. Now a Hadamard matrix is given by [1]: ⎡ H m−1 Hm = ⎢ ⎣⎢ H m−1 H m−1 − H m−1 ⎤ ⎥ ⎦⎥ Where, m > 0 . Now for m=1, M=2. Hence matrix is given by: ⎡1 H =⎢ ⎣⎢1 (1) 2 × 2 Hadamard 1 ⎤ ⎥ − 1⎦⎥ A digital image can be considered as a two unit function f(x,y) in the plane Z. It can be represented as Z = f(x,y) where x,y ∈ {0,1,2,3....N-1} . Hence, N represents order of digital image. The matrix of image can be changed into a new matrix by using the Arnold transform which results in a scrambled version to offer better security. It is a mapping function which changes a point (x,y) to another point ( x ′ , y ′ ) by using the equation. x ′ = (x + y) mod N y ′ = (x + 2y) mod N V. PROPOSED WATERMARKING METHOD In this section, we describe our proposed method of watermarking. This watermarking method consists of two process; watermark embedding process and watermark extraction process. A. Embedding Process The proposed watermark embedding process is shown in Figure 1. To embed the watermark into the host image following steps are required. Watermark image Original image (2) Let [O ] represents the original image and [T ] the transformed image, the 2D-Hadamard transform is given by [1]: H [O ]H m [T ] = m M IV. ARNOLD TRANSFORM Apply Arnold transform Divide the image into 8x8 sub-blocks Divide the image into 8x8 sub-blocks Apply Hadamard transform Apply Hadamard transform (3) Apply Best-first-search algorithm The inverse 2-D Hadamard transformation (IHT) is given as: [O] = H −1 m [T ]H * m = H m [T ]H m M (4) In our algorithm the processing is performed on 8 × 8 sub-blocks of whole image, so we use the third order Hadamard matrix (H3) and H 3 becomes: ⎡1 ⎢1 − ⎢ ⎢1 ⎢ 1− H = ⎢ ⎢1 ⎢ ⎢1 − ⎢1 ⎢ ⎢⎣1 − 1 1 1⎤ ⎥ 1 1−1 1−1 1 −1 ⎥ 1−1−1 1 1−1 −1⎥ ⎥ 1−1 1 1−1−1 1 ⎥ 1 1 1−1−1−1 −1⎥ ⎥ 1 1−1−1 1−1 1⎥ 1−1−1−1−1 1 1⎥ ⎥ 1 − 1 1 − 1 1 1 − 1 ⎥⎦ 1 1 1 Co-efficient within range: 0<x<1 & -1<x< 0 Select highest value of longest increasing sequence Watermark embed equation f ( x, y) = α ∗ w(i, j ) Visited matrix Key matrix 1 Apply inverse Hadamard transform (5) Watermarked image Fig.1 Flow Diagram for Watermark Embedding Process 1) Divide the Host Image: The host image is divided into 8 × 8 non-overlapping blocks. 2) Apply Hadamard Transform: Hadamrd transformation is performed on each sub-blocks of the host image. For each sub-block, 64 Hadamard transform co-efficients are obtained. Each block has a DC value at upper-left corner which is avoided for embedding. So we have 63 coefficients left for embedding. 3) Apply Best-first-Search Algorithm: Now the best-firstsearch algorithm is applied to find the increasing sequence of each block to embed watermark. In figure 2, C is the main point from which we start searching and 8, 5, 6 are the order of searching the coefficients and we get some increasing sequences. From these sequences efficient point is selected by using a priority queue. 1 2 3 4 5 6 7 8 CC applied to all matrixes using the equation (4) to produce the watermarked image. B. Extraction Process The proposed watermark extraction process is shown in figure 3. To extract the watermark image from watermarked image following steps are required. 1) Apply Hadamard Transform: The watermarked image is divided into 8*8 sub-blocks and hadamard transformation is applied in each block. Watermarked image Divide the watermarked image into 8x8 sub-blocks Apply Hadamard transform on each subblock Extract the watermark point using visited matrix Fig. 2 Order of Searching 4) Selection of Longest Increasing Sequence: From these increasing sequences, longest sequence is selected. The highest value of this longest increasing sequence is used for embedding in our proposed method. 5) Perform Arnold Transform: Arnold transform is performed to scramble the elements of watermark image and obtain new matrix M. Then M is divided into 8 × 8 nonoverlapping sub-blocks. 6) Apply Hadamard transform: Hadamard transform is also applied on each sub-block of watermark image. From Hadamard transform coefficients of watermark image, points are selected within the range: 0<x<1 or -1 <x<0 and stored in a visited matrix. The rest of the components stored in a key matrix which is used in extraction process. Watermark extraction equation: w′(i, j ) = f ′( x, y) α Store the co-efficient on new matrix Divide the matrix into 8x8 blocks Apply inverse Hadamard transform on each block Perform Anti-Arnold Transform 7) Apply Embedding Equtation: Then we have to use the following equation for embedding process. Extract watermark image f ( x, y) = α ∗ w(i, j ) (6) Where w(i, j ) denotes watermark Hadamard co-efficient, f ( x, y ) denotes watermarked Hadamard co-efficient and α denotes the scaling factor, which defines the strength of the watermark. 8) Apply Inverse Hadamard Transform: After performing insertion in all sub-blocks the inverse Hadamard transform is Fig.3 Flow Diagram of Watermark Extraction Process 2) Apply Extraction Equtation: The watermark image is extracted using the visited matrix. Here we use the inverse equation of (6) for extraction of watermark image. w′(i, j ) = f ′( x, y) α (7) Where, f ′( x, y ) denotes the watermarked co-efficient and w′(i, j ) denotes the extracted watermark co-efficient. The watermark Hadamard coefficients are extracted from all the sub-blocks of watermarked image and stored it in a new matrix along with key matrix. 3) Apply Inverse Hadamard Transform: Divide the new matrix into 8*8 block and inverse Hadamard transform is applied. Thus we obtain the extracted scrambling watermark image 4) Perform Anti-Arnold Transform: Anti Arnold transform is used to get the original watermark image from scrambling image. VI. EXPERIMENTAL RESULTS AND ANALYSIS The peak signal to noise ratio (PSNR) is used to evaluate the quality of the watermarked image in comparison with host image. The PSNR formula is as follows: PSNR = 10 log10 Where MSE = 255 × 255 MSE TABLE I. PSNR AND NCC FOR NO ATTACK Image Name Lena For No Attack PSNR NCC 38.78 1 TABLE II. PERFORMANCE COMPARISON Attack NCC Anthony’s Method [1] Proposed Method Sharpening 3x3 0.9573 0.9933 1 row 1 column removed Frequency mode laplacian removal 0.9866 0.9883 0.9580 0.9754 Scaling 7.5 0.9354 0.9554 JPEG compression Of factor 30 Changing aspect ratio 0.8688 0.9042 0.8199 0.8558 (8) [I (i, j) − K (i, j)] 1 M −1 ∑ M × N N −1 2 MSE defines mean square error. The M and N are the height and width of the image respectively. I (i, j ) and K (i, j ) are the values located at coordinates (i ,j) of the host image and the watermarked image. (a) (b) (c) (d) After extracting the watermark, the normalized correlation coefficient (NCC) is computed to measure the correctness of an extracted watermark. It is defined as: NCC = 1 m n ∑∑ w(i, j) × w′(i, j) m × n i =1 j =1 (9) Where, m and n are the height and width of the watermark respectively. w(i, j ) and w′(i, j ) are the watermark bits located at coordinates (i, j) of the original watermark and the extracted watermark. Among various test images employed in experiments, the 512 x 512 “Lena” image which is shown in Fig. 4 (a) is used to show the effectiveness of the proposed method. The 64×64 watermark is shown in Fig 4 (b). (e) Fig.5 Different attacks (a) Salt & pepper (b) Cropping (c) Gaussian noise (d) JPEG compression (e) Rotation (a) (b) (c) Fig.4 (a) Host image (b) Watermark image (c) Watermarked image TABLE VII. PSNR AND NCC FOR CROPPING ATTACK TABLE III. PSNR AND NCC AFTER APPLYING JPEG COMPRESSION For cropping For JPEG compression Attack Attack PSNR JPEG compression (QF=30) JPEG compression (QF=60) JPEG compression (QF=90) 33.7029 0.9842 36.3765 0 .9909 38.4525 1 For filtering Attack PSNR Weiner filtering NCC Cropping[32x32] 30.1313 0 .9982 Cropping[64x64] 25.4391 0 .9894 Cropping[128x128] 16.3047 0 .9595 VII. CONCLUSION TABLE IV. PSNR AND NCC AFTER APPLYING FILTERING ATTACKS Median filtering 3 × 3 ) PSNR NCC NCC 34.4961 0 .9873 36.2677 0.9711 TABLE V. PSNR AND NCC AFTER DIFFERENT ROTATION ATTACKS In this paper, we proposed a new digital image watermarking scheme based on hadamard transform which provides a complete procedure that embeds and extracts the watermark information effectively. Our watermark embedding process does not degrade the visual quality of the image. Moreover this authentication process provides some qualities like imperceptibility, robustness and security. The performance of the watermarking scheme is evaluated with common image processing attacks. The experimental results show that our proposed method is efficient and more robust against those malicious attacks. For rotation ATTACK PSNR NCC VIII. REFERENCES [1] Rotation angle=5 24.1601 0.8875 Rotation angle=10 22.5655 0.9915 [2] Rotation angle=20 21.4640 0.9956 [3] TABLE VI. PSNR AND NCC AFTER APPLYING DIFFERENT NOISE [4] For noise Attack PSNR NCC Gaussian Noise (Average=0,density=.002) 26.8266 0.9873 Speckle Noise (density=.01) 26.5200 0.9887 Salt & pepper noise (Strength=.01) 24.7564 0.9856 [5] [6] [7] [8] [9] Anthony T.S.Ho, Jun Shen, Andrew K.K. Chow, J. Woon, “Robust Digital Image-in-Image Watermarking Algorithm Using the Fast Hadamard Transform”, In proceedings of the international symposium on Circuit and system 2003, (ISCAS ’03), IEEE, Vol.3, pp.826-829. Saeid Saryazdi, Hussein Nezamabadi-pour, “A Blind Digital Watermark in Hadamard Domain”, Proceedings of World Academy of Science, Engineering and Technology, Vol.3, 2005. K.Deb, M.S. Al-Seraj, M.M. Hoque, M.I.H. Sarker, “Combined DWTDCT Based Digital Image Watermarking Technique for Copyright Protection”,IEEE International Conference on Electrical & Computer Engineering (ICECE), 2012. F. Husain “A Survey of Digital Watermarking Techniques for Multimedia Data”, MIT International Journal of Electronics and Communication Engineering, Vol.2,No.1 pp 37-43, 2012. Ali Al-Haj, “Combined DWT-DCT Digital Image Watermarking, “Journal of Computer Science 3 (9): 740-746, 2007. Zhao Rui-mei, Lian Hua, Pang Hua-wei, Hu Bo-ning, “A Watermarking Algorithm by Modifying AC Coefficies in DCT Domain,”International Symposium on Information Science and Engineering (ISISE), IEEE, 2008. R.Kountchev, S.Rubin, M.Milanova,V.Todorov, “Resistant Image Watermarking in the Phases of the Complex Hadamard Transform Coefficients”, IEEE International Conferecne on Information Reuse and Integration (IRI), pp 159 – 164, 2010. S.J. Lee, S. H. Jung “A Survey of Watermarking Techniques Applied to Multimedia”, IEEE Transactions on Industrial Electronics, Vol. 12, pp 272-277, 2001. Dr. M. Sathik and S. S. Sujatha “An Improved Invisible Watermarking Technique for Image Authentication”, International Journal of Advanced Science and Technology(IJAST), Vol. 24, 2010.
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