A robust blind image watermarking based on Double Haar Wavelet Transform (DHWT)

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1 324 Journal of Scientific & Industrial Research J SCI IND RES VOL 7 MAY 202 Vol. 7, May 202, pp A robust blind image watermarking based on Double Haar Wavelet Transform (DHWT) S Maheswari * and K Rameshwaran 2 Department of EEE, Kongu Engineering College, Perundurai, Erode , India 2 MAM Group of Institutions, Siruganur, Tiruchirappalli 62 05, India Received 8 November 20; revised 9 March 202; accepted 2 March 202 This study proposes a blind watermarking algorithm based on Double Haar Wavelet Transform (DHWT) for copyright protection of digital images. Watermark embedding is performed in wavelet transform domain. DHWT is applied to the original cover image and binary watermark. Singular value decomposition (SVD) is applied on selected subband of cover image and binary watermark. Eigen values of selected subband of cover image are replaced by Eigen values of selected subband of binary watermark, which are multiplied with an appropriate strength factor. Proposed algorithm achieved very high imperceptibility, evidenced by high PSNR value for gray scale images, and produced very high robustness against image processing attacks. Keywords: Digital watermarking, Double Haar Wavelet Transform (DHWT), M-channel filter bank, Singular Value Decomposition (SVD) Introduction For multimedia copyright protection, the most promising solution seems to be the watermarking process, where original data is marked with ownership information, hidden in an imperceptible manner in the original signal. Hiding the ownership information can be done in two ways [Spatial domain technique (SDT) and Transform domain technique (TDT)]. In SDT -3, pixel value is modified directly to embed the secret information. In TDT, original image is transformed into transform coefficients by using transforms like DCT 4, DFT 5 and DWT 6-2 etc. Then, transform coefficients are modified to embed the secret information. Wavelet Transform (WT) is a very popular technique, especially in watermarking of images. Byun et al proposed a watermarking method using quantization and statistical characteristics of WT. Wang et al 2 proposed a wavelet tree based Blind Watermarking (BW) scheme. Jiang et al 3 proposed a BW scheme based on 4-band WT. Yuan et al 4 proposed an integer wavelet based multiple logowatermarking scheme. Mahmood et al 5 proposed a semi BW scheme using image denoiseing based on DWT. Li et al 6 proposed wavelet tree quantization based watermarking scheme robust to geometric attacks *Author for correspondence maheswari_bsb@yahoo.com (rotation, scaling and cropping). Lein & lin 7 proposed a BW scheme using wavelet trees quantization. Wei et al 8 proposed a BW algorithm based on the significant difference of wavelet coefficient quantization. Papakostas et al 9 proposed a watermarking algorithm based on Krawtchouk moments, in terms of locality. Feng et al 20 proposed zero-bit watermarking technique, where watermark is generated by the features of cover image and makes host image without any distortion. Xinge et al 2 proposed a BW method by using non-tensor product of wavelet filter banks. MultiWavelet (MW) provides superior performance for image processing applications, compared with scalar wavelets 22. Haar wavelet transform consistently outperform the more complex ones when using noncolored watermark 0. Haar Wavelet based M-channel Filter bank (HWF) with M=3 is called Double Haar Wavelet Transform (DHWT) 23, which divides original image into 9 subimages. Binary watermark is embedded in selected subimage. This study proposes a DHWT based BW scheme. Experimental Section M-Channel Filterbank and DHWT MW is developed from multi resolution analysis (MRA). It offers short support, orthogonality, symmetry, and vanishing moments, besides providing better

2 MAHESWARI & RAMESHWARAN: A ROBUST BLIND IMAGE WATERMARKING BASED ON DHWT 325 Fig. 2 First-level DHWT Fig. M-channel filter bank Strength factor DHWT Subband selection Eigen value Watermark Embedding HWT Original cover image DHWT Subband selection Eigen values Inverse SVD & IDHWT Binary reconstruction while preserving length, good performance at boundaries and a high order of approximation. Each MW system is a matrix valued multirate filterbank (Fig. ), which has taps that are (N x N) matrices. A filter bank decomposes a signal into a collection of subsignals, which may be easier to work with than that of the original signal 24. Perfect Reconstruction Quadrature mirror filters are used to split input signal into M subbands, which are decimated by M in signal decomposition. During reconstruction, M subband signals are decoded, interpolated and recombined using synthesis filters. Decomposition and reconstruction filter banks are defined as follows: Fig. 3 Watermark embedding algorithm Watermarked image Similar to two dimensional (2-D) orthogonal WT, DHWT can be extended to 2-D signals. Let be an image of N x N pixels. 2-D discrete DHWT are defined by the following steps: i) In horizontal direction, original image is filtered by filters, and respectively, and three images [ X 00 (m, n), X 0 (m, n) and ] are produced; ii) In vertical direction, three images are filtered by filters H o (z), H (z) and H 2 (z) respectively, to give 9 images (m,n) 0 j 8.]; iii) Down-sampling 9 images [x n oj ) ) 0 2 H = (0 3 G ( 2

3 326 J SCI IND RES VOL 7 MAY 202 Strength factor DHWT Subband selection Eigen values Watermark Extraction Test image Extracted watermark Fig. 4 Watermark extraction algorithm Inverse SVD & IDHWT Eigen values of subband of watermark (a) (b) (c) (d) Fig. 5 a) cover image; b) original watermark; c) watermarked image; and d) extracted watermark with an interval of 3 gives 9 subimages [.]; and iv) Steps i) to iii) can be repeated on subimage [ ] to get the other subimages in next scale. Proposed Scheme Proposed scheme is DHWT based BW scheme. Watermark is extracted by using secret key only. DHWT is applied on both cover image and binary watermark image (size, 33 33). In 2-D DHWT, each level of decomposition produces 9 bands of data. Fig. 2 shows the first level of decomposition. Low pass band can further be decomposed to give second level of decomposition. Proposed watermark embedding scheme (Fig. 3) is as follows: i) DHWT is applied on original cover image and binary watermark W; ii) Eigen values of cover image (s) of subimage and of watermark image (s w ) of subimage are obtained by applying SVD 25,26 ; iii) Then s of subimage are replaced by s w of watermark image after multiplying with proper strength factor a; iv) Inverse SVD is applied on new Eigen values s * in order to obtain watermarked subimage ; and v) Inverse DHWT is applied on image to obtain watermarked. Proposed watermark extraction scheme (Fig. 4) is as follows: i) DHWT is applied on watermarked image ; ii) Subimage is chosen in the first level DHWT, and SVD is applied on the selected subimage to obtain Eigen values s * ; iii) Then Eigen values of watermark are extracted by diving s * with proper strength factor; and iv) Inverse SVD and inverse DHWT are applied to obtain the watermark. Results and Discussion Experiments were performed on different gray scale images (Lena, Cameraman etc.). Peak signal to noise ratio (PSNR, db) could be obtained with no perceptibility problem on watermarked image when using a at 00 (Lena image). Fig. 5 shows cover image, original watermark, watermarked image and extracted watermark. Any watermarking system should be robust against various image processing attacks (average filtering, rotation, median filtering, Salt and Pepper noise, Gaussian noise, speckle noise and so on). It should not be removable by unauthorized users and should not degrade the quality of images. Mean square error (MSE), PSNR and (normalized cross-correlation) are used to estimate the quality of extracted watermark. MSE, PSNR and are defined as 7,9 where MSE is defined as () (2) where M and N are size of images, and are value at (x, y) location of host and watermarked image.

4 MAHESWARI & RAMESHWARAN: A ROBUST BLIND IMAGE WATERMARKING BASED ON DHWT 327 Table PSNR values of watermarked image Image type PSNR Normalized correlation () Moon (500 x 375) Lena (52x52) Rohith (89 x 253) Rose (50 x50) Cameraman (256 x 256) Girl ( x ) Boat (3 x 3) Fruits (3 x 3) Baboon (3 x 3) Circles (300 x320) Circuit (300 x 234) Fig. 6 under various noise conditions a) b) c) d) Fig. 7 under various attacks: a) average filtering; b) median filtering; c) cropping; and d) angles of rotation...(3) where and are pixel value at i,j location of original watermark and recovered watermark pattern respectively. Table shows obtained PSNR values for various gray scale images and of extracted watermark. Robustness Noise Proposed algorithm was tested against four kinds of noise (zero mean Gaussian noise with variance 00, % salt and pepper noise, Poisson and speckle noise), and simulated results of under various noises demonstrate robustness (Fig. 6) of this algorithm to noise.

5 328 J SCI IND RES VOL 7 MAY 202 TABLE 2 Comparison of PSNR value and of extracted watermark of proposed method and existing methods under various attacks Methods PSNR a Median filter Gaussian Histogram Cropping Angle of rotation db 3x3 noise equalization 0.25 b Wang et al Jiang et al Mahmood et al 5 Li et al Lein & Lin Wei et al Yuan et al Papakostas et al 9 Feng et al Proposed method a Byun et al, 4.95; b Xinge et al 2, 0.79 Attacks Watermarking algorithm is also robust to image processing techniques. Experimental results of various gray scale images under filter attacks indicate that this scheme is robust and can resist filter attacks under low pass filter (Fig. 7a) and Median filter (Fig. 7b), besides geometric attacks such as cropping (Fig. 7c) and angles of rotation (Fig. 7d). Comparing proposed watermarking scheme with existing schemes -2 (Table 2), PSNR of proposed watermarked image and robustness of proposed watermark have been observed far better than that of existing methods. Proposed method can effectively resist image processing attacks like Median filtering, histogram equalization, addition of noise and geometric attacks like rotation, cropping and can obtain a higher PSNR of watermarked image. Conclusions A new watermarking algorithm has been proposed based on DHWT. 2D-DHWT divides cover image into 9 sub images. This algorithm embeds Eigen values of mid frequency band of binary watermark to the Eigen values of mid frequency band of cover image after multiplying with an appropriate strength factor. This algorithm guarantees a satisfactory level of robustness against different types of image processing distortions (low pass filtering, Median filtering, rotation, noise addition, histogram equalization and cropping etc.). Thus, proposed algorithm is highly image adaptive. References Nikolaidis N & Pitas I, Robust image watermarking in the spatial domain, Int J Signal Process, 66 (998) Lei-Da & Guo B-L, Localised image watermarking in spatial domain resistant to geometric attacks, Int J Electron Commun, 63 (2009) Lin C-C, Tai W L & Chang C C, Multilevel reversible data hiding based on histogram modification of difference images, Int J Pattern Recogn, 4 (2008) Hernandez J R, Amado M & Perez-Gonazalez F, DCT domain watermarking techniques for still images: Detector performance analysis and a new structure, IEEE Trans Image Process, 9 (2000) Solachidis V & Pitas I, Circularly symmetric watermark embedding in 2-D DFT, IEEE Trans Image process, 0 (200) Wang Y, Doherty J F & Van Dyck R E, A wavelet based watermarking algorithm for ownership verification of digital images, IEEE Trans Image Process, (2002) Temi C, Choomchuay S & Lasakul A, A robust image watermarking using multiresolution analysis of wavelet, in Proc Int Symp on Commun & Inform Technol, vol 2 (Malaysia) 2005, Wang S H & Lin Y-P, Wavelet tree quantization for copyright protection watermarking, IEEE Trans Image Process, 3 (2004) Hameed K, Mumtaz A & Gilani S A M, Digital image watermarking in the wavelet transform domain, J World Acad Sci, Engg & Technol (WASET), 3 (2006), Brannock E, Weeks M & Harrisonm R, The effect of wavelet families on watermarking, J Comput, 4 (2009) Byun K, Lee S & Kim H, A watermarking method using quantization and statistical characteristics of wavelet transform, in Proc IEEE, Parallel and Distributed Computing Applications and technologies (Singapore) 2005, Wang S-H & Lin Y-P, Wavelet tree quantization for copyright protection watermarking, IEEE Trans Image Process, 3 (2004) Jiang M, Xu G & Yuan D, A novel blind watermarking algorithm based on multiband wavelet transform, in Proc IEEE Int Conf on Signal Processing (Istanbul, Turkey) 2004,

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