Berkeley Wavelet Transform Based Image Watermarking

Remya Ravindran.P Computational Engineering and Networking

Amrita Vishwa Vidyapeetham Coimbatore, India.

remyanandanam2007@gmail.com

Soman K P Computational Engineering and Networking

Amrita Vishwa Vidyapeetham Coimbatore, India.

kp_soman@ettimadai.amrita.edu

Abstract— In this paper Berkeley wavelet transform (BWT) based watermarking is described. BWT is a two-dimensional triadic wavelet transform. Inorder to achieve copyright protection, the proposed scheme embeds watermark into host image. The efficiency of the watermarking scheme is supported with the help of experimental results. Keywords—Berkeley Wavelet Transform, Triadic wavelet Transform, Mother Wavelets

I. INTRODUCTION The illegal usage of digital information such as video, image, and sound is increased recently according to easy acquisition and exchange of digital data via the Internet. Therefore, the need of the information protection is strongly required. Through various schemes for the information protection, the intellectual property right and copyright for visual and audio information can be protected. The watermarking is a kind of digital information protection methods. The watermarking process can be considered as a special technique , where a low-energy signal is imperceptibly embedded in another signal. The two signals are related to each other in some way. Here the low-energy signal is called watermark and it depicts some metadata, like security or rights information about the main signal. The main signal in which the watermark is embedded is known as the cover signal, since it covers the watermark. Generally the cover signal may be a still image, audio clip, video sequence or a text document in digital format. The watermark contains useful certifiable information for the owner of the host media, such as producer's name, company logo, etc. The watermark is extracted later to make an assertion about the host media. There are mainly two important properties of a watermark; the first is that the watermark embedding should not alter the quality and visually of the host image and should be perceptually invisible. The second property is that the watermark should be robust to image distortions. The robustness means the watermark should be difficult for an attacker to remove and it should be also robust to common image processing and geometric operations, such as filtering, resizing, cropping and image compression [5, 13].

In this paper, an efficient image watermarking scheme is proposed. The proposed method embeds the watermark signal into the host image after performing the Berkeley Wavelet Transform(BWT) of the host image. This scheme makes use of a binary image as watermark data. For every 9x9 non-overlapping block of the host image, a binary watermark image pixel is embedded. Using the reverse process, the embedded binary watermark is extracted from the watermarked image. With the support of experimental results, the efficiency of the proposed scheme is demonstrated. The remaining sections in this paper are organized as follows. In Section 2,it explains in the Berkeley Wavelet Transform used. The third section discusses the methodology used in the proposed algorithm. The fourth section tabulates and discusses the various results obtained. The final section summarizes the finds and concludes the research work.

II. BERKELEY WAVELET TRANSFORM The Berkeley wavelet transform (BWT), is a two-dimensional triadic wavelet transform. The Berkeley wavelets are localized in space, tuned in spatial frequency and Orientation. Hence it forms a set that is approximately scale invariant. BWT is a complete, orthonormal basis and hence it is inexpensive to compute, manipulate, and invert. These properties of BWT make useful in the situations where computational power or experimental data are limited. BWT consists of four pairs of mother wavelets at four orientations. In each pair, one of the wavelet has odd symmetry, and the other has even symmetry. Translation and scaling of the whole set (plus a single constant term) of wavelet results in a complete, orthonormal basis in two dimensions [3]. The BWT comprises eight mother wavelets, in four pairs. Each pair has a different orientation—0, 45, 90, and 135 degrees. For applying the BWT, the input image must be square and also the side length should be a power of three. The BWT decomposition is laid out as shown in Figure 1.

2009 International Conference on Advances in Recent Technologies in Communication and Computing

978-0-7695-3845-7/09 $25.00 © 2009 IEEE

DOI 10.1109/ARTCom.2009.201

357

2009 International Conference on Advances in Recent Technologies in Communication and Computing

978-0-7695-3845-7/09 $26.00 © 2009 IEEE

DOI 10.1109/ARTCom.2009.201

357

2009 International Conference on Advances in Recent Technologies in Communication and Computing

978-0-7695-3845-7/09 $26.00 © 2009 IEEE

DOI 10.1109/ARTCom.2009.201

357

Figure 1. BWT decomposition Each band represents a pair of wavelets having a specific orientation and is represented as

[ 90-odd 135-odd 45-even ] [ 90-even 135-even 45-odd ]

[ DC 0-even 0 -odd ]

III.PROPOSED WATERMARK METHOD In the proposed scheme a binary image is utilized as watermark data and its pixels are embedded invisibly into the host image for protecting the copyrights of the host image. The steps involved in the watermark embedding and extraction processes are described in the following subsections. A. Watermark Embedding The process of embedding the binary watermark image into the host image is presented in this sub-section. The host image’s size should be a power of 3nx3n and a binary image is used as watermark. At first, the non-overlapping blocks of size 9x9 are extracted from the host image. One pixel of binary watermark image is embedded into a single block. Since the watermark is a binary image, the embedding of watermark involves two cases: embedding the pixel value ‘1’ and embedding the pixel value ‘0’. In this grayscale watermark pre-processing, a 2-D 8 bit watermark pattern of size M×M is changed into a binary sequence w = {w(n) | n = 0, …, M2}, where w(n) ε {0,1}. The next step is to map the watermark information w into a bipolar vector wb = {wb(n) | n = 0,…, M2} and wb(n) is denoted as wb (n) = (−1)w(n) (1)

where n = 0,1,...,M2 The watermarking process is started by applying the BWT to the original image. The watermark embedding in the BWT domain is implemented through the following steps.

Step1.By using the BWT to the input image I(x,y), the image is decomposed in a triadic pattern as shown in figure 1. Step 2.Taking the lowermost band to the left,which is the DC band,perform the blocking process.As a result this band is divided into blocks of size 9x9.Each block ’Y’ is described by its coefficients as follows. Y = { Y(u, v) | u, v = 0, 1, 2, 3…9} (2) Step 3. During the watermarking embedding process, only one certain middle frequency coefficient Y(u,v) in the diagonal positions in a 9×9 block is substituted by Y(u, v) =α.β(n).wb (n) (3) Where u, v ε{0, 1, 2, 3….9} and indicate the position of the coefficient.Here, both α and β(n) are positive gain factors and α is decided empirically by the user. The local gain factor β(n) is obtained from the DC coefficient Y(0,0) and the AC coefficients Y(u,v) as given in (4). β(n) =|-Y(0,0)+ μ∑ 1≤u,v≤9|Y(u,v)|| (4)

where μ is a weighting factor. Step 4. Watermark sequences wb are iteratively embedded in the blocks. Step 5. Apply the inverse blocking process and the inverse Berkeley Wavelet Transform to the modified wavelet coefficients to obtain the watermarked image.

B. Watermark Extraction At the receiver, the watermarked signals are extracted from the transmitted data .The original image is required in extracting watermarks. Apply the same Berkeley Wavelet Transform to the watermarked image. Perform step 2. The watermark is extracted as follows.

wb (n) =Y(u,v) /(beta*alpha) (5)

III. EXPERIMENTS Experimental results of the proposed watermarking scheme are presented in this section. This paper takes host image of size 243x243 and a watermark of size 27x27.After embedding, the embedded watermarks are extracted from the watermarked images efficiently. The watermarked images have good Peak Signal to Noise Ratio (PSNR) and good visual quality. Also the robustness against standard noise attacks are tested.ie, Gaussian, Salt and Pepper, Median Filtering, PEG compression etc to the marked images. The watermark used is shown as

Figure 2. Watermark The watermarked images and extracted watermark for three different host images and the corresponding PSNR values are shown below.

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(a) (b)

PSNR=42.43 (c) Figure 3. (a) Host image (b) Watermarked Image (c) Extracted watermark

(a) (b)

PSNR=40.01 (c) Figure 4 . (a) Host image (b) Watermarked Image (c) Extracted watermark

(a) (b)

PSNR=41.34 (c) Figure 5. (a) Host image (b) Watermarked Image (c) Extracted watermark

V.CONCLUSION

In this paper a new watermarking algorithm based on Berkeley Wavelet Transform is proposed. The watermarked image produces good PSNR values. The BWT transform used in this paper is a complete orthonormal basis and is therefore inexpensive to compute, manipulate and invert.

REFERENCES

[1] D.Taskovski,S.Bogdanova,M.Bogdanov,“Digital Watermarking in Wavelet Domain”.University Sts.Cyril and Methodius, Faculty of Electrical Engineering,Karpos II b.b, Macedonia,1998.

[2] Ben Willmore, Ryan J. Prenger, Michael C.-K. Wu, Jack L. Gallant. “The Berkeley Wavelet Transform:A Biologically Inspired Orthogonal

Wavelet Transform”.2008. [3] Dr.M.A.Dorairangaswamy, “A Novel Invisible and Blind Watermarking

Scheme For Copyright Protection of Digital Images”,Easwari Engineering College, Chennai, India, IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.4, April 2009.

[4] Won-Jei Kim†, Jong-Keuk Lee††, Ji-Hong Kim††, and Ki-Ryong “Block-based Watermarking Using Random Position Key”, IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.2, Kwon†† Pukyong National University, Busan, Korea†† Dong-eui University, Busan, Korea,February 2009.

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