An Efficient Image Watermarking Scheme Using BFS Technique ...

Smart Computing Review, vol. 3, no. 5, October 2013

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Smart Computing Review

An Efficient Image Watermarking Scheme Using BFS Technique Based on Hadamard Transform Md. Iqbal Hasan Sarker and Mohammad Ibrahim Khan Department of Computer Science & Engineering, Chittagong University of Engineering & Technology / Chittagong-4349, Bangladesh / {iqbal, muhammad_ikhancuet}@cuet.ac.bd * Corresponding Author: Md. Iqbal Hasan Sarker Received June 22, 2013; Revised September 2, 2013; Accepted September 9, 2013; Published October 31, 2013

Abstract: Digital watermarking is a modern technology evolved to prevent illegal copy or reproduction of digital content. In recent years, several digital watermarking techniques have been presented based on discrete cosine transform, discrete wavelet transform and discrete Fourier transform. In this paper, we propose a robust and efficient digital image watermarking algorithm based on Hadamard transform. This algorithm can embed or hide an entire image or pattern, such as a company’s logo or trademark, as a watermark directly into original image for copyright protection. In proposed scheme, a best-first search technique is used to find the longest increasing sequence in each block of an image in which to embed the watermark. The experimental results show that the proposed algorithm offers better image quality and more robustness under various alterations, such as JPEG compression, cropping, sharpening and filtering among others. The results have proved the higher imperceptibility and robustness of this scheme when compared with other available watermarking techniques. Keywords: Digital image watermarking, best-first search, Hadamard transform

Introduction

DOI: 10.6029/smartcr.2013.05.001

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igital media is the main source of communication now-a-days. While digital media has been flourishing, with notable advancement in recent years, the distribution of unauthorized copies of media content has also been increasing day by day. Because of easy access to digital content, online purchasing and distribution becomes easier. Thus there is an urgent need to provide protection for digital content. One solution gaining popularity in protecting digital content is digital watermarking. Digital watermarking is the process of embedding information into a digital signal, which may be used to verify its authenticity or the identity of its owners. Watermarking can be classified in several ways. Based on application, watermarks are sub-divided into fragile, semifragile and robust. Fragile watermarks are very sensitive. They can be destroyed easily with slight modifications in the watermarked signal. Semi-fragile watermarks are also broken if the modifications to the watermarked signal exceed a predefined user threshold. If the threshold is set to zero then it operates as a fragile watermark. On the other hand, a robust watermark withstands disruptive alterations, such as scaling, rotation, filtering and compression. These watermarks cannot be broken easily. For this reason, we use this kind of watermarks in our proposed method. According to the need of original image for watermark extraction or detection, watermarking techniques are classified as either non-blind or blind. The original image is needed when detecting the watermark in a non-blind technique whereas a blind watermarking technique does no need the original image for extraction or detection. Our proposed method follows the blind watermarking technique. According to domain, there are different approaches for digital image watermarking such as the spatial-domain and frequency domain technique. In frequency domain, the watermark is embedded by changing the frequency coefficients and there are several domains such as discrete wavelet transform (DWT), discrete cosine transform (DCT) discrete Fourier transform and Hadamard Transform. In the spatial domain, the watermark is embedded directly by modifying the intensity values of pixels. The spatial domain watermarking technique is easier and its computing speed is higher than transform domain watermarking. But the disadvantage is that it is not robust against common image processing operations. Transform domain techniques are introduced to increase the robustness of the digital media. In this paper, we propose a robust watermarking method based on Hadamard Transform. The simplicity of Hadamard transform offers significant advantages (shorter processing time and ease of hardware implementation) compared to most orthogonal transform techniques, such as DWT and DCT. The rest of the paper is organized as follows. In Section 2, we review the previous work in digital watermarking. Section 3 introduces the Hadamard transform. Section 4 introduces the search technique and longest sequence. Section 5 describes our proposed watermarking system including the watermark embedding process and the watermark detection process. Section 6 represents our experimental results and finally, Section 7 concludes this paper.

Related Work Ho et al. proposed a frequency domain watermarking system for digital image using the Fast Hadamard Transform [1]. Saryazdi and Nezamabadi-pour developed a new blind gray-level watermarking scheme in which the host image is first divided into 4×4 non-overlapping blocks [2]. For each block, two first AC coefficients of its Hadamard transform are then estimated using DC coefficients of its neighbor blocks. Deb et al. proposed a combined DWT and DCT-based watermarking technique with low frequency watermarking with weighted correction [3]. Hosain presented a survey of various digital watermarking techniques for multimedia data such as text, audio, image and video for copyright protection [4]. Al-Haj described an imperceptible and robust combined DWT-DCT digital image watermarking algorithm for copyright protection [5]. Rui-mei et al. proposed a blind watermarking algorithm based on DCT for digital image in which a two-bit image is embedded in an eight-bit gray image [6]. Kountchev et al. proposed a new method where a still digital image is transformed using the complex Hadamard transform [7]. Lee et al. proposed a survey of watermarking systems that are applied to multimedia [8]. Sathik and Sujatha described a watermark construction process where a scrambled version of the watermark is obtained with the help of the Arnold transform [9].

Hadamard Transform The Hadamard transform (also known as the Walsh-Hadamard transform or Walsh-Fourier transform) is an example of a generalized class of Fourier transform. The Hadamard matrix H m represents an M  M Hadamard matrix, where M

 2m , 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 [1], can be found in Appendix, using Formula (6) where m > 0.

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Sarker et al.: An Efficient Image Watermarking Scheme Using BFS Technique Based on Hadamard Now, for m=1, M=2, a

2 2 Hadamard matrix is in Appendix, by Formula (7). Let [O] represents the original image

and [T ] the transformed image, and the 2D-Hadamard transform is [1], can be found in Appendix using Formula (8). The inverse two-dimensional Hadamard transform is in appendix, using Formula (9). In our proposed algorithm, the processing is performed on 8 8 sub-blocks of the whole image, so the third order Hadamard matrix

H 3 becomes : 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   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)

Search Technique and Longest Sequence Best-first search in its most general form is a simple heuristic search algorithm. “Heuristic” here refers to a general problem-solving rule or set of rules that do not guarantee the best solution, or any solution, but serves as a useful guide for problem-solving. Best-first search is a search algorithm that explores a graph by expanding the most promising node chosen according to a specified rule. The term “best-first” refers to the method of first exploring the node with the best “score”. Best-first search is a graph-based search algorithm meaning that the search space can be represented as a series of nodes connected by paths. Hence in Figure 1, S is the starting point.

1

2

3

4

5

6

7

8

S

Figure 1. Order of searching Example : Consider a matrix of a sub-block of size

Figure 2. All possible sequences

8 8 .

Figure 3. Longest sequence

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To get an increasing sequence, first we start our search from the bottom-right corner (S) of each block. Hence, in our example S=7.0. Then we compare all values from S which is shown in Figure 1 using directions. In the first step, all values greater than S are stored in a queue. Then S values are changed. Now all the values of the queue are again considered as S. Then in the second step, all values greater than S are stored in another queue. And this goes on. When no value is found greater than S then search is finished. Finally we may get some sequences of values which are shown in Figure 2. Finally, we select the longest sequence, also shown in Figure 3. Longest increasing sequence

7.0

13.2

15.5

37.0

37.2

53.7

55.5

121.2

In the above example, we get such a long increasing sequence consisting of some values that start from 7.0 and end at 121.2, and the highest value of this sequence is 121.2.

Proposed Watermarking Method In this section we will discuss about our proposed method of watermarking. This method consists of two processes; the watermark embedding process and the watermark extraction process.

■ Embedding Process For the embedding process we have the following steps. Figure 4 presents the flow diagrams of the proposed embedding method. Step_1: Divide the input host image into

8 8 non-overlapping blocks.

Step_2: The Hadamard transform is performed on each sub-blocks of the host image. For each sub-block, 64 Hadamard transform coefficients are obtained. Each block has a DC coefficient in the upper-left corner which is avoided for embedding. So we choose the other 63 coefficients for embedding purposes of each block. Step_3: Now the best-first search algorithm is applied to find the increasing sequence of each block. In Figure 1, S is the main point from which we start searching of each block and 8, 5, 6 is the order of searching. From these coefficients, an efficient point is selected by using a priority queue. We also show this in our above example. Step_4: From these increasing sequences, the longest sequence is selected. The highest value of this longest increasing sequence in used for embedding. Step_5: The watermark image is divided into each sub-block.

8 8 non-overlapping blocks and the Hadamard transform is applied to

Step_6: From the Hadamard transform coefficients of the watermark image, points are selected within the range-: 0 < x < 1 or -1 < x < 0 stored in a visited matrix. The rest of the components are stored in a key matrix, which is used in the decoding process. Step_7: Apply the following equation to embed the watermark image in to the host image.

f ( x, y)    w(i, j ) where

(2)

w(i, j ) denotes a watermark Hadamard co-efficient, f x, y  denotes a watermarked Hadamard co-efficient and

 denotes the scaling factor.

Step_8: After performing the insertion for all sub-blocks the inverse Hadamard transform is applied to all matrices using Equation (9) to produce the watermarked image.

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Original image Watermark image Divide the host image into sub-blocks Divide the watermark image into sub-blocks Apply Hadamard transform on each block

Apply Hadamard transform on each block Apply best-first search algorithm to find longest increase sequence of this block for embedding watermark

yes

Highest value of the longest increase sequence is selected for embedding watermark co-efficient

Watermark embed equation f(x, y) = α*w(x, y)

Hadamard coefficient x in range: 0