Audio Watermarking in Image by Using Radon – Wavelet Transforms

JOURNAL OF TELECOMMUNICATIONS, VOLUME 21, ISSUE 1, JULY 2013

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Audio Watermarking in Image by Using Radon – Wavelet Transforms

Osama Qasim Jumah Al-Thahab and Heba Abdul-Jaleel Alasady

Abstract-The rapid growth of digital media and communication network has highlighted the need for Intellectual Property Rights (IRP) protection technology for digital multimedia. Watermarking of multimedia data has become a hotspot for research in recent years. Watermarking can be used to identify the owners, license information, or other information related to the digital object carrying the watermark. Watermarks can provide the mechanism for determining if a particular work has been tampered with or copied illegally. In this paper, a novel algorithm for robust audio watermarking is presented in image using wavelet transform based on image, and radon transform on audio file for the first time. The motivation of choosing image as a cover is driven by the fact that human visual system is less sensitive than human auditory system thus an image provides better masking effect. The algorithm is based on decomposition of images using Haar wavelet basis. Performance of the algorithm has been evaluated extensively, and simulation results are presented to demonstrate the imperceptibility and robustness of the proposed algorithm.

Keyword-‐Discrete wavelet transform, radon transform, audio watermarking, image watermarking

.

• Osama Qasim Jumah Al-Thahab Lecturer in Engineering College Electrical Departement of Babylon university.

• Heba Abdul- Jaleel Alasady Msc student at Babylon University.

1 INTRODUCTION

Digital watermarking is a new technology used for copyright protection of digital media. Digital watermarking was introduced at the end of the 20th century to provide means of enforcing copyright protection of digital data. Where, ownership information data called watermark is embedded into the digital media (image, audio, and video) without affecting its perceptual quality. In case of any dispute, the watermark data can be detected or extracted from the media and used as a proof of ownership. Imperceptibility and robustness against attacks are the fundamental issues in digital watermarking techniques[1]. Audio watermarking techniques reported in literature can be grouped into two types; time-‐domain

techniques and frequency-‐transform domain technique. The two domains have different characteristics, and thus performances of their techniques may vary with respect to the robustness and imperceptibility (inaudibility) requirements of audio watermarking. Inaudibility refers to the condition that the embedded watermark should not produce audible distortion to the sound quality of the original audio, in such a way that the watermarked marked version of the file is indistinguishable from the original one[2].

watermarking gets divided into the following categories non-‐blind, semi-‐blind and blind methods. In non-‐blind methods, to extract the watermark the original image itself is being employed, while the semi-‐blind methods engages particular characteristics of the original image, in exception of the other two cases, the detection process in the blind methods do not


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necessarily requires the original image. To progress the robustness, majority of the researches, embed the watermark in the frequency domain. As a substitute for the spatial domain, diverse transformations widely employed are of the Discrete Cosine Transform (DCT), the Discrete Wavelet Transform (DWT), the Discrete Fourier Transform (DFT), Discrete Hadamard Transform (DHT) and more[3]. A fair amount of research has been done related to watermarking a binary sequence in an audio or an image which is relatively easy as the watermark consists of just two binary values and deviation of the coefficients of the transformed host from a predefined threshold are monitored to find out either a ‘1’ or ‘0’ value of the watermark[4].

The basis for using an image as a cover for watermarking an audio is the fact that Human Visual System (HVS) is less sensitive than Human Auditory System (HAS) and provides better masking properties [4-‐5]. Embedded watermarks are recovered by running the inverse process that was used to embed them in the cover work, that is, the original work. This means that all watermarking systems consist of at least two generic building blocks: a watermark embedding system and a watermark recovery system. Figure 1 shows a basic watermarking scheme, in which a watermark is both embedded and recovered in an audio file. As can be seen, this process might also involve the use of a secret key. In general terms, given the audio file A, the watermark W and the key K, the embedding process is a mapping of the form A*K*W=A' [5].

Figure 1: Basic watermarking system

This paper is organized as follows. In Sec. 2. DWT method is briefly described. Section 3. the Radon transform . Section 4. Audio watermarking . The Result &Discussion proposed in Section 5. Finally, The conclusion in section 6. 2 Discrete Wavelet transform:

Wavelets are special functions which, in a form analogous to sines and cosines in Fourier analysis, are used as basal functions for representing signals[2-‐6]. Discrete wavelet transform divides an image into 4 coefficient images in the single level. Each coefficient image contains one of low frequency bands and high frequency bands. With an M×N image, 2-‐D DWT generates four M/2×N/2 coefficients: LL, LH, HL, and HH, where LL represents a low frequency band, LH a horizontal high frequency band, HL vertical high frequency band, HH a diagonal high frequency band.

The low frequency band is utilized to the net level of DWT. In DWT, the most prominent information in the signal appears in high amplitudes and the less prominent information appears in very low amplitudes. Data compression can be achieved by discarding these low amplitudes.

The wavelet transforms enables high compression ratios with good quality of reconstruction Wavelet transform is capable of providing the time and frequency information simultaneously, hence giving a time frequency representation of the signal. DWT is believed to more accurately model aspects of the HVS (Human Visual System) as compared to the FFT or DCT. This allows to use higher energy watermarks in regions that the HVS is known to be less sensitive to. Inserting watermarks in these regions increases the robustness of watermark, additional impact on image quality. Experimentally it is being found that insertion in the LL portion of the DWT proves to be most robust against various kinds of attacks [7].

The wavelet functions induce an orthonormal decomposition of L2(R) using the equations (1,2):


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)2....(....................)(2)2/(

)1.....(....................)(2)2/(

∑∑

−=

−=

kk

k k

kxgx

kxh

ψψ

φχφ

The mother wavelet function is represented by ψ, φ is the scaling function (father wavelet), hk and gk are respectively low-‐pass and high-‐pass filters. The decomposition of a function f(x) can be done in two ways as seen in equations (3,4).

)4)....((,)(,)(

)3...(..............................).........(,)(

,,0

,,0,0

,,

,

xfxfxf

xfxf

nmnmm

nmnmn

nm

nmnm

nm

ψψφφ

ψψ

∑∑

∑

≤

+=

=

Since most dynamic processes have a low-‐pass character, the scaling function term only is able to approximate the dynamic system [8]. Discrete Wavelet Transformation (DWT) of image produces the multi-‐resolution representation of image. A multi-‐resolution representation provides a simple hierarchical framework for interpreting the image information. At different resolutions, the details of an image generally characterize different physical structures of the image. At a low level resolution, these details correspond to the larger structures which provide the image content. Wavelet transformation consist of two main steps namely DWT and IDWT (Inverse DWT). DWT segments a digital signal into high frequency quadrant and low frequency quadrants. The low frequency quadrant is split again into two more parts of high and low frequencies and this process is repeated till the signal has been entirely decomposed.

In watermarking, generally 1-‐5 level of decompositions is used. The reconstruct of the original signal from the decomposed image is performed by IDWT. Several types of wavelets exist for decomposition. Some examples include Haar, Daubes chies, Coif lets, Sym lets, Mor lets, Mexican Hat Meyer and Bi-‐orthogonal wavelets[9]. For example starting from the original audio signal S, DWT produces two sets of coefficients as shown in Figure 2. The

approximated coefficients A (low frequencies) are produced by passing the signal S through a low pass filter y. The details coefficients D (high frequencies) are produced by passing the signal S through a low pass filter g.

Figure 2: One-‐level DWT decomposition Depending on the application and the

length of the signal, the low frequencies part might be further decomposed into two parts of high and low frequencies. Figure 3 shows a 3-‐level DWT decomposition of signal S. The original signal S can be reconstructed using the inverse DWT process[10].

Figure 3: Three-‐level DWT decomposition.

3 Radon transform:

The Radon transform is named after the Austrian mathematician Johann Karl August Radon. The main application of the Radon transform is CAT scans. where the inverse Radon transform is applied[11]. In recent years the Radon transform have received much attention. This transform is able to transform two dimensional images with lines into a domain of possible line parameters, where each line in the image will give a peak positioned at the corresponding line parameters.

This have led to many line detection applications within image processing, computer vision, and seismic . The Radon Transformation is a fundamental tool which is used in various applications such as radar imaging, geophysical imaging, nondestructive testing and medical


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imaging. The Radon transform computes projections of an image matrix along specified directions. A projection of a two-‐dimensional function f(x,y) is a set of line integrals. The Radon function computes the line integrals from multiple sources along parallel paths, or beams, in a certain direction. The beams are spaced one pixel unit apart.

To represent an image, the radon function takes multiple, parallel-‐beam projections of the image from different angles by rotating the source around the center of the image. Figure 4 shows a single projection at a specified rotation angle.

Figure 4: Single projection at a specified rotation angle.

The Radon transform is the projection of

the image intensity along a radial line oriented at a specific angle. The radial coordinates are the values along the x'-‐axis, which is oriented at θ degrees counter clockwise from the x-‐axis. The origin of both axes is the center pixel of the image. For example, the line integral of f(x,y) in the vertical direction is the projection of f(x,y) onto the x-‐axis; the line integral in the horizontal direction is the projection of f(x,y) onto the y axis. Figure 5 shows the horizontal and vertical projections for a simple two-‐dimensional function.

Projections can be computed along any angle θ, by using general equation of the Radon transform as seen in equation (5).

)5.........()'sincos(),()'( dxdyxyxyxfxR −+= ∫ ∫∞

∞−

∞

∞−

θθδθ

)6.(........................................sincos' θθ yxx +=

where δ(·∙) is the delta function with value not equal zero only for argument equal 0, and x' is the perpendicular distance of the beam from the origin, and θ is the angle of incidence of the beams.

Figure 6 illustrates the geometry of the Radon Transformation. The very strong property of the Radon transform is the ability to extract lines (curves in general) from very noise images. Radon transform has some interesting properties relating to the application of affine transformations. We can compute the Radon transform of any translated, rotated or scaled image, knowing the Radon transform of the original image and the parameters of the affine transformation applied to it.

Figure 5: Horizontal and Vertical Projections of a Simple Function.

Figure 6: Geometry of the Radon Transform.

This is an interesting property for symbol representation because it permits to distinguish


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Host image DWT

Audio file

Radon transform

Detail coefficients

Embed

Water-‐marked image

IDWT

between transformed objects, but we can also know if two objects are related by an affine transformation by analyzing their Radon transforms. It is also possible to generalize the radon transform to detect parameterized curves with nonlinear behavior [12]. 4 The Proposed Audio Watermarking Scheme

The proposed system is shown in figure 7, and it can be seen that the addition is the using of radon transform for encoding the audio signal, then the result of it will embedded with the host encoded image to produce the watermarking image.

Figure 7: block diagram of the proposed system

4.1 Embedding process The embedding process can be described

in the following steps: a) Convert the original image to gray of size N*N. b) Apply the DWT to the cover image which is the original image. c) Apply the Radon transform on the audio file. d) Embed the audio file after Radon transform in the wavelet coefficients which is the approximation coefficients matrix (ca) & details coefficients matrices (cd). e) finally, Apply the IDWT to reconstruct the original image which is called watermarked image.

4.2 Extraction process

The audio signal can be introduced from the watermarking image by using the extraction process as seen in figure 8.

Figure 8: block diagram of extraction process.

The extraction process can be described in the following steps: a) Read the watermarked image of size N*N. b) Apply the discrete wavelet transform on the watermarked image. c) Extract the watermark from the wavelet coefficients and resize it to the desired size. d) Finally , Apply the inverse Radon transform and read the Audio file. 5 Result &Discussion

In this section the effect of embedding algorithm on cover image is discussed in terms of perceptual similarity between the original image and watermarked image using PSNR and Entropy. The proposed technique uses the wavelet transformation and Radon transformation domains to embed the data so as to exploit the advantages of wavelet and Radon transformations being resistant to frequency attacks. The host image is (Lena.jpg) cover images of size 512*512 as shown in figure 9. The performance of extraction algorithm can be tested by considering different types of image processing attacks on watermarked gray-‐level image such as rotation, adding salt, pepper noise, contrast enhancement, and adding Gaussian noise. A host of these attacks can be depicted in Table 1.

From the table 1, it is cleared that the proposed algorithm works well and have a resistant to a different types of attacks.

The effect of different noise on the cover image can be seen in figure 10, while The original audio signal is seen in figure 11, and the extracted audio signal is shown in figure 11 which shows the effect of noise on the audio signal.

Watermarked image

Audio file

DWT Watermark extraction process

Inverse Radon

transform

Size of watermark


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Figure 9: the host image

Table 1: The effect of attacks

(a)

(b)

(c)

(d)

(e)

Parameter No attack

Salt & pepper

noise(0.05)

Gaussian noise (0.01)

Rotation (900)

Poisson noise

Contrast Enhancement

PSNR

79.7797 79.3061 73.2712 79.7489 75.2240 54.9120

SNR

27.7403 27.5237 23.4097 27.7583 25.8337 11.2002

Rate

0.2921 0.2916 0.3345 0.2915 0.3141 0.4422

RMS

4.6049 4.7026 6.3073 4.5995 5.7902 15.9240

Entropy (Original image)

7.5239 7.5239 7.5239 7.5239 7.5239 7.5239

Entropy (Watermarked image)

7.5239 7.6441 7.8014 7.6302 7.7022 7.9002


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0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000-0.5

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(f) Figure 10: Noise effect a) Salt & pepper. b) Gaussian noise. c) Rotation (900). d) Poisson noise. e) Contrast effect. f) cover image.

Figure 11: The original audio signal

Figure 12: The effect of noise on audio file 6 Conclusion

This work proposes an innovative audio watermarking scheme employing image as a host medium and audio as watermark that uses randomness as a metric for selecting the target area in an image. However, fine correlation between the original audio watermark and the extracted watermark using the proposed technique is observed from their respective

RMS values. Selecting the target area based on the randomness metric allows us in achieving better PSNR.

The wavelet domain was chosen for data hiding due to its low processing noise and suitability for frequency analysis, because of its multi resolutional properties that provide access both to the most significant parts and details of signal’s spectrum Furthermore, the watermarked image is subjected to various noisy attacks. Here a watermarking algorithm based on hybrid technique which uses the methods of (DWT-‐Radon) transform is a highly robust and can resist many image processing attacks. The quality of the watermarked image is good in terms of perceptibility and PSNR .

The proposed algorithm is shown to be robust to all the attacks mentioned earlier except for Contrast Enhancement attack a good PSNR values can be get.

7 References

[1] Ni. I. Yassin1, N. M. Salem2, and M. I. El Adawy, ''Block Based Video Watermarking Scheme Using Wavelet Transform and Principle Component Analysis'', IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 1, No 3, January. [2] A. Al-‐Haj, A. Mohammad and L. Bata,'' DWT–Based Audio Watermarking''. The International Arab Journal of Information Technology, Vol. 8, No. 3, July 2011 [3] P. Vundela and V. Sourirajan, ''A Robust Multiwavelet-‐Based Watermarking Scheme for Copyright Protection of Digital Images Using Human Visual System''. [4] M. A. Bhat, P. G. Arfaat, S. M. Hussain, ''Audio Watermarking in Images using Wavelet Transform'', IJCST Vol. 2, Iss ue 4, Oct . -‐ Dec. 2011, International Journal of Computer Science And Technology. [5] C. S. Lu, ''Multimedia security steganography and digital watermarking techniques for protection of intellectual property'', Chun-‐Shien Lu Institute of Information Science Academia Sinica, Taiwan, ROC, 2005. [6] S. Murty, P. R. Kumar, ''A Robust Digital Image Watermarking Scheme Using Hybrid


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DWT-‐DCT-‐SVD Technique'', IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.10, October 2010. [7] S. N. Ahmed, B. Sridhar, C. Arun, ''Robust Video Watermarking based on Discrete Wavelet Transform'', International Journal of Computer Network and Security(IJCNS) Vol 4. No 1. Jan-‐Mar 2012. [8] M. Steinbuch, and V. D. Molengraft., ''Wavelet Theory and Applications'', Eindhoven, June 7, 2005. [9] M. L. Valarmathi, and S. Radharani, ''Multiple Watermarking Scheme for Image Authentication and Copyright Protection using Wavelet based Texture Properties and Visual Cryptography'', International Journal of Computer Applications (0975 – 8887) Volume 23– No.3, June 2011. [10] A. Al-‐Haj, A. Mohammad, ''Digital Audio Watermarking Based on the Discrete Wavelets Transform and Singular Value Decomposition'' European Journal of Scientific Research Vol.39 No.1 (2010), pp.6-‐21. [11] C. Hoilund, ''The Radon Transform'' November 12, 2007. [12] M. Miciak, '' radon transformation and principal component analysis Method applied in postal address recognition task'', International Journal of Computer Science and Applications, Techno-‐mathematics Research Foundation Vol. 7 No. 3, pp. 33 -‐ 44.

Audio Watermarking in Image by Using Radon – Wavelet Transforms

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