Hybrid Robust Watermarking Technique Based on DWT, DCT and SVD

Hybrid Robust Watermarking Technique Based on DWT, DCT and SVD

Harish N J, B B S Kumar & Ashok Kusagur Department of ECE, RRCE,Bangalore E-mail : [email protected], [email protected], [email protected]

Abstract - In this paper, we propose a hybrid watermarking scheme for digital images based on singular value decomposition (SVD). The two key aspects of watermarking schemes are copyright protected and robustness. In this, we are embedded the principal components of the watermark in the DCT domain of DWT subband of host image, for providing copyright protection as well as reliability. Since Scaling factor is an image dependent. Experimental results are provided to illustrate that the proposed scheme is able to withstand a variety of image processing attacks as well as imperceptibility.

The important requirements to be satisfied by any digital watermarking scheme are as follows.

Keywords - Copy Right Protection, Discrete Cosine transform, Discrete Wavelet Transform, Singular Value Decomposition, Watermarking.

Robustness: Even though an unauthorized person performs some modifications to the watermarked image through some common signal processing attacks and compression attacks etc. But the watermark can still be extracted. The scheme should resist the various attacks from hackers.

I.

Fidelity: This is about the perceptual similarity between the original image and the watermarked image. The watermark should be imperceptible and no visual effect should be perceived by the end user. The watermark may degrade the quality of the content, but in some applications a little degradation may accepted to have higher robustness or lower cost.

INTRODUCTION

Watermarking has been considered to be a promising solution that can protect the copyright of multimedia data through Trans coding, because the embedded message is always included in the data. However, today, there is no evidence that watermarking techniques can achieve the ultimate goal to retrieve the right owner information from the received data after all kinds of content-preserving manipulations. Because of the fidelity constraint, watermarks can only be embedded in a limited space in the multimedia data. There is always a biased advantage for the attacker whose target is only to get rid of the watermarks by exploiting various manipulations in the finite watermarking embedding space.

Non-invertibility: If we are unable to generate the same watermarked image with the help of different combinations of host and watermark images then it is called as Non-invertible watermarking scheme. This provides copyright protection. According to the domain in which watermark is embedded, these are divided into spatial domain and transform domain schemes. Embedding the watermark in the spatial domain is the direct method. It has less computational cost, high capacity, more perceptual quality but less robust and it mainly suits for authentication applications. In the frequency domain schemes, we embed the watermark with the transformed coefficients of host image. It has more robust, less control of perceptual quality and mainly suits for copyright application. The robustness and perceptual quality of the watermarking schemes are mainly depends on how much percentage of the watermark is embedded into host image i.e., Scaling factor.

There are many solutions that have been proposed like Cryptography [1], Steganography and Watermarking [2]. The watermarking technique provides one of the best solutions among them. This technique embeds information so that it is not easily perceptible to the others. The embedded watermark should not degrade the quality of the image and should be perceptually invisible to maintain its protective secrecy [1].

The main impediments of SVD based watermarking schemes are as follows:

ISSN (Print): 2278-8948, Volume-2, Issue-5, 2013

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International Journal of Advanced Electrical and Electronics Engineering, (IJAEEE)

False positive Problem: when a specific watermark is detected from content in which a different watermark was embedded, causing an ambiguous situation [6], [7] and [8].

the watermarking step is performed by scaling down the pixel values of watermark and then embedding those values into the cover image. After this the watermarked image is obtained on which various attacks are applied in order to achieve the robustness in watermarking. Then we follow the extraction phase where we apply again the wavelet transform, DCT and SVD and extract the watermark under attacks. Finally the correlation is determined between the watermark extracted and original watermark.

Diagonal Line Problem: If we modify the singular values of the cover image directly with the watermark image then there will be a diagonal line in the reconstructed watermark from the attacked watermarked images [9]. So to avoid the false positive problem and diagonal line problems, here we are embedding the principle components [7] of the watermark in the host image. The scaling factor controls the robustness and transparency of the scheme, which is imadependent.

Advantages:

The discrete cosine transform (DCT) represents an image as a sum of sinusoids of varying magnitudes and frequencies. The DCT has special property of Energy Compaction. The Discrete Wavelet Transform is useful for multi resolution analysis and subband coding. In this paper we are embedding the watermark in the DCT domain of DWT coefficients of host image. Hence we can achieve better performance when compared to using DCT alone [13]. II.

Diagonal line problem occurs



PSNR is less



Correlation coefficient is less

Diagonal line problem is resolved



PSNR is good



Correlation coefficient is high

V.

THE CHOICE OF MATLAB

MATLAB [2] brings to Digital Image Processing is an extensive set of functions for processing multidimensional arrays of which images(twodimensional numerical arrays) are a special case. The Image Processing Toolbox(IPT) with Wavelet Toolbox(WT)is a collection of function that extend the capability of the MATLAB numeric computing environment. These functions, and the expressiveness of the MATLAB language, make many image-processing operations easy to write in a compact, clear manner, thus providing an ideal software prototype environment for the solution of image processing problems.

Disadvantages: 



The main motive to implement this project is to solve the problems arising like False Positive and Diagonal Line problem. Also, the demerits of low PSNR and less correlation coefficient after extraction phase is to be resolved here. Considering these disadvantages of previous existing watermarking techniques, the project implemented to extract the image having a good quality of data and also if any type of attack is inserted in the channel and the effect of this attack should be nullified.

There are many solutions that have been proposed like Cryptography, Steganography and Watermarking. The watermarking technique provides one of the best solutions among them. This technique embeds information so that it is not easily perceptible to the others. The embedded watermark should not degrade the quality of the image and should be perceptually invisible to maintain its protective secrecy. The robustness and perceptual quality of the watermarking schemes are mainly depends on how much percentage of the watermark is embedded into host image i.e., Scaling factor.

False positive problem occurs

False positive problem is resolved

IV. PROBLEM DESCRIPTION

EXISTING SYSTEM





There are a several low-level programming languages that can be used to demonstrate the application of DSP & DIP, such as C++, and Java; but these require a high proficiency in programming.

III. PROPOSED SYSTEM

There are several high-level software packages that can be used to teach signal processing, such as Mathematica, Octave, and MATLAB. A good overview of these and other packages in terms of engineering education can be found. Mathematica is meant more for symbolic mathematics than creating applications.

The proposed Watermarking scheme is implemented as in two phases first watermarking and then extraction. First on image we will apply haar wavelet transform and will get four sub band images. On sub band we are applying first DCT and on that DCT matrix we are going to apply SVD in all values. Then


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Octave, a free open-source mathematics software application, is quite compatible with MATLAB code, but it lacks much of the rich library of functions available in MATLAB. In addition there is no easy way to create graphical user interfaces (GUIs).

The information to be embedded in a signal is called a digital watermark, although in some contexts the phrase digital watermark means the difference between the watermarked signal and the cover signal. The signal where the watermark is to be embedded is called the host signal. A watermarking system is usually divided into three distinct steps, embedding, attack, and detection. In embedding, an algorithm accepts the host and the data to be embedded, and produces a watermarked signal.

VI. 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, in the same manner as paper bearing a watermark for visible identification. In digital watermarking, the signal may be audio, pictures, or video. If the signal is copied, then the information also is carried in the copy. A signal may carry several different watermarks at the same time.

Then the watermarked digital signal is transmitted or stored, usually transmitted to another person. If this person makes a modification, this is called an attack. While the modification may not be malicious, the term attack arises from copyright protection application, where pirates attempt to remove the digital watermark through modification. There are many possible modifications, for example, lossy compression of the data (in which resolution is diminished), cropping an image or video or intentionally adding noise.

In visible digital watermarking, the information is visible in the picture or video. Typically, the information is text or a logo, which identifies the owner of the media. The image on the right has a visible watermark. When a television broadcaster adds its logo to the corner of transmitted video, this also is a visible watermark.

Detection (often called extraction) is an algorithm which is applied to the attacked signal to attempt to extract the watermark from it. If the signal was unmodified during transmission, then the watermark still is present and it may be extracted. In robust digital watermarking applications, the extraction algorithm should be able to produce the watermark correctly, even if the modifications were strong. In fragile digital watermarking, the extraction algorithm should fail if any change is made to the signal.

In invisible digital watermarking, information is added as digital data to audio, picture, or video, but it cannot be perceived as such (although it may be possible to detect that some amount of information is hidden in the signal). The watermark may be intended for widespread use and thus, is made easy to retrieve or, it may be a form of steganography, where a party communicates a secret message embedded in the digital signal. In either case, as in visible watermarking, the objective is to attach ownership or other descriptive information to the signal in a way that is difficult to remove. It also is possible to use hidden embedded information as a means of covert communication between individuals.

VIII. DISCRETE COSINE TRANSFORM The discrete cosine transform (DCT) is a function that has the ability to convert a signal into elementary frequency components. It represents an image as a sum of sinusoids of varying magnitudes and frequencies. The popular block-based DCT transform segments an image non-overlapping block and applies DCT to each block. This result in giving three frequency sub-bands: low frequency sub band, mid-frequency sub-band and high frequency sub-band. DCT-based watermarking is based on two facts [10]. The first fact is that most of the signal energy lies at low-frequencies sub band which contains the most important visual parts of the image. The second fact is that high frequency components of the image are usually removed through compression and noise attacks. [3] The watermark is therefore embedded by modifying the coefficients of the middle frequency sub band so that the visibility of the image will not be affected and the watermark will not be removed by compression.

One application of watermarking is in copyright protection systems, which are intended to prevent or deter unauthorized copying of digital media. In this use, a copy device retrieves the watermark from the signal before making a copy; the device makes a decision whether to copy or not, depending on the contents of the watermark. VII. DIGITAL WATERMARKING LIFE-CYCLE PHASES

Fig.1: General digital watermark life-cycle phases with embedding, attacking, and detection and retrieval functions


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IX. DISCRETE WAVELET TRANSFORM

most suitable biorthogonal wavelets implicated in digital watermark.

In mathematics, a wavelet series is the best representation of a square integrable (real or complexvalued) function by certain orthonormal series generated by a wavelet. This article provides a formal, mathematical definition of an orthonormal wavelet and of the integral wavelet transform. The wavelet transform can provide us with the frequency of the signals and the time associated to those frequencies, making it very convenient for its application in numerous fields. For instance, signal processing of accelerations for gait analysis.

A digital image is decomposed into three high frequency sub bands and a low frequency sub band by one level wavelet transform. The low frequency sub band can be decomposed continuously. With the more levels the image is decomposed by wavelet transform, the energy of the image is diffused better and the stronger image intensity can be embedded. So the wavelet decomposing levels adopted in the algorithms should be chosen as far as possible. The symmetry extension is adopted in wavelet decomposing process, while repeat symmetrical extension is adopted in wavelet reconstruction process. The standard test sequence Lena is decomposed by twolevel wavelet decomposition into low frequency sub band LL2, horizontal high frequency sub bands LH2 LH1, vertical high frequency sub band HL2 HL1 and diagonal direction high frequency sub band HH2 HH1, as shown in figure 3. (In order to display, the high frequency sub bands coefficients are amplified.)

In numerical analysis and functional analysis, a discrete wavelet transform (DWT) is any wavelet transform for which the wavelets are discretely sampled. As with other wavelet transforms, a key advantage it has over Fourier transforms is temporal resolution: it captures both frequency and location information (location in time). The discrete wavelet transform has a huge number of applications in science, engineering, and mathematics and computer science. Most notably, it is used for signal coding, to represent a discrete signal in a more redundant form, often as a preconditioning for data compression.

Fig.3: (a) Lena image after one level wavelet decomposition and (b) Two level wavelet decomposition

Fig. 2: Block diagram of filter banks of DWT first level

The Wavelet Transform of Digital Image

X. SINGULAR VALUE DECOMPOSITION

The basic thought of wavelet transform is using the same function by expanding and shifting to approach the original signal. The wavelet coefficients carry the timefrequency information in certain areas. It has good local characteristics both in time domain and frequency domain. It can maintain the fine structure of the original images in various resolutions and it is convenient to combine with human vision characteristics. Compared with the orthogonal wavelet, bi-orthogonal wavelet has more obvious superiority in image processing because it balances the orthogonality and symmetry. In addition, the reconstructing signal of biorthogonal wavelet transform is suitable to embed watermark for its balance. Daubechies9/7 wavelet is recommended in JPEG2000. In this algorithm Daubechies9/7 biorthogonal wavelet is selected because it is one of the

In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix, with many useful applications in signal processing and statistics. Formally, the singular value decomposition of an m×n real or complex matrix M is a factorization of the form

M  U V * where U is an m×m real or complex unitary matrix, Σ is an m×n rectangular diagonal matrix with nonnegative real numbers on the diagonal, and V* (the conjugate transpose of V) is an n×n real or complex unitary matrix. The diagonal entries Σi,i of Σ are known as the singular values of M. The m columns of U and the n columns of V are called the left-singular vectors and


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International Journal of Advanced Electrical and Electronics Engineering, (IJAEEE) k EPC = (U k ∗ Ck ∗ V kT )/ Φ

right-singular vectors of M, respectively. US are called the principal components of M.

Step 5: Construct the distorted principal component from their parts i.e.

XI. ALGORITHAMS FOR PROPOSED METHOD The proposed Watermarking characterized as follows:

scheme

Epc =

is

1 Epc 3 Epc

2 Epc 4 Epc

Step 6: Obtain the extracted watermark.

A. Watermark Embedding:

T EW = Epc ∗ VW

Step 1: Apply one-level Haar DWT to decompose the host image A, into four sub bands i.e.

XII. EXPERIMENTAL RESULTS

ALL , AHL , A LH , and AHH .

In this paper the simulation process is implemented in MATLAB using different types of host and watermark images. For testing purpose gray scaled (JPEG format) image is used.

Step 2: Consider AHL and perform 2D DCT and using zig-zag sequence, map the DCT Co-efficients of AHL into four quadrants: B1 , B2 , B3 and B4 . Step 3: Apply SVD to all four quadrants, Bk =U k S k V kT , where k=1, 2, 3 and 4.

The opted host (original) and watermark images are as follows:

Step 4: Apply the SVD on the watermark image and calculate the principal components of the watermark. T W = UW SW VW , P = UW ∗ SW

Step 5: Divide the principal components P, into four quadrants: P1 , P2 , P3 and P4 . Step 6: Modify the singular values of the DCT coefficients of the cover image with the principal components of watermark image i.e.,

Lena

Home

Girl

Fig.4: Host or Cover (original) images of size 256 x 256 pixels

S Wk = S k + Φ. Pk , k = 1, 2, 3 and 4. k Step 7: Perform, BW =U k S wk V kT , where k = 1, 2, 3and4. k Step 8: Map the coefficients of BW back to their original positions and apply DCT to produce the modified HL band, AW HL .

Text

Text

Girl

Fig.5: Watermark (original) images of size 128x128 pixels

Step 9: Perform the inverse DWT by using modified and non-modified coefficients to get the watermarked image, AW .

For robustness inspection of the scheme the watermarked image was tested against several types of attacks namely Histogram equalization, Speckle noise, Gaussian noise, Rotate-45, Best contrast, Salt & Pepper noise, Poisson noise, JPEG compression, Low pass filter and High pass filter.

B. Watermark Extraction: Step 1: Apply one-level Haar DWT to decompose the watermarked (possibly attacked) image Aw into four subbands: A∗LL , A∗HL , A∗LH , and A∗HH .

The quality of the watermarked image can be estimated using peak-signal-to-noise ratio (PSNR) and is calculated as follows

A∗HL

Step 2: Apply DCT on and using zig-zag scan arrange the DCT coefficients of A∗HL into four quadrants B1∗ , B2∗ , B3∗ and B4∗ .

PSNR  10 log 10

Step 3: Subtract each quadrant with the original transformed quadrants: Ck = Bk∗ - Bk , where k=1, 2, 3, and 4.

255 2 MSE

Where MSE represents the mean square error and is given by

Step 4: Compute the distorted principal component parts


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Table 2: Different values of NCC and CC for different attacks on Lena Image Where A and Aw are original and watermarked images. The similarities between the original, W and extracted watermarks, eW can be determined by using the normalized correlation coefficient (NC) and it is defined as follows: m

Noise Type Histogram equalize

n

 (w(i, j))  w )( w (i, j)   w) I 1 j 1

NC 

m

1

n

m

n

  (w(i, j)  w ) *  ( E 2

i 1

where w and respectively.

j 1

Ew

i 1 j 1

2 w (i, j )   w )

are mean values of W and Ew

The following figures represent the extracted watermark images with several attacks of the proposed scheme.

MSE 1.6308 1.6416

Girl Boat

45.9573 45.9804

1.6495 1.6407

0.989 0.969

0.992

0.998

0.94 0.946 0.944 0.987 0.982

0.918 0.894 0.939 0.993 0.991

High Pass Filter

0.982

0.991

CONCLUSIONS

By embedding the principle components of the watermark into the DCT of horizontal sub band of DWT decomposition of host image, it provides better imperceptibility as well as reliability in the quality and recovery of image. By implementing, can avoid the false positive problem and hence provides the copyright protection. From the extracted watermarks have proved the there is no diagonal line problem. And also achieved the suitable scaling factor for any watermark which is image dependent. Hence the robustness and transparency of the scheme for any watermarking can be processed.

Table 1: Different values of PSNR and MSE for different images PSNR (in dB) 46.0068 45.7364

0.918

0.982 0.961

In this proposed paper taken for analysis of different cover images like Lena and House for comparing the difference between PSNR and MSE values. In all the cases the value of PSNR is well above 20dB which shows the good quality of embedding algorithm in comparison of other techniques like DWT watermarking or DCT watermarking of DWT and SVD watermarking, the evaluated PSNR value limited up to 20-30dB along with diagonal line problems or false positive problems.

Fig.6: Extracted watermark images with several attacks

Image Lena House

Correlation Coefficient (NC)

Speckle noise Gaussian noise Rotate by +45 degree Best contrast Salt n pepper noise Poisson noise JPEG Compression Low Pass Filter

XIII.

The different values of PSNR & MSE for different images are listed in Table 1. Also for different attacks the values of Normalized Correlation Coefficient and Correlation Coefficient also changes. The different values of Normalized Correlation Coefficient and Correlation Coefficient for different attacks have been listed in Table 2.

Normalized Correlation coefficient (NCC) 0.949

The performance of the proposed method is tested by applying 1-level DWT compression technique and image processing attacks like Histogram equalization, Speckle noise, Gaussian noise, Rotate-45, Best contrast, Salt & Pepper noise, etc. and has got the values of NCC and CC are all above 0.85 which are acceptable. Thus proposed method is robust as well as reliable watermarking technique. The scheme has been tested


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with success on various test images on a MATLAB simulation platform.

[9]

Roman Rykaczewski. “Comments on “An SVDBased Watermarking Scheme for Protecting Rightful Ownership,” IEEE Transactions on Multimedia, Vol. 9, No. 2, pp. 421-423, Feb. 2007.

[10]

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[11]

Veysel Aslantas, A. Latif Dogan and Serkan Ozturk, “DWT-SVD based Image Watermarking Using Particle Swarm Optimizer,” ICME 2008.

[12]

www.swarmintelligence.org

[13]

Baisa L. Gunjal, R. R. Manthalkar, “An Overview of Transform Domain Robust Digital Image Watermarking Algorithms”, CIS Journal, Vol. 2 No.1, pp. 37-42, 2010-2011.

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[15]

Sha Wang, Dong Zheng and Jiying Zhao proposed “An Image Quality Evaluation Method Based on Digital Watermarking” Proceedings of IEEE Transactions on Circuits And Systems For Video Technology, Vol. 17, No. 1, January 2007.

XIV. FUTURE SCOPE This project further can be extended with multilevel DWT or two levels DCT or two levels SVD to enhance PSNR and Normalized Correlation Coefficient values. Also the method can be tried to analyze watermarking for RGB or color images with embedding in all three channels and extraction can also be done from all the three channels. Also, instead of hiding watermark image inside the cover image the project can be extended for hiding a text message inside the cover image. This technique can also be analyzed for audio watermarking or video watermarking. XV. REFERENCES [1]

Hernandez, J.R., M., Amado, and F.P., Gonzalez, 2000. “DCT-domain watermarking techniques for still for still Images: Detector performance analysis and a new structure”, IEEE Trans. on Image Processing, Vol. 9, pp. 55-68.

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