Efficient hybrid Watermarking Approach by Using SVD, DWT, and Back Propagation Neural Network

Hemraj Saini

Department of Computer Science & engineering Jaypee University of Information Technology, Waknaghat-173234 (INDIA)

hemraj1977@yahoo.co.in; hemraj.saini@juit.ac.in

Abstract— The paper deals to propose a hybrid watermark embedding and extracting technique. SVD and DWT methods are used for watermark embedding because DWT method is more flexible and provides a wide range of functionalities for still image processing. Further a significant image attacks are carried out on the watermarked image and the watermark is extracted by the help of proposed watermark extraction algorithm based on Back Propagation Neural Network (BPNN).

In the current era of online business such type of technique can play a significant role to identify the copyright of the product even there are numerous image attacks on the watermarked image. It can lead to save the owner of the product from a heavy loss and restrict the further theft of the products.

The proposed watermark embedding and extracting algorithms are tested on MATLAB 8.2 with various parameters such as PSNR and MSE. The result shows that after a numerous image attacks like paper and salts, mean, median, shear, noise, crop and rotation the watermark is identifiable.

Index Terms— DWT, SVD, DWT-SVD Hybrid watermarking, Bipolar Neural Network, and Image Attacks

I. INTRODUCTION Due to the numerous developments in the field of Internet

Technology the traditional shops are shifting their business on the Internet to earn more and more profit and sale of volume. Products are delivered by venders through Internet only if the form of the products is software. In this situation there is a possibility of theft and misuse of the product which leads to a heavy loss to the owner of the product. In this process an audit to identify the owner of the product may include. Generally, the owner of the product embeds a copyright watermark in the main image of the product which is considered as the license. There may be a significant number of image attacks to destroy the watermark in the licensed image. Therefore, an efficient algorithm is required to embed in the original image and extract the watermark from the attacked watermarked image. There are various literatures available for the purpose based on different elementary approaches like Discrete Cosine Transform (DCT) [1, 2, 3], Discrete Wavelet Transform (DWT) [4, 5, 6], Discrete Fourier transform (DFT) [7, 8, 9] and Singular value decomposition- Discrete Cosine Transform (SVD-DCT) [10, 11, 12] but there is a scope of improvement in case of computation aspects.

The paper uses SVD-DWT hybrid method for watermark embedding as it is able to fill the gap of DCT-based image processing method. A fundamental shift in the image

compression approach has been observed after the DWT. A large number of usages are also provided by DWT in the applications of still images. Some of the beneficial characteristics of DWT are like-

a) High compression efficiency b) Error resilience and lossless color transformations c) Lossless encoding d) Embedded lossy to lossless encoding e) Better resolution and quality etc.

SVD has the degree of freedom which means that three decomposition matrixes are equal to the original image and therefore it is decided to use it with DWT.

The paper is further organized into different sections like- section-II: Existing methodologies used in the text (DWT, SVD and BPNN), section-III: Algorithm for watermark embedding, section-IV: Proposed algorithm for watermark extraction, section-V: Result analysis and section-VI: Conclusion.

II. EXISTING METHODOLOGIES USED IN THE TEXT

A. DWT To understand the DWT, it is assumed that there is a

discrete signal x which has N samples and can be represented in low-pass filters and high-pass filters with down sampling of output signal by 2. After this process each frequency band has N/2 samples and this process is reversible. By this methodology an image can be converted into two frequency bands- low-pass band and high-pass band. This division can be extended further to achieve multiple dimensions by using separable filters.

The wavelet series expansion [13] can be defined by the function relative to wavelet and scaling function . It can be written as

(1) where is an random initial scale and the ’s are

normally called the scaling coefficients, the ’s are called the wavelet coefficients. The expansion coefficients are calculated as below-

(2) (3)

DWT of is the resulting coefficients of the sequential expansion of samples of a continuous function . This

985978-1-4799-2572-8/14/$31.00 c©2014 IEEE

series expansion is defined in Equations (2) and (3) which becomes the DWT transform pair as follows-

(4)

(5) for and

(6)

where , , and are considered as the functions of discrete variable .

B. Two Dimension Wavelet Transforms In two dimensions, a 2-D scaling function, , and

three 2-D wavelet , and , are required [13]. Each one of them are defined as the product of a 1-D scaling function and corresponding wavelet .

(7) (8) (9) (10)

Where, is the measurement of variations along columns, is the measurement of variations along rows, and

is the measurement of variations along diagonals. The 2-D DWT can be implemented like 1-D DWT, using

filters and down samplers. The purpose can be achieved by taking 1-D FWT of the

rows of followed by the 1-D FWT of the resulting columns with separable 2-D scaling and wavelet functions. Figure-1 shows the process in block diagram form.

Figure-1: The 2-D FWT- the analysis filter.

Figure-2: 2-scale of 2-D decomposition

Figure-3: The 2-D FWT - the synthesis filter bank.

An arbitrary scale transform has to be achieved by applying the iterations over the approximation output of 1-scale filter bank to the input of another filter bank. In case of 1-D DWT an image is to be used as the first scale input which produces four quarter-size subimages- , , and as an output. These subimages are shown in the middle of figure-2. 2-scale decomposition is to be produced by applying two iteration of the filtering process as depicted in figure-2. The reverse process of the above for synthesis of filter bank is also depicted in figure-3.

C. Singular Value Decomposition If a matrix is singular or close to singular, it has no well

defined inverse. This problem can be solved by SVD, the most popular solution [14]. It is possible to uniquely decompose any real matrix as shown below-

(11) Where, is a column orthogonal matrix, is

orthogonal matrix and is diagonal matrix having non-negative real values called singular values.

ordered so that , if is a singular value of , its square is an eigenvalues of

. If and , then

(12) Where the range of sum varies from 1 to rank of A which is

r. B. Chandra Mohan [15] stated that the non-negative

component of D represents the luminance of the image and changing them a little does not affect the quality of the image. These singular values do not also change much after a significant image attacks and hence used in the watermarking algorithms.

D. Back Propagation Neural Network In case of realistic applications of learning, BPNN plays

not only the role of most accepted network but also a tool. BPNN is the most popular network for practical

applications and a very powerful tool. It is an efficient supervised learning approach to the neural networks. Most elementary part of a neural network is a neuron. Figure-4 depicts the structure of a neuron.

Figure-4: Neuron The neuron has:

a. Synapses- a set of nodes with inputs, output or other neurons.

b. Linear Combiner- a function that accepts all inputs and generates a single value.

986 2014 IEEE International Advance Computing Conference (IACC)

c. Activation Function- It will accept any input value from and convert it into the or into

interval. Computational flavor of the neuron will be observed when

the neurons are to be connected in a multilayer structure which is known as neural networks. The following figure-5 represents a simple neural network.

Figure-5: Neural Network In the first layer of the network there are required inputs

to number of neurons. Further in the next layer there are number of inputs to number of neurons where is equal to the number of outputs from first layer.

E. Back Propagation: Teaching the Neural Network Inputs are submitted to the nodes of first layer and these

nodes further produce the single output which may have some error in comparison of the desired output. This error can be corrected by modifying the input weights of previous layer and this process is known as back propagation which is depicted in figure-6.

Once the adjustment is decided, it needs to apply to the neurons in the output layer, which can back propagate the changes to the previous layers of the network. This process will remain continue till the desired output is not obtained.

Above mentioned characteristic of BPNN can be utilized to reduce the different types of errors in image processing like- root mean square error or mean square error. In the present work BPNN is utilized to establish the relationship among the singular values of original image, attacked image and watermarked image. Structure of BPNN is shown in Figure 6 which is having three layers-

a. Input b. Hidden and c. Output Let the is of input layer, is of hidden

layer and output node then : Weight for connecting

: Weight for connecting

Figure-6: Structure of BPNN

III. ALGORITHM FOR WATERMARK EMBEDDING Watermark_Embedding(Original Image, Watermark Image) Step-1: Divide the original image into non-overlapping 16 x 16 blocks. Step-2: Apply DWT on each block. Let it is ID. Step-3: Apply SVD on all blocks

Step-4: Divide the watermark image into non-overlapping 16 x 16 blocks. Step-5: Apply SVD on all blocks

Step-6:

Where, is the watermark strength. Step-7: Perform inverse SVD to get all modified blocks. Step-8: Perform inverse DWT i.e. IDWT on each modified block and then merge all the blocks to get the watermarked image. Step-9: Generate a random key ID. Step-10: Secrete key composition: Apply the XOR operation to the corresponding bits of the key to compute the secrete key as-

Where, (i = 1 to 7 ) and

Step-11: Add secrete key to the watermarked image.

Figure-7: Watermark Embedding Process Watermark embedding process is depicted in Figure-7.

IV. PROPOSED ALGORITHM FOR WATERMARK EXTRACTION Watermark_Extraction(Key, Modified Image) Algorithm Step-1: Enter the key. If (Entered key == Original Key) then

Apply BPNN to extract the watermark. Note: Input to the BPNN is modified singular values. Target to the BPNN is singular values of original image. Output of the BPNN is Singular values of watermark image. Step-1.1: Apply SVD on ID.

Step-1.2: Extract the singular values of watermark

2014 IEEE International Advance Computing Conference (IACC) 987

Step-1.3: Perform inverse SVD to get the extracted watermark

else exit.

Figure-8: Watermark Extraction Process Watermark extraction process is depicted in Figure-8.

V. ANALYSIS OF RESULTS A set of six (06) images has been considered for analyzing

the performance of proposed algorithms. In the set three (03) images are considered as the original host images and other three (03) are considered as watermark images as depicted in figure-9 (a)-(i).

Figure-9: (a)-(c) Original Images; (d)-(f) Watermark

Images; (g)-(i) Watermarked Images

Figure-10: (a)-(c) Watermarked Images; (d)-(f) Gaussian

Noise Attacked Images and (g)-(i) Corresponding Extracted Watermark; (j)-(l) Pepper & Salt Attacked Images at density of 0.2 and (m)-(o) Corresponding Extracted Watermark

MATLAB R2010a has been used to implement the algorithms. First of all three (03) original images and a

watermark imaged is used to generate the corresponding watermarked images as shown in figure-11 (a)-(c). In the next step a particular image attack has been carried out on the watermarked image which generates the attacked images. Finally the proposed BPNN algorithm is executed to extracting the watermark from the watermarked image.

Figure-11: (a)-(c) Watermarked images; (d)-(f) Cropping

attacked images and (g)-(i) corresponding extracted watermark; (j)-(l) Shear attacked images and (m)-(o) corresponding extracted watermark

Figure-10 (d)-(f), Figure-10 ((j)-(l), Figure-11 (d)-(f), Figure-11 ((j)-(l), Figure-12 (d)-(f), Figure-12 (j)-(l) and Figure-13 (d)-(f) shows the attacked images generated after Gaussian noise attack, pepper & salt attack, cropping attack, shear attack, mean attack median attack and rotation attack respectively. Finally, the corresponding extracted watermarks from the attacked images are also depicted in Figure-10 (g)-(i), Figure-10 (m)-(o), Figure-11 (g)-(i), Figure-11 (m)-(o), Figure-12 (g)-(i), Figure-12 (m)-(o) and Figure-13 (g)-(i).

It has been observed clearly that all the extracted watermarks are identifiable even the attacked images are having very less PSNR value in comparison of the original PSNR value of original image as shown in Table-1 and in Figure-14. In the present case the three original images are having the PSNR as- Lena – 56.826, Mandril – 54.643 and Pepper – 55.754.

Figure-12: (a)-(c) Watermarked images; (d)-(f) Mean

attacked images and (g)-(i) corresponding extracted watermark; (j)-(l) Median attacked images and (m)-(o) corresponding extracted watermark

988 2014 IEEE International Advance Computing Conference (IACC)

Figure-13: (a)-(c) Watermarked images; (d)-(f) Rotation

attacked images and (g)-(i) corresponding extracted watermark Table-1: PSNR after a particular Attack

Types of Images PSNR of Lena

PSNR of Mandril

PSNR of Pepper

Gaussian Noise Attacked Images

17.352 16.867 16.987

Peppers & Salt Attacked Images

15.835 15.728 15.698

Corp Attacked Images 15.337 12.448 13.883 Shear Attacked Images 14.247 12.358 12.793 Mean Attacked Images 16.237 13.348 14.783 Median Attacked Images 15.837 13.748 13.983 Rotation Attacked Images 14.837 12.948 13.783

Robustness of the proposed algorithms can be understood by the help of MSE value of the extracted watermark shown in Table-2 and Figure-15. MSE of an image is defined as below-

(13) where, is the watermark image, is the

extracted watermark and are the dimensions of the images.

Figure-14: PSNR of Watermarked Image after a Particular

Attack Values shown in the Table-2 are the evident of better

results achieved by the proposed algorithms. Hence, the proposed process of embedding watermark and extracting watermark is providing better security for the commercial world.

Table-2: MSE of extracted watermark after a particular attack Types of Images MSE of

Lena MSE of Mandril

MSE of Pepper

Gaussian Noise Attacked Images 0.057373 0.058604 0.058296 Peppers & Salt Attacked Images 0.061346 0.061641 0.061724 Crop Attacked Images 0.062734 0.071798 0.067060 Shear Attacked Images 0.065936 0.072113 0.070611 Mean Attacked Images 0.060257 0.068766 0.064332 Median Attacked Images 0.061341 0.067484 0.066748 Rotation Attacked Images 0.064173 0.070088 0.067374

Figure-15: MSE for Extracted Watermark under Different

Attacks

VI. CONCLUSION The manuscript proposed an efficient approach for digital

watermarking by using DWT-SVD and BPNN. Whole process is divided into three (03) steps- first to embed the watermark image into the original image by DWT-SVD methodology. Second, perform various attacks over watermarked image and third, extract the watermark image by using BPNN. Here, BPNN is to be used to establish a relationship among the singular values of original image, attacked image and extracted watermark image. Experimental results of the approach represents that the quality of extracted watermark image is good enough to identify in terms of PSNR value. Finally, it is concluded that the proposed methodology produces good acceptable MSE values with enough robustness in case of various image attacks.

REFERENCES [1] S. Nikolaidis, A. Tsekeridou and V. Solachidis Tefas, “A

Survey on Watermarking Application Scenarios and Related Attacks,” IEEE international Conference on Image Processing, Vol. 3, pp. 991-993, Oct. 2001.

[2] Daxing Zhang, Zhigeng Pan and Haihua Li, “A Novel Watermarking Algorithm in DCT Domain to Authenticate Image Content,” IEEE International Conference on Intelligent Computing and Intelligent Systems, 2009, Vol.-3, pp.- 608–611, 20-22 Nov. 2009, Shanghai.

[3] S. D. Lin, S. C. Shie, and J. Y. Guo, “Improving the robustness of DCT based image watermarking against JPEG compression,” Computer Standards & Interfaces, Vol.-32, pp. 54–60, 2010.

2014 IEEE International Advance Computing Conference (IACC) 989

[4] Marco Corvi and Gianluca Nicchiotti, “Wavelet�based image watermarking for copyright protection,” Scandinavian Conference on Image Analysis SCI�97, Lappeenranta, Finland, June 1997.

[5] J. Cox , M. L.Miller and J. A.Bloom, “Digital Watermarking,” New York: Academic Press , 2002.

[6] R.A. Vahedi and M. Shiva Zoroofi, “Toward a new wavelet-based watermarking approach for color images using bio-inspired optimization principles,” Digital Signal Processing, Vol.-22, pp. 153–162, 2012.

[7] P. Duhamel, B. Piron, and J. M. Etcheto, “On computing the inverse DFT,” IEEE Trans. Acoust., Speech and Sig. Processing 36 (2): 285–286, 1988.

[8] Eftychios V. Papoulis and T. Stathald, “A DFT algorithm based on filter banks: the extended subband DFT,” Proceedings of the International Conference on Image Processing ICIP’03, Vol.-1, I - 1053-6, pp.-14-17, Sept. 2003.

[9] Haijun Li and Ran Fei, “An Efficient Algorithm of 10-Point DFT and its Applications,” International Conference on Electrical and Control Engineering (ICECE’10), pp.-25-27, pp.-4642 – 4645, June 2010, Wuhan

[10] Feng Liu and Yangguang Liu, “A Watermarking Algorithm for Digital Image Based on DCT and SVD”, 2008 Congress on Image and Signal Processing, pp.-380-383, 2008.

[11] R. Liu and T. Tan, “An SVD-Based Watermarking Scheme for Protecting Rightful Ownership,” IEEE Transactions on Multimedia, Vol. 4, No. 1 (2002), pp.121-128.

[12] L. Quan and A. Qingsong, “A Combination of DCT Based and SVD-Based Watermarking Scheme,” Proceedings of 7th International Conference on Signal Processing (ICSP04), Vol. 1, pp.873-876, 2004.

[13] R. C. Gonzolez, R. E. Woods, Digital Image Processing second edition. Prentice Hall, 2002.

[14] M. Petrou and P. Bosdogianni, Image Processing: The Fundamentals, John Wiley, 2000.

[15] B. Chandar Mohan, “A Robust Image Watermarking Scheme using SVD,” Journal of Multimedia, Vol.-3, pp.-7-15, 2008.

990 2014 IEEE International Advance Computing Conference (IACC)