A Digital Video Watermarking Technique Exploiting Entropy featureand DWT

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ITSI Transactions on Electrical and Electronics Engineering (ITSI-TEEE)

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ISSN (PRINT) : 2320–8945, Volume -6, Issue -1-2, 2018 39

A Digital Video Watermarking Technique Exploiting Entropy feature and DWT

1Baldip Kaur,²Amanpreet Singh Brar,³Anupama Gupta,⁴Jasdip Kaur

1,2Department of Computer Science and Engineering, GNDEC, Ludhiana

3Department of Computer Science and Engineering, LLRIET, Moga

4Department of Electronics and Communication Engineering, NWIET, Dhudike, Moga

Email:¹[email protected],²[email protected],³[email protected], ⁴[email protected]

Abstract—As the advancement in the field of technology and sharing of multi-media over the network is increasing at an alarming rate, there is an immediate concern needed to secure the data. Piracy of digital videos is one among all the isuues. Providing copyright protection, authenticity, etc to users can be used as a measure to overcome the problem.

Digital video watermarking is one such technique to provide copyright protection and authentication to the owner of the digital data. The paper proposes, an improved digital video watermarking technique which is combining the feature of entropy and Discrete Wavelet Transform (DWT). Entropy feature escalates the imperceptibility of embedded watermark, whereas DWT helps making the embedded watermark robust. The results obtained validate that the proposed technique can well survive under various attacks.

Index Terms— Discrete Wavelet Transform, Entropy, Image, Watermark

I. INTRODUCTION

In the trending years, prominent escalation in distribution of multimedia objects such as image file, audio file and video file over the network makes protection of digital data an issue of concern. This concludes into the compulsion of creating technologies which are intended towards the protection of digital data. Technique of digital watermarking is an innovative concept to explicate these concerns [1]. Watermarking is a clandestine security to catch frauds over copyrights. Digital video watermarking is defined as the phenomenon in which the information about the video itself or the information about the author of that video is embedded into the video [2]. The major challenges for video watermarking are imperceptibility, quality and security. The motivation of this research work emerges from the challenges to find the best watermarking solution underline.

II. RELATED WORK

Several algorithms for watermarking have been proposed in the past. Wang et al. [3] were the first to bring into picture a wavelet based watermark casting scheme. An adaptive watermark methodology was designed to determine wavelet sub-bands and then selecting a couple of certain significant wavelet sub-bands to embed the watermark on it. Lu and Liao presented a novel

multipurpose scheme of watermarking in which two watermarks, out of which one was robust and the other was fragile were embedded into the cover media [4]. The purpose of concurrent embedding of both robust and fragile watermarks was to provide copyright protection and data authentication, respectively. Ghazy et al. [5]

came forward with a block based digital watermarking technique which exploited mathematical technique of SVD, under which the original image was dissected into blocks and the watermark was embedded in singular values of each block separately. A detailed review on digital watermarking, its classification and implementation was presented by Rawat et al. [6].

Various watermarking techniques were explained in mainly three domains, namely, spatial domain, discrete cosine transform (DCT) domain and discrete wavelet transform domain. Basic algorithms for embedding and extraction of watermarks were explained in different domains using least significant bit (LSB) substitution were explained for spatial domain. Rajkumar et al. [7]

projected a Hadamard approach to digital image watermarking which was based on the concept of entropy.

Kashyap and Sinha [8] introduced the robust image watermarking technique for having the copyright protection based on 3-level DWT. Kaur and Jindal [9]

introduced the novel semi-blind (that needs some data for extraction) composite image watermarking technique based on SVD-DWT which was robust against all the attacks. A robust watermarking technique referred to as hybrid watermarking technique was developed to embed larger watermarks and to extract good quality watermarks by Akter et al. [10].

III. PRELIMINARIES

If This section presents a brief description of concepts of entropy, DWT and SVD which have been used in the proposed work.

A. Entropy

Entropy is a concept which was originally developed from the study of the physics of heat engines. It is a measure that describes the quantity of disorder in a system.

Another way of expressing entropy is to consider the various states which a system can adopt. Hence a low

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entropy system signifies a small number of such states, while a high entropy system will have a large number of states. It is a statistical measure of randomness that can be used to characterize the texture of the image which is given as input [11]. Let P contains the value of histogram counts. The entropy value is calculated as follows [7]:





 P P

Entropy log

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B. Discrete Wavelet Transform

Discrete Wavelet transform (DWT) is a mathematical tool implied for hierarchically decomposing an image. It is useful when it comes to processing of non-stationary signals. Working of this transform is based on small waves, called wavelets [12], which have varying frequency and limited duration. Wavelet transform offers both frequency and spatial description of an image.

Wavelet transform offers both frequency and spatial description of an image. Wavelets are created by applying translations and dilations of a fixed function called mother wavelet. DWT is the multi-resolution representation of an image. Kundur and Hatzinakoes [13] specified that in 2-dimensional applications, for each level of decomposition, the DWT is first performed in the vertical direction, followed by the horizontal direction. After the first level of decomposition, there are four sub-bands:

LL1, LH1, HL1, and HH1.`

IV. PROPOSED METHODOLOGY

The proposed work fuses the concept of entropy and mathematical functions DWT. The proposed algorithm works in two stages, which are embedding and second one is extraction. In embedding phase, firstly the cover video and the image to be embedded as watermark are selected.

Then, the original video is divided into frames and out of all frames, a random frame (let it be named as F_C) is selected and other frames which are not selected are saved for future use. The randomly selected frame is further divided into three layers (RGB) and watermark is embedded in one of its layer. To pick out the layer for embedding watermark, the comparison of entropy is done for each layer. The layer with maximum value of entropy is nominated for embedding the watermark and marked as L_C. On the other hand, layers with lesser entropy values are saved as such and marked as unchanged layers L_UC. After selecting the particular layer, the image to be embedded as watermark is made suitable for the purpose of embedding into the selected layer by applying resizing.

The watermark is subjected to DWT to get LL_W, LH_W, HL_Wand HH_W. Simultaneously, the DWT is applied on selected layer to get four details, i.e., LL2, LH2, HL2 and HH2. Then, HH2 is chosen for embedding the watermark.

After this phase, the actual embedding takes place. The matrices HHw and HH2 are added to get wmkdHH. Then,

to form the watermarked layer, inverse DWT is applied on LL2, LH2, HL2 and wmkdHH. After this, all the unchanged layers are combined with watermarked layer to get frame and in the end watermarked frame is rearranged with unchanged frames to form the watermarked video.

The extraction of the embedded watermark can be done by following the same process in reverse order.

V. EXPERIMENTAL RESULTS

The proposed algorithm has been implemented in matlabR2012a. Two test videos namely “fruits.avi” and

“astronaut.avi” (Fig. 4a and 4b) are chosen to evaluate the proposed methodology. The watermark images to be embedded into the videos are “lena.png” and

“images.png” (Fig. 4c and Fig. 4d).

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Figure 4: Test videos: (a) Fruits.avi, (b) Astronaut.avi, Watermarks: (c) Lena.png and (d) Images.jpg

VI. QUANTIFIABLE VALIDATION OF PROPOSED ALGORITHM

The quantifiable validation of proposed work is carried out using two performance metrics, which are, peak signal to noise ratio (PSNR) and correlation coefficient (CC).

Peak signal to noise ratio is defined as a metric which calculates the difference occurred between original media and watermarked media. It evaluates the image and is measured in unit called as decibels, (db). The PSNR [14]

can be calculated using (2).

 

 



 

 MSE

PSNR 255 255 10

log

10

(2)

In (2) MSE stands for mean square error and calculates the mean value of the square of the value calculated by difference in pixel values of two images(media).

The next metric, Correlation coefficient whose value falls between 0 to 1 represents how similar the two images of

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same size are. The best value of CC is 1, which means that the images are exactly similar. CC is calculated using mathematical equation (3) where C is cover image and S is watermark. M1 and M2 represents the mean pixel values of C and S, respectively.

 

     

 

     



^



^



 

2

2 , 2

1 ,

2 ,

1 ,

M j i S M

j i C

M j i S M j i

CC C ⁽³⁾

Table I and II shows the values of PSNR and CC calculated for comparison of original and watermarked images for both the test videos.

Table I. Evaluation of PSNR and CC for original frame and watermarked frame for “fruits.avi” having 99 frames

File Name ^Watermark Image

Fram

e No. PSNR CC Fruits.avi Lena.png 81 68.8702 0.9977 Fruits.avi Lena.png 13 67.8069 0.9980 Fruits.avi Images

.jpg 95 71.7832 0.9974

Fruits.avi Images

.jpg 4 73.1374 0.9985

Table II. Evaluation of PSNR and CC for original frame and watermarked frame for “Astronaut.avi” having 200 frames

File Name ^Watermark Image

Frame

No. PSNR CC

Astronaut.avi Lena.png 50 78.8576 0.9993

Astronaut.avi Lena.png 20 81.1288 0.9994

Astronaut.avi Images.jpg 21 79.0740 0.9994 Astronaut.avi Images.jpg 56 75.9762 0.9993 To check the robustness of proposed algorithm, the watermarked video is subjected to various geometrical and non-geometrical attacks. After this, the values of PSNR and CC are recomputed and listed in Table III.

Table III. Evaluation of PSNR and CC for orignal and watermarked frame.

Attacks PSNR CC

No Attack 40.6270 0.9975

Gaussian (0.001) 40.3729 0.9974 Salt & Pepper (0.001) 40.3181 0.9973

Speckle Noise 40.3644 0.9974

Poisson Noise 40.3662 0.9974

Median Filter 39.4461 0.8911

Cropping 10.1213 0.1267

VII. VISUAL EVALUATION OF PROPOSED WORK

The proposed algorithm is qualitatively tested using visual perception technique. The results of qualitative analysis are listed in Table IV. Qualitative analysis is done on videos mentioned in Fig.

Total no. of frames in Fruits.avi=99 Total no. of frames in Astronaut.avi=200

Table IV. Comparison of original frame with watermarked frame of test videos (“fruits.avi”=99frames and “astronaut.avi”=200frames)

Video File

Frame

No. Original frame Watermarked frame

Fruits.

avi 81

Astron aut.avi 193

Results listed in Table IV also verify that proposed algorithm has achieved the feature of imperceptibility of embedded watermark.

The visual inspection of extracted watermarks has also been done for the purpose of qualitative validation and the results are shown in Table V. It is clear that the extracted watermark images survive under various non-geometrical attacks like noises and filtering but not under geometrical attacks like cropping.

Table V. Comparison of original watermark with extracted watermark under various attacks

Attack Original watermark image

Extracted watermark

No attack

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Gaussian noise (0.001)

Filtering

Cropping

VIII. CONCLUSIONS

The proposed approach is successful in achieving desired properties of imperceptibility, robustness and security.

The robustness of embedded watermark shown in Table V is achieved due to working in transform domain instead of spatial domain. Imperceptibility of watermark is the result of embedding it in the highest entropy layer only.

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