Comparison of Wavelet Watermarking Method With & without Estimator Approach

IJSRD - International Journal for Scientific Research & Development| Vol. 1, Issue 3, 2013 | ISSN (online): 2321-0613

Comparison of Wavelet Watermarking Method With & without Estimator Approach Neha Y. Joshi1 Kavindra R. Jain2 Pratyaksh A Maru3 1 Research Scholar 2 Assistant Professor, G. H. Patel College of Engg. and Tech., Vallabh Vidyanagar, India 3 Assistant Professor, Dr. Jivraj Mehta Institute of Tech, Mogar, India Abstract — In this paper we propose an Estimator approach with wavelet watermarking method which is capable to hide watermark in the host image based on wavelet domain technique. Using the Estimator approach the proposed technique becomes robust against different noise attacks.

For the evaluation of Imperceptibility & Robustness of the proposed method we have calculated basic statistical parameters. We have tested watermarked image against different noise attacks at different noise densities. Due to the use of estimator the perceptible quality of extracted image is better though the image is degraded by high density noise.

been divided into V sections. Section II comprises of problem definition & solution for the same to extract the digital watermarked image. In section III (A) the various Estimators to make the method robust against noisy attack.

We being discussed in Section III (B) the Statistical parameters evaluated to check the quality of extracted watermark image. Section IV proposes various evaluation parameters being measured on various images & Result of applied approach. In our last section we conclude our paper by enhancing the result of wavelet method against noise attack using estimator technique.

I. INTRODUCTION

II. PROBLEM DEFINATION

Nowadays technologies are changing so to use digital watermarking instead of the age old techniques for hiding information & protecting multimedia data has become the greatest area of interest. This makes the availability of digital information in the form of audio, video & images are quiet easily and immensely available and in reach to public via web. The main concern in such techniques is the robustness & imperceptibility.

The watermarking can be applied to the multimedia data like Images, text, audio & video. Watermarking technique mainly contains two processes embedding & extracting for that different types of watermark & various watermarking techniques are available. According to the properties of watermark it can be divided into two main categories visible & invisible watermark[2]. Human audio & video system means Human visual perception decides the properties of watermark. Watermarking technique can also be classified based on the extraction process. According to it, we can divide it into three types Blind, Semi-Blind & Non-Blind. Techniques which do not require the original data or signal fall into the first category which is blind.

Techniques which require the original watermark are considered as a Semi-Blind watermarking & techniques which require the original signal for extraction process is the Non-Blind technique. According to the domain we can also classified watermarking as a spatial & Frequency domain. In frequency domain we cannot directly embed watermark for that first we have to convert original signal & watermark into frequency domain using different transforms while in spatial domain we can directly apply watermark by changing pixel values or by using spread spectrum approach [3]. The past researches suggest that compare to spatial domain; frequency domain watermarking is more robust against different attacks. To achieve data protection the watermarking technique should be imperceptible with high level of security i.e. watermarked image should not reveal any things about hiding information. The whole paper has

As seen watermarking technique has emerge as a solution to the problem of copying the digital content. Recently so many watermarking schemes have been developed in the image domain. There are different ways to watermark image like spatial domain & using different transform in frequency domain [4].

The basic problem of Spatial domain method is that it could not resist the simple noisy attack while the wavelet method of watermarking is capable to resist the noise attack but if the image is degraded by high density noise then this method fails to extract the actual watermark. So that is why we have applied estimator approach to make this method robust against noise attacks. Here we have applied Mestimator to remove the effect of different noises at the extraction part.

The basic flow of our algorithm is shown in below fig.1

Images to Waterma rk

Wavelet method of Waterma rking

Apply Noise Attack on Waterma rked Image

Extract the watermar k image & Apply Estimator on result

Fig. 1: Proposed approach Basically estimator estimates the data & fit a line which effectively rejects the outlier & makes the system robust against outlier. In our case the outlier is noise so that if we apply estimator after extraction process then it estimates the noise pixels & effectively rejects it. As a result the perceptual quality of watermark becomes better. After applying estimator we have evaluated our scheme using different quality attributes. We have also compared these results with the result of simple spatial embedding method.

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Comparison of Wavelet Watermarking Method With & without Estimator Approach (IJSRD/Vol. 1/Issue 3/2013/0017)

III. MATERIALS & METHODS In our proposed algorithm, hiding of watermark in cover image has been done using spatial domain with & without estimator. For that we have chosen different grey scale images which are shown below:

(a)

(b)

(c)

4 5

Sr. No. 1 2 2 3 4

(d)

(e)

(d)

Fig. 2. (a) Cell (used as a Watermark Image) and (b) Moon, (c) Circuit, (d) Bag, (e) Cameraman, (f) Mandi ((b),(c),(d),(e) used as a Cover Image)

images.

Select the lowest frequency component of cover Image & Highest frequency component of watermark.

Reconstruct the image using above specified component.

Table. 1: Embedding Process Steps First take the watermarked image.

Apply different noise attack on watermarked image.

Perform Wavelet Decomposition on watermarked image.

Select the lowest frequency component of original watermark & higher frequency component of watermarked image.

Reconstruct the image using above specified component.

5

Apply Estimator on extracted watermark image.

6

Calculate the statistical parameters.

Table. 2: Extraction Process

IV. ESTIMATOR

V. STATISTICAL PARAMETERS

Estimator basically works as a filter but the advantage of estimator is that it gives filtering result with preservation of fine detail. Normally, the available denoizing filter blurs the image after filtering. From the available estimators we have used M estimator. The robustness of any estimator depends on two parameters: Influence Function & Breakdown Point [5]. Influence Function gives the change in an estimate caused by insertion of outlying data and Breakdown Point is the largest percentage of outlier data points that will not cause a deviation in the solution. So the robustness of any estimator depends on these two parameters. The outlier in our case is noise attack. M estimator effectively rejects the outlier so that we can use it to remove from the extracted watermark without knowing the noise density. We have here used M estimator cauchy function because it is effectively rejects the noise. The Robustness of any estimator is defined by the above explained two parameters. We have applied M estimator on the extracted watermark & the given result shows the same. To embed watermark we have used haar wavelet transform based on the past research. Steps of the propose algorithm for the process of watermark embedding is mention in table1 & for extraction of the same is in table 2.In the extraction method we have applied estimator to reject the effect of Noise. We have here described the algorithm with Estimator approach.

To measure the imperceptibility & robustness of any watermarking technique PSNR & MSE are the two major parameters.

MSE: Mean Square Error & Root Mean Square Error is usually used to measure perceptual quality of image. It finds error between watermarked image and the one without watermark .

Sr. No. 1 2 3

Steps Select one grey scale image as a Cover image.

Take one watermark image of same size.

Perform 2-level wavelet decomposition on both the

∑∑

)

))

)

PSNR: Peak Signal To Noise Ratio usually used to measure the imperceptibility of watermarking method. It gives the measure of invisibility of watermark in the original signal. (

)

)

This attributes are based on the objective criteria. So it is necessary to check same technique on different objects to measure the perfect range or value.  Correlation Coefficient: It gives the correlation between original watermark & extracted watermark.

This attributes are based on the objective criteria. So it is necessary to check same technique on different objects to measure the perfect range or value.

VI. RESULTS & ANALYSIS To check the fidelity of outcomes the proposed approach is applied on four different grey scale cover images & the attributes so calculated are shown in table 3. The result of

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469

Comparison of Wavelet Watermarking Method With & without Estimator Approach (IJSRD/Vol. 1/Issue 3/2013/0017)

watermark embedding & extraction on the images are shown below in table 4 Figure 1 shows four different grey cover images & watermark image. Figure 2 shows Images after Embedding Watermark.

Noise with Density

Salt & pepper 0.05 Without Estimator

Salt & pepper 0.05 With Estimator

Cameraman.tif

0.9786

0.9863

Circuit.tif

0.9799

0.9772

Moon.tif

0.9760

0.9822

Mandi.tif

0.9787

0.9831

Bag.tif

0.9771

0.9900

Table 3.(c) Correlation coefficient based comparison Cover Image

Salt & Pepper Noise(density 0.05)

Watermark Image

Watermarked Image

Extracted Watermark Without Estimator Approach

Extracted Watermark With Estimator Approach

Table.3 : Comparison of Extracted Watermark with & without Estimator

After applying estimator we have compare the two results of applied approach by calculating different statistical parameters. We have used three different noises as attack.

Table 3 Comparison Based on Statistical Parameters  Salt & Pepper noise: Noise with Density

Salt & pepper 0.05 Without Estimator

Salt & pepper 0.05 With Estimator

Cameraman.tif

626.2221

391.6110

Circuit.tif

585.0728

675.1084

Moon.tif

701.8396

513.7373

Mandi.tif

622.4515

488.6302

Bag.tif

669.9769

281.9712



Gaussian Noise: Noise with Density

Salt & pepper 0.05 Without Estimator

Salt & pepper 0.05 With Estimator

Cameraman.tif

1.6909e+003

Circuit.tif

1.5501e+003

381.4452

Moon.tif

1.0345e+003

373.6339

Mandi.tif

1.5656e+003

611.6894

Bag.tif

1.5134e+003

444.7307

386.2296

Table 3.(d) Mse based Comparison Salt & pepper 0.05 Without Estimator 31.6992 32.4543 35.9669 32.3680 32.6627

Salt & pepper 0.05 With Estimator 44.5247 44.6330 44.8127 40.5310 43.2997

Noise with Density

Salt & pepper 0.05 Without Estimator

Salt & pepper 0.05 With Estimator

Cameraman.tif

0.9453

0.9864

Circuit.tif

0.9495

0.9865

Moon.tif

0.9654

0.9868

Mandi.tif

0.9490

0.9792

Bag.tif

0.9506

0.9843

Noise with Density Cameraman.tif Circuit.tif Moon.tif Mandi.tif Bag.tif

Table 3.(e)PSNR based comparison

Table 3.(a) Mse based comparison Noise with Density

Salt & pepper 0.05 Without Estimator

Salt & pepper 0.05 With Estimator

Cameraman.tif

40.3270

44.4045

Circuit.tif

40.9174

39.6741

Moon.tif

39.3368

42.0468

Mandi.tif

40.3795

42.4820

Bag.tif

39.7404

47.2575

Table 3.(b) PSNR based comparison

Table3(f) Correlation coefficient based comparison Speckle Noise Noise with Density

Salt & pepper 0.05 Without Estimator

Salt & pepper 0.05 With Estimator

Cameraman.tif

635.8647

484.1536

Circuit.tif

272.2698

248.2074

Moon.tif

253.4473

246.8898

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470

Comparison of Wavelet Watermarking Method With & without Estimator Approach (IJSRD/Vol. 1/Issue 3/2013/0017)

244.0666

265.7713

Bag.tif

437.7578

355.6137

Table 3.(g) Mse based comparison Noise with Density

Salt & pepper 0.05 Without Estimator

Salt & pepper 0.05 With Estimator

Cameraman.tif

40.1943

42.5619

Circuit.tif

47.5616

48.3653

Moon.tif

48.1839

48.4115

Mandi.tif

48.5114

47.7714

Bag.tif

43.4369

45.2420

Correlation Coefficient: Corr. Coef.

Mandi.tif

1 0.95 0.9

Without Estimator With Estimator Test Images

Fig. 3: Charts for different variable and comparison VII. CONCLUSION

Table 3.(b)PSNR based Comparison Noise with Density Cameraman.tif Circuit.tif Moon.tif Mandi.tif Bag.tif

Salt & pepper 0.05 Without Estimator 0.9783 0.9905 0.9910 0.9915 0.9849

Salt & pepper 0.05 With Estimator 0.9834 0.9912 0.9914 0.9906 0.9874

Table 3.(c)Correlation based comparison

Plot for the above tables are as shown below:  Salt & pepper noise:

MSE

MSE: 800 600 400 200 0

Without Estimator With Estimator

Test Images

PSNR

PSNR: 50 40 30 20 10 0

Without Estimator With Estimator

Test Images

From the above results we conclude that by adding the estimator step at the extraction part gives better results compare to without estimator. From the given result we can observe that MSE decreases and PSNR increases. The value of correlation coefficient shows the correlation between extracted images with M estimator is higher compare to simple WAVELET method.

ACKNOWLEDGEMENT We are very thankful to Dr. Chintan Modi & Mr. Pratyaksh Maru for providing support and their knowledge of robust estimators. We are also thankful to our institute G.H.Patel college of Engg. & Technology, for providing the necessary platform for our research.

REFERENCES [1] Frank Hartung, Martin Kutter, “Multimedia Watermarking Techniques”, Proceedings of The IEEE, Vol. 87, No. 7, pp. 1085 – 1103, July 1999.

[2] Ajit kulkarni, Digital watermarking”,American Journal of computer & Multimedia, conference’04, Chicago.

[3] Mei Jiansheng, Li Sukang and Tan Xiaomei, “A Digital Watermarking Algorithm Based On DCT and DWT” , International Symposium on Web Information Systems and Applications (WISA’09) ,Nanchang, P. R. China, ISBN 978-952-5726-00-8, May 22-24, 2009, pp. 104107, Proceedings of the 2009.

[4] Chandra Mohan B and Srinivas Kumar S , “Robust Multiple Image Watermarking Scheme using Discrete Cosine Transform with Multiple Descriptions” International Journal of Computer Theory and Engineering, ISSN: 1793-8201, December, 2009.

[5] Jayesh D. Chauhan, et. al., “Robust M estimator for surface roughness estimation using Machine vision”, International Conference on Advances in Mechanical Engineering (ICAME), SVNIT, Surat, August 3-5, 2009.

[6] Dr.B.EswaraReddy, P. Harini, S. Maruthu Perumal & Dr. V. Vijaya Kumar: “A New Wavelet Based Digital Watermarking Method for Authenticated Mobile Signals International Journal of Image Processing (IJIP), Volume 5, Issue 1, 2011.

[7] DineshKumar and Vijay Kumar, “Contourlet Transform Based Watermarking For Colour Images” The International Journal of Multimedia & Its Applications (IJMA), ISSN : 0975 – 5578, February 2011.

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