[ieee 2013 fourth international conference on computing, communications and networking technologies...

Improved Modified Fast Haar Wavelet

Transformation [MFHWT] based Visible

Watermarking

Shefaly Sharma

Computer science and Engineering

Lovely Professional University

Phagwara, Punjab

Jaspreet Kaur

Computers Science and Engineering


Phagwara, Punjab

Shipra Gupta

Computer Science and Engineering


Phagwara, Punjab

[email protected] [email protected] [email protected]

Abstract— With the explosive growth of internet technology,

many innovative applications requiring exchange of large

amount of data have become feasible and hence security of data

have become a critical issue. In this paper, a robust and secure

visible image watermarking algorithm is purposed. This

purposed algorithm is based on Multi Wavelets with Modified

fast Haar Wavelet transform [MFHWT]. MFHWT is the latest

technique used for wavelets transformation which is used to

perform image analysis at faster rate than previous techniques.

The main idea of the purposed algorithm is to decompose the

original image into fine scale sub bands using MFHWT

according to the size of the Watermark. In this algorithm, the

obtained watermarked image. In this algorithm has very high

Peak Signal to Noise Ratio [PSNR].

Keywords- PSNR, MFHWT, MSE, Image Processing, Digital

Watermark.

I. INTRODUCTION

We use digital data for capturing an image, and this digital equipment is used to convert the image into set of numbers called pixels. This process is called Digitization or Scanning. We can store, modify and view images, for performing these functions we make use of various machines out of which computer is the most popular.[3] Digital image is stored in digital machine in form of smallest particles called pixels. These pixels are able to store color values in it. Each pixel represent a sample of an original image. If there will be more samples then they will give us more accurate representation of image. If we have colored image system, then in that case color will be represented by three color component intensities Red, Green, Blue which are primary colors, or cyan, magenta, yellow and black which are secondary colors.[2]

A. Watermarking:

A lot of advancements are being done in the field of multimedia. So due to these advancements there is increase in threats in data authentication, its licensed use and protection from illegal use of data. Watermarking provides us copyright protection. There are two types of Watermarking algorithm: visible Watermarking and Invisible Watermarking.[2] For Visible Watermarking, the watermark should be visible and

robust. For Invisible Watermarking, the watermark should be transparent and robust.

B. Techniques of Watermarking:

Techniques of Digital Watermarking can be described in four different ways:

1. Blind versus Non Blind:

If a technique does not require original image to recover the watermark then it is said to be Blind but if a technique require original image to recover Watermark then it is said to be Non Blind. [1]

2. Perceptible versus Imperceptible:

If the embedded watermark is visible to everyone then this watermarking technique is called Perceptible but if the embedded watermark is not visible to then it is called Imperceptible. [1]

3. Robust versus Fragile:

If a watermarking technique protects the embedded watermark from variation resulting from cropping, filtering, compression etc. then this technique is called as Robust but if the watermark is altered or destroyed by slight modification then it is called as Fragile. [1]

4. Spatial Domain versus Frequency Domain:

In spatial domain, image is represented or processed in the form of matrix, and matrix consist of pixel values in (x, y) representing an image. In this case, change in pixel value corresponds to change in scene. In case of Frequency domain, image is processed in form of sub bands. All types of transformations are applied in frequency domain. Eg DWT, DFT, HAAR etc. change in image corresponds to change in spatial frequency. [1]

C. Watermarking Models:

There are different ways by which we can model a Watermarking process. These can be classified into two groups. [4]

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4th ICCCNT 2013 July 4-6, 2013, Tiruchengode, India

1. Communication Based Model:

Watermarking is a process of communicating a message from sender to receiver. Therefore there is a need to use a model ehich provide secure communication. So for this purpose we use communication based model. In this model, we have sender at one side which will encode message using encoding key to prevent intruder to decode the message. Then message would be transmitted on a communication channel, which would add some noise to the encoded message. Then resulting noisy message would be received by receiver using a decoding key.[4]

noise

Output

Input message

Message

Encoding key decoding key

Figure 1. Communication based model[4]

2. Geometric Model:

In this model, Images can be viewed as high dimensional vectors in media space. A 512 X 512 image can be described as 262144 element vector in a 262144 dimensional space.

Geometric model helps us in visualizing the watermarking process using number of regions like embedding region, which contains all possible images resulting from embedding a message inside unwatermarked image using some algorithm and detection region, which contain all possible images from which watermark can be successfully extracted using algorithm.[4]

Figure 2. Embedding and Detection Region[4]

D. Attacks On Watermark:

There are many attacks that affect the efficiency of

Watermarking. These attacks are listed below:

1. Subtractive Attack: In this attack the adversary or

malicious user tries to detect the presence and

location of the watermark and tries to extract it from

the host. An effective subtractive attack is one where

the cropped object has retained enough original

content to still be of value. [5]

2. Distortive Attack: If an adversary or malicious user

applies some distortive transformation uniformly

over the object in order to degrade the watermark so

that it becomes undetectable/unreadable. An effective

distortive attack is one where one can no longer

detect the degraded watermark, but the degraded

object still has value to the adversary. [5]

3. Additive Attack: An adversary or malicious user can

augment host by inserting his own watermark W (or

several such marks).An effective additive attack is

one in which adversary's mark completely overrides

original mark, so that it can no longer be extracted or

it is impossible to detect that the original mark

temporally precedes the adversary's mark. [5]

4. Filtering: Low-pass filtering, for instance, does not

introduce considerable degradation in watermarked

images, videos or audio, but can dramatically affect

the performance, since spread-spectrum-like

watermarks have non negligible high-frequency

spectral contents. [5]

5. Compression: This is generally an unintentional

attack which appears very often in multimedia

applications. Practically all the audio, video and

images that are currently being distributed via

Internet have been compressed. If the watermark is

required to resist different levels of compression, it is

usually advisable to perform the watermark insertion

task in the same domain where the compression takes

place. For instance, DCT domain image

watermarking is more robust to JPEG compression

than spatial domain watermarking. [5] 6. Rotation and Scaling: It has been very successful

with still images. Correlation based detection and

extraction fail when rotation or scaling is performed

on the watermarked image because the embedded

watermark and the locally generated version do not

share the same spatial pattern anymore. Obviously, it

would be possible to do exhaustive search on

different rotation angles and scaling factors until a

correlation peak is found, but this is prohibitively

complex. [5]

E. Haar Wavelet Transformation:

Haar transformation performs average and

differentiation functions for deleting data, sorting

cofficients, reconstruc-ting the matrix such that the

final matrix is similar to the initial matrix.

Encoder Decoder +

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II. PURPOSED SCHEME

Step 1: First of all, we will decompose the original image

repetadily using Modified fast Haar Wavelet Transformation

until a decomposition level is reached in which the size of

each sub-image is four times the size of the watermark. The

number of decomposition levels is determined according to

the size of the watermark and is given by the following

equation:

L= 2 2

21/ 2log / 1M N (1)

where MxM is the size of the original image and N xN is the

size of the watermark.

Step 2: Then we will apply Modified fast Haar Wavelet

Transform (MFHWT) to the sub-images of the last

decomposition level of the MFHWT. For example, if the

original image and the watermark are 512 x 512 pixels and

64 x 64 pixels respectively, then a sub-image is of size

128x 128, which is four times as large as the size of the

watermark. Therefore, the MFHWT decomposition is applied

to the fine-scale sub-images.

Figure 3. Decomposition of Image into Sub bands

Step 3: After that for each pixel a Parameter P is computed

where P is the minimum number of bits needed to represent

the gray level value of the pixel. For example, if the gray level

value of a pixel is five, then P=3.

Step 4: The value of each pixel is divided into 3 parts X, Y

and Z which corresponds to the most, middle and least

significant parts respectively. Let PX, PY and PZ be the number

of bits of X, Y and Z respectively. They are computed as PX =

PY = [ T/3] and PZ = P - PA - PB. For example: if the gray-scale

value of a pixel =" 10100101", then P=8, PX= PY=3, PZ=2,

X=5, Y=1 and Z=l.

Step 5: The sub-images S1,2 , S1,3 and S1,4 are decomposed into

4 bands, each sub-image is the same size as the watermark

where X, Y and Z are embedded in the S1,2, S1,3 and S1,4

coefficients.

6: Calculate PSNR for Watermarked image.

PSNR computes Peak Signal to Noise Ratio, in decibels,

between two images. This ratio is used to provide the quality

measurement between two resultant fused images. Efficiency

of particular technique is calculated by calculating

PSNRvalue. Higher the PSNR more will be the quality.

PSNR = 2

1010log 255 / MSE (2)

Where MSE is calculated as:

MSE =

1

0

,

, 0

12([ ( , ) ( , )] / *

M N

i j

OI i j DI i j M N

(3)

Where OI is the original image with , DI is watermarked

image, M*N is number of pixels. More the value of MSE

lower the value of PSNR.

III. EXPERIMENTAL RESULTS

Figure 4. GUI Interface for Watermarking

Figure 5. Effect of Salt and Pepper noise

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Figure 6. Effect of Poisson Noise

Figure 7. Effect of Speckle Noise

Method PSNR of watermarked Lena

image (db)

Proposed Method

73.5008

Method of [1]

52.16

Method of [2]

43.3374

Method of [3]

48.20

Table 1. PSNR of proposed method and other methods

Position Of Watermark Watermarked Image PSNR Value

Left Top

74.8377

Right Top

73.5008

Left Bottom

73.9944

Right Bottom

74.1657

Center

74.1463

IV CONCLUSION

We have implemented this technique with MFHWT. User can

change the position of Watermark by choosing the location,

and hence can secure the image from cropping attack.

Moreover it can also withstand various attacks like- Salt and

Pepper attack,Poission attack, Speckle attack

V FUTURE SCOPE

In future, some schemes can be purposed that can support big

files.In future we can also implement this technique with

audio and video.

REFERENCES

[1] Saaid Mary, Nossair Zaki and Hanna Magdy,(2011) “An image watermarking scheme based on Multiresolution Analysi,”IEEE

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[2] Gupta Akshya, Raval Mehul,(2012) “ A Robust and Secure Watermarking scheme based on singular values replacement,”Indian academy of sciences,vol. 37,pp-425-440

[3] Luo Lixin, Chen Zhenyong, Chen Ming, Zeng Xiao and Xiong Zhang,(2010) “ Reversible image Watermarking using Interpolation Technique,”IEEE, Vol. 5,pp-187-193

[4] Sharma Shefaly, Kaur Jagpreet(2013), “ An efficient method of watermarking using Multi Wavelet technique with modified fast Haar Wavelet transform [MFHWT] ”, IJITEE, Vol-4,Issue-2.

[5] Sharma Shefaly, Kaur Jagpreet (2013), “ A Robust and Secure Visible Watermarking Scheme based on Multi Wavelet technique with Modified Fast Haar Transform [MFHWT]”, IJARET, Vol-4, Issue-2, pp.240-247.

[6] Ali Rashid, Bhardwaj Anuj ,2009, “Image compression using modified fast haar wavelet transform, “world applied sciences journal.

[7] Damien Adams, Patterson Hasley, 2006, ‘The Haar Wavelet Transform: compression and Reconstruction’.

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