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1. INTRODUCTION

The protection of copyrights of digital-image content has become more important

because increasingly more digital image content is being distributed throughout the Internet and

it can be copied exactly the same as that of the original because it is digital. Digital watermarking

is an effective way of protecting copyrights from being illegally copied. Various techniques of

digital watermarking for digital images have been developed. Digital watermarking has also been

recently used in printed images, where digital watermarking is embedded in the digital data

before it is printed. This is to prevent images copied by digital cameras or scanners from being

illegally used.

However, whether digital watermarking is in the displayed image on an electronic display

or on a printed image, conventional digital watermarking rests on the premise that people who

want to protect the copyrights of their content have the original digital data because it has been

embedded by digital processing. However, there are some cases where this premise does not

apply. One such case can arise for images that have been illegally produced by people taking

photographs of real objects that are invaluable as portraits, e.g., art works at museums that have

been painted by a famous artists or faces of celebrities on a stage. The images produced by

malicious people capturing these real objects with digital cameras or other image-input devices

have been vulnerable to illegal use since they have not contained digital watermarking. So a new

technique proposed for protecting the famous paintings and sculptures in museums etc, by using

Optical Watermarking. This optical watermarking technique provides better protection of the

images or pictures.

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2 .LITERATURE SURVEY

Before going in detail with watermarking procedure etc., let’s have a brief knowledge on image.

2.1 Converting image into digital image

Any image in the world if wants to be processed it should be converted into a digital

image; the conversion of the natural or still image to digital content is only possible with the

digital camera. Now a days digital camera or camera is a part of life which is playing a

omnipotent role in its kind, capturing each and every moment of life and storing in a micro sd

card, probable trending to the latest technologies based on the cameras resolution the image is

being saved in only just size of KB’s by this large number of pictures are stored in the sd card.

Making camera more reliable and sophisticated, let’s see the basic structure of digital camera.

Fig 2.1. Converting Image into Digital Image

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Representation of Digital Images:

An image may be defined as a two-dimensional function of f (x, y), where x and y are

spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the

intensity or gray level of the image at that point. When x, y, and the intensity values of f are all

finite, discrete quantities, we call the image a digital image. The field of digital image processing

refers to processing digital images by means of a digital computer. Note that a digital image is

composed of a finite number of elements, each of which has a particular location and value.

These elements are called picture elements, image elements and pixels. Pixel is the term used

most widely to denote the elements of a digital image

Types of Digital Images:

For photographic purposes, there are two important types of digital images-color and

black and white. Color images are made up of colored pixels while black and white images are

made of pixels in different shades of gray.

Black and White Images

A black and white image is made up of pixels each of which holds a single number

corresponding to the gray level of the image at a particular location. These gray levels span the

full range from black to white in a series of very fine steps, normally 256 different grays. Since

the eye can barely distinguish about 200 different gray levels, Assuming 256 gray levels, each

black and white pixel can be stored in a single byte (8 bits) of memory.

Color Images

A color image is made up of pixels each of which holds three numbers corresponding to

the red, green, and blue levels of the image at a particular location. Red, green, and blue

(sometimes referred to as RGB) are the primary colors for mixing light—these so-called additive

primary colors are different from the subtractive primary colors used for mixing paints (cyan,

magenta, and yellow). Any color can be created by mixing the correct amounts of red, green, and

blue light. Assuming 256 levels for each primary, each color pixel can be stored in three bytes

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(24 bits) of memory. This corresponds to roughly 16.7 million different possible colors. Note

that for images of the same size, a black and white version will use three times less memory than

a color version.

2.2 Image Sampling and Quantization

From the discussion in the preceding section, we see that there are numerous ways to

acquire images, but our objective in all is the same: to generate digital images from sensed data.

The output of most sensors is a continuous voltage waveform whose amplitude and spatial

behavior are related to the physical phenomenon being sensed. To create a digital image, we

need to convert the continuous sensed data into digital form. This involves two processes:

sampling and quantization.

Basic Concepts in Sampling and Quantization

The basic idea behind sampling and quantization is illustrated in Fig. Below which shows

a continuous image f that we want to convert to digital form. An image may be continuous with

respect to the x- and y-coordinates, and also in amplitude. To convert it to digital form, we have

to sample the function in both coordinates and in amplitude. Digitizing the coordinate values is

called sampling. Digitizing the amplitude values is called quantization.

The one-dimensional function in Fig. 2.2.(b) is a plot of amplitude (intensity level) values

of the continuous image along the line segment AB in Fig. 2.2.(a). The random variations are due

to image noise. To sample this function, we take equally spaced samples along line AB, as shown

in Fig. 2.2.(c).The spatial location of each sample is indicated by a vertical tick mark in the

bottom part of the figure. The samples are shown as small white squares superimposed on the

function. The set of these discrete locations gives the sampled function. However, the values of

the samples still span (vertically) a continuous range of intensity values. In order to form a

digital function, the intensity values also must be converted (quantized) into discrete quantities.

The right side of Fig. 2.2.(c) shows the intensity scale divided into eight discrete intervals,

ranging from black to white. The vertical tick marks indicate the specific value assigned to each

of the eight intensity intervals. The continuous intensity levels are quantized by assigning one of

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the eight values to each sample. The assignment is made depending on the vertical proximity of a

sample to a vertical tick mark. The digital samples resulting from both sampling and quantization

are shown in Fig. 2.2.(d). Starting at the top of the image and carrying out this procedure line by

line produces a two-dimensional digital image. It is implied in Fig. 2.2. that, in addition to the

number of discrete levels used, the accuracy achieved in quantization is highly dependent on the

noise content of the sampled signal. Sampling in the manner just described assumes that we have

a continuous image in both coordinate directions as well as in amplitude.

Fig 2.2. Generating a digital image.(a) Continuous image. (b) A scan line from A to B in the continuous image,

used to illustrate the concepts of sampling and quantization. (c) Sampling and quantization.(d) Digital scan line.

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Fig 2.3. (a) Continuous image projected onto a sensor array. (b) Result of image

sampling and quantization.

2.3 Watermarking

What is Watermarking?

A Watermark is a recognizable image or pattern which appears on fine paper or some

documents to prevent counterfeiting. It is a visible embedded overlay on a digital photo

consisting of text or copy right information. It is prominently used for tracking copyright

infringements and for backbone authentication.

Classification of Watermark Algorithms

In this section we discuss different classification of watermarking algorithm Firstly,

According to type of document, watermarking technique can be divided into four groups:

a) Text watermarking

b) Image watermarking

c) Audio watermarking

d) Video watermarking

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Secondly based on the human perception, watermark algorithms are divided into two

categories as shown below.

Visible Watermarking:

Visible watermarking are easily perception by the human eye, means the visible

watermark can be seen without the extraction process. For example it can be name or logo of the

company.

Invisible Watermarking:

In this watermarking mark cannot be seen by human eye. It is embedded in the data

without affecting the content and can be extracted by the owner only.

Robust Watermark:

A digital watermark is called robust if it resists a designated class of transformations. Robust

watermarks may be used in copy protection applications to carry copy and no access control

information

Fragile watermark:

A digital watermark is called fragile if it fails to be detectable after the slightest modification.

Fragile watermarks are commonly used for integrity proof.

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2.4 Attributes of Digital Image Watermarking

The requirements for image watermarking can be treated as characteristics, properties or

attributes of image watermarking. Different applications demand different properties of

watermarking. Requirements of image watermarking vary and result in various design issues

depending on image watermarking applications and purpose [4]. These requirements need to be

taken into consideration while designing watermarking system. There are basic five requirements

as follows.

Fidelity:

Fidelity can be considered as a measure of perceptual transparency or imperceptibility of

watermark. It refers to the similarity of un-watermarked and watermarked images. This

perspective of watermarking exploits limitation of human vision. Watermarking should not

introduce visible distortions as it reduces commercial value of the watermarked image.

Robustness:

Watermarks should not be removed intentionally or unintentionally by simple image

processing operations Hence watermarks should be robust against variety of such attacks. Robust

watermarks are designed to resist normal processing. On the other hand, fragile watermarks are

designed to convey any attempt to change digital content.

Data Payload:

Data payload is also known as capacity of watermarking. It is the maximum amount of

information that can be hidden without degrading image quality. It can be evaluated by the

amount of hidden data. This property describes how much data should be embedded as a

watermark so that it can be successfully detected during extraction

Security:

Secret key has to be used for embedding and detection process in case security is a major

concern. There are three types of keys used in watermark systems: private-key, detection-key

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and public-key. Hackers should not be able to remove watermark with anti-reverse engineering

research algorithm.

Computational Complexity:

Computational complexity indicates the amount of time watermarking algorithm takes to

encode and decode. To ensure security and validity of watermark, more computational

complexity is needed. Conversely, real-time applications necessitate both speed and efficiency.

2.5 WATERMARKING APPLICATIONS

Copyright Protection:

Watermarking can be used to protecting redistribution of copyrighted material over the

untrusted network like Internet or peer-to-peer (P2P) networks. Content aware networks (p2p)

could incorporate watermarking technologies to report or filter out copyrighted material from

such networks.

Content Archiving:

Watermarking can be used to insert digital object identifier or serial number to help

archive digital contents like images, audio or video. It can also be used for classifying and

organizing digital contents. Normally digital contents are identified by their file names; however,

this is a very fragile technique as file names can be easily changed. Hence embedding the object

identifier within the object itself reduces the possibility of tampering and hence can be

effectively used in archiving systems.

Meta-data Insertion:

Meta-data refers to the data that describes data. Images can be labeled with its content

and can be used in search engines. Audio files can carry the lyrics or the name of the singer.

Journalists could use photographs of an incident to insert the cover story of the respective news.

Medical X-rays could store patient records.

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Broadcast Monitoring:

Broadcast Monitoring refers to the technique of cross-verifying whether the content that

was supposed to be broadcasted (on TV or Radio) has really been broadcasted or not.

Watermarking can also be used for broadcast monitoring. This has major application is

commercial advertisement broadcasting where the entity who is advertising wants to monitor

whether their advertisement was actually broadcasted at the right time and for right duration.

Tamper Detection:

Digital content can be detected for tampering by embedding fragile watermarks. If the

fragile watermark is destroyed or degraded, it indicated the presence of tampering and hence the

digital content cannot be trusted. Tamper detection is very important for some applications that

involve highly sensitive data like satellite imagery or medical imagery. Tamper detection is also

useful in court of law where digital images could be used as a forensic tool to prove whether the

image is tampered or not.

Digital Fingerprinting:

Digital Fingerprinting is a technique used to detect the owner of the digital content.

Fingerprints are unique to the owner of the digital content. Hence a single digital object can have

different fingerprints because they belong to different users.

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2.6 Principle of Digital Watermarking

Fig 2.4. Principle of Digital Watermarking

A watermarking system is divided into two distinct steps. They are embedding and

detection. In embedding process the proposed algorithm accepts the host and the data to be

embedded, and a watermarked signal is produced. The watermarked signal is then transmitted or

stored. The obtained watermarked image is passed through a decoder in which a reverse

algorithm is applied to retrieve the watermark. The different techniques uses different ways of

embedding watermark onto the cover object. During embedding and extraction process a secret

key to prevent illegal access to watermark. For a practical and useful watermarking scheme it has

to meet the following requirements: Robustness: Robustness means a digital watermarking

scheme should be able to resist the watermark attacks or modifications like resizing, file

compression, rotation etc. made to the original file. On the other hand, several intentional or

unintentional attacks may be incurred to remove the embedded watermark. Thus, the

watermarked image has to survive the legitimate usage such as resamples, conversions, lossy

compressions and other malicious operations. A robust watermarking scheme should recognize

the retrieved watermark and the image quality should not be seriously harmed. Imperceptibility:

A visible or invisible watermark can be embedded into an image, the visible watermark is

perceptible and it is just like noise. Using a noise removal process we can remove the visible

watermark. In order to reduce this risk of cracking, most of the proposed watermarking

techniques use invisible watermarks. On the other hand, the quality of the watermarked image is

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also very important. If in the process of embedding watermark, the quality of the watermarked

image is affected, then the watermarked image will lose its value or even draw the attention of

the attackers. Imperceptibility is a very important requirement therefore the quality between the

original image and the watermarked image should not be seriously degraded. Readily embedding

and retrieving: The watermark should be securely and easily embedded and retrieved by the

owner of the original image. Data load or capacity: Data load or capacity means the maximum

amount of data that can be embedded into the image to ensure proper retrieval of the watermark

during extraction. Blind: Some of the conventional watermarking schemes require the help of the

original image in order to retrieve the embedded watermark. But the reversible watermarking

schemes has the ability to recover the original image from the watermarked image directly. As

the retrieval process doesn’t need the original image, we reversible watermarking as blind.

Transparency: This refers to the perceptual similarity between the watermarked image and the

original image. The inserted watermark should be imperceptible. The watermark may lead to the

degradation in the quality of the digital content, but in some applications a small amount of

degradation may be accepted to get higher robustness.

Fig 2.5. A visible pattern watermarking on a image

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3. Existing System

3.1 Optical Watermarking

Figure `1’ outlines the basic concept underlying our technology of watermarking that

uses light to embed information. An object is illuminated by light that contains invisible

information on watermarking. As the illumination itself contains the watermarking information,

the image of a photograph of an object that is illuminated by such illumination also contains

watermarking. By digitizing this photographic image of the real object, the watermarking

information in binary data can be extracted in the same way as that with the conventional

watermarking technique. To be more precise, information to be embedded is first transformed

into binary data, “1” or “0,” and it is then transformed into a pattern that differs depending on

whether it is “1” or “0.” This pattern is transformed into an optical pattern and projected onto a

real object. It is this difference in the pattern that is read out from the captured image. Some

applications that use invisible patterns utilize infrared light however, infrared light cannot be

used for our purposes because cameras usually have a filter that cuts off infrared light and the

invisible pattern is not contained in the captured image of the object although it is contained in

the optically projected image on the object. Therefore, the technique we propose uses visible

light, and the pattern is made invisible by using fine patterns or low contrast patterns both of

which are under the resolving power of the human visual system. Using this method, the pattern

can be made invisible in both an optically projected image on the object and the image of the

object captured with the camera.

The light source used in this technology projects the watermarking pattern similar to a

projector. Since the projected pattern has to be imperceptible to the human visual system, the

brightness distribution given by this light source then looks uniform to the observer over the

object, which is the same as that with the conventional illumination. The brightness of the

object’s surface is proportional to the product of the reflectance of the surface of the object and

illumination by an incident light. Therefore, when a photograph of this object is taken, the image

on the photograph contains watermarking information, even though this cannot be seen. The

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main feature of the technology we propose is that the watermarking can be added by light.

Therefore, this technology can be applied to objects that cannot be electronically embedded with

watermarking, such as pictures painted by the artists.

Fig. 3.1 Basic concept underlying technology of watermarking that uses light to embed data.

In the base paper the authors had used frequency domain techniques to embed watermark or to

project invisible watermark onto pictures displayed at museum and celebrity pictures to protect

from illegal use. Those frequency domain techniques are DFT, DCT, WHT, DWT and Haar

Discrete wavelet transform.

Let us go through the above mentioned frequency domain techniques.

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3.2 Techniques Used in Existing System

Discrete Cosine Transform:

The DCT is the most popular transform function used in signal processing. It transforms

a signal from spatial domain to frequency domain. Due to good performance, it has been used in

JPEG standard for image compression. It is a function represents a technique applied to image

pixels in branded. DCT techniques are more robust compared to spatial domain techniques. Such

algorithms are robust against simple image processing operations like adjustment, brightness,

blurring, contrast and low pass filtering and so on[3]. But it is difficult to implement and

computationally more expensive. The one-dimensional DCT is useful in processing one

dimensional signals such as speech waveforms. For analysis of two-dimensional (2D) signals

such as images, we need a 2D version of the DCT. The 2D DCT and 2D IDCT transforms is

given by equation 1 and 2.

Formulae of 2-D DCT:

F i , j (u , v )=C (u ) C (v )N∗N

∑x

N −1

∑y

N−1f

i , j(x , y )∗cos {(2 x+1 ) uπ /2 N }cos {(2 x+1 ) vπ /2 N }

………………… (1)

Formulae of 2-D inverse DCT:

f i , j (x , y )=∑u

N−1

∑v

N−1

C (u )C (v )F i, j (u , v )∗cos {(2 x+1 )uπ /2 N }cos {(2 y+1 ) vπ /2 N }

……………….. (2)

Where,

C (u)={ 1 ,∧u=0√ 2 ,∧u ≠ 0

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C (v )={ 1 ,∧v=0√ 2 ,∧v ≠ 0

Walsh Hadamard Transform:

The Hadamard transform is a non-sinusoidal, orthogonal transformation that decomposes

a signal into a set of orthogonal, rectangular waveforms called Walsh functions. The

transformation has no multipliers and is real because the amplitude of Walsh (or Hadamard)

functions has only two values +1 or -1

The Hadamard matrix is a square array of plus and minus ones whose rows (and columns) are

orthogonal to one another.

Forward Walsh Hadamard transform

F i , j(u , v)= 1N∑

x

N−1

∑y

N−1

f i , j ( x , y ) wh (u , x ) wh( y , v)

When a 2D inverse WHT (i-WHT) is used, the equation is ex- pressed by

f i , j( x , y )= 1N∑

u

N −1

∑v

N −1

F (i , j ) (u , v ) wh ( x ,u ) wh(v , y)

Where wh (i , j ) denotes a component of the Walsh-Hadamard matrix

Where f i , j( x , y ) are the watermarked imager data for pixel (x,y) of block (i, j) in real space

F i , j(u , v) are the data for component (u,v) block of block (i,j) in frequency space and N is the

number of pixels in the block in x and y directions

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Fig 3.2. Producing watermarks using DCT and WHT

Introduction to WAVELETS:

Wavelets are functions that satisfy certain mathematical requirements and are used in

representing data or other functions. The idea is not new. Approximation using superposition of

functions has existed since early 1800’s, when Joseph Fourier discovered that he could superpose

sine’s and cosines to represent other functions. However, in wavelet analysis, the scale that we

use to look at data plays a special role. Wavelet algorithms process data at different scales and

resolutions. If We look at a signal with a large “window”, we would notice gross features.

Similarly, if we look at a signal with a small ”window”, we would notice small features. The

result in wavelet analysis is to see both the forest and the trees

.

Discrete Wavelet Transform:

Wavelet Transform is a modern technique frequently used in digital image processing,

compression, watermarking etc. The transforms are based on small waves, called wavelet, of

varying frequency and limited duration. A wavelet series is a representation of a square-

integrable function by a certain orthonormal series generated by a wavelet. Furthermore, the

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properties of wavelet could decompose original signal into wavelet transform coefficients which

contains the position information. The original signal can be completely reconstructed by

performing Inverse Wavelet Transformation on these coefficients. The basic idea of DWT in

which a one dimensional signal is divided in two parts one is high frequency part and another is

low frequency part. Then the low frequency part is split into two parts and the similar process

will continue until the desired level. The high frequency part of the signal is contained by the

edge components of the signal. In each level of the DWT (Discrete Wavelet Transform)

decomposition an image separates into four parts these are approximation image (LL) as well as

horizontal (HL), vertical (LH) and diagonal (HH) for detail components. In the DWT

decomposition input signal must be multiple of 2n. Where, n represents the number of level. To

analysis and synthesis of the original signal DWT provides the sufficient information and

requires less computation time. Watermarks are embedded in these regions that help to increase

the robustness of the watermark.

Haar Wavelet Transform:

Recently, wavelet-based watermarking schemes have begun to attract greatly increased

attention. The main reasons for inserting watermarks in the wavelet domain are that it has good

space-frequency localization, superior HVS modeling, and low computational cost. In practice,

when a watermark is to be embedded in the wavelet domain, there are many wavelet bases to

choose from. Since the different bases have different characteristics, the choice of which base to

use to embed the watermark is important and found that the Haar wavelet is suitable for

watermarking images.

Let I(x, y) denote a digital image of size 2M×2N, if not, boundary prolongation should be used to

ensure that the size of the image is divisible by 2, which is necessary for Haar wavelet transform.

The wavelet low-pass and high-pass filters are h(n) and g(n) respectively. Then the image can be

decomposed into its various resolutions based on the approximate weight (LL) and the detailed

weights of the horizontal direction (HL), vertical direction (LH), and diagonal direction (HH).

The decomposition formula is:

¿ ( i , j )=∑x, y

h ( x−2 i )h ( y−2 j) I (x , y)

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LH (i , j )=∑x , y

h ( x−2i ) g( y−2 j) I (x , y )

HL (i , j )=∑x , y

g ( x−2 i )h ( y−2 j)I (x , y)

HH ( i , j )=∑x , y

g ( x−2 i ) g( y−2 j)I (x , y )

Fig.3.3. Two-level wavelet decomposed image.

where i, j, N∈ Z+, x, y ∈ Z, −2L+1≤x−2i≤0, −2L+1≤y−2i≤0.On this basis, similar

decomposition procedure can be implemented on LL to get the two-level wavelet transformed

image, as shown in Fig. 1, and so on. The wavelet image reconstruction is the inverse transform

of the wavelet decomposition. The formula is:

I ( x , y )=∑i , j

h ( x−2 i ) h ( y−2 j )≪(i , j )+∑i , j

h ( x−2 i ) g ( y−2 j ) LH ( i , j )+∑i , j

g ( x−2 i ) h ( y−2 j ) HL (i , j )+∑i , j

g ( x−2 i ) g ( y−2 j ) HH ( i , j )

3.3 Trouble in Present or Existing System

The above described techniques excluding Fourier transform, DWT suffer from 4

fundamental, intertwined shortcomings problems they are

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Problem 1: Shift Variance

Problem 2: Oscillations

Problem 3: Aliasing

Problem 4: Lack of Directionality

Problem 1: Shift Variance:

A small shift of the signal greatly perturbs the wavelet coefficient oscillation pattern

around singularities Shift variance also complicates wavelet-domain processing algorithms must

be made capable of coping with the wide range of possible wavelet coefficient patterns caused

by shifted singularities, To better understand wavelet coefficient oscillations and shift variance,

consider a piecewise smooth signal x(t− t0) like the step function

u (t )=f ( x )={0 ,∧t<01 ,∧t ≥ 0

analyzed by a wavelet basis having a sufficient number of vanishing moments[6]. Its wavelet

coefficients consist of samples of the step response of the wavelet

d ( j ,n)≈ 2−3 j

2 ∆ ∫−∞

2 j¿−n

¿φ ( t ) dt

where ∆ is the height of the jump. Since ψ(t ) is a bandpass function that oscillates

around zero, so does its step response d( j, n) as a function of n (recall Figure 1). Moreover, the

factor 2 j in the upper limit ( j≥ 0) amplifies the sensitivity of d( j, n) to the time shift t0, leading

to strong shift variance.

Problem 2: Oscillations

Since wavelets are band pass functions, the wavelet coefficients tend to oscillate positive

and negative around singularities. This considerably complicates wavelet-based processing,

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making singularity extraction and signal modeling, in particular very challenging [22].

Moreover, since an oscillating function passes often through zero, we see that the conventional

wisdom that singularities yield large wavelet coefficients is overstated. Indeed, it is quite

possible for a wavelet overlapping a singularity to have a small or even zero wavelet coefficient.

PROBLEM 3: ALIASING

The wide spacing of the wavelet coefficient samples, or equivalently, the fact that the

wavelet coefficients are computed via iterated discrete-time down sampling operations

interspersed with non ideal low-pass and high-pass filters, results in substantial aliasing. The

inverse DWT cancels this aliasing, of course, but only if the wavelet and scaling coefficients are

not changed[6]. Any wavelet coefficient processing (thresholding, filtering, and quantization)

upsets the delicate balance between the forward and inverse transforms, leading to artifacts in the

reconstructed signal.

PROBLEM 4: LACK OF DIRECTIONALITY

Finally, while Fourier sinusoids in higher dimensions correspond to highly directional

plane waves, the standard tensor product construction of M-D wavelets produces a checkerboard

pattern that is simultaneously oriented along several directions. This lack of directional

selectivity greatly complicates modeling and processing of geometric image features like ridges

and edges.

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4. PROPOSED SYSTEM

4.1 Introduction

The aim of the project is to find the better accuracy results of the embedded watermark

information on any image at watermarking extraction module.

We know present the whole world runs on computer via internet with trending to latest

technologies making communication of data very easy and the data may be an audio, text, video

or image, at the same time disturbances or attacks on data is quite general, but those attacks or

disturbances should not reduce the performance of the communication system or data transmitted

via the internet so there are so many generic schemes were introduced by various people to

protect the data from attacks or disturbances from modifying the original data, day to day data is

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transmitted more precisely or securely via internet at the same time the attack of disturbance is

also severe so the shifting to most prominent technique is very good.

The most promising technique to protect data from being illegally modified is

watermarking technique, watermarking technique aroused from steganography but the

disadvantage of steganography is the hidden information or data cannot be recovered after

manipulation, hence digital watermarking plays a confidential role in embedding the

watermarked information in the data and recovering it after manipulation. As described in

literature survey the classification of watermarking, digital watermarking can be done in

frequency domain techniques that are explained above. The frequency domain techniques are

DCT, WHT, Haar DWT etc. and these techniques suffer from 4 fundamental, intertwined

shortcomings as explained above.

Fortunately, there is a simple solution to these four DWT short comings. Hence a new

scheme or technique based on wavelet transform is proposed for embedding information into the

image using complex wavelets the new technique is DUAL TREE COMPLEX WAVELET

TRANSFORM. This technique is applied to the same existing optical watermarking technique

for a set of images and comparing the result with the previous techniques

The main feature of the technology we propose is that the watermarking can be added by

light. Therefore, this technology can be applied to objects that cannot be electronically embedded

with watermarking, such as pictures painted by the artists.

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4.2 BLOCK DIAGRAM

Vary for diff HC Values

Watermarked Images

Painting taken with camera

Painting/ Human Face

Project Watermark

Pattern

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Fig 4.1. Block diagram of the proposed system.

Project Watermark pattern:

This is the first stage of our experiment, we need to choose on watermark pattern (either a

logo, or any information) etc which is to be projected on to the image of any real type or any

museum paintings, archeological monuments etc. After choosing the pattern, it should be

projected on the selected image using a projector. The light source used in this technology

projects the watermarking pattern similar to a projector. Since the projected pattern has to be

imperceptible to the human visual system, the brightness distribution given by this light source

then looks uniform to the observer over the object, which is the same as that with the

conventional illumination. The brightness of the object’s surface is proportional to the product of

the reflectance of the surface of the object and illumination by an incident light. Therefore, when

a photograph of this object is taken, the image on the photograph contains watermarking

information, even though this cannot be seen

Painting/Human Face:

It is the subject of our experiment to be conducted we know that to apply watermark we

need any object here we are considering a real painting or human face etc. which is projected

with the required or considered pattern using a projector.

Painting taken with camera:

Extract Watermarking Inverse

transform

Calculate Accuracy Ratio

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Here the projected image with the watermarked pattern is taken by a digital camera to be

processed for further extraction stage. And the output of the camera is a digital image with

watermark embedded using light.

Vary for Different HC Values:

The watermarked area is divided into units of pixel blocks, and each block has a DC

component that gives an average brightness for the entire watermarked area, i.e., brightness of

illumination. Every block also has the highest frequency component (HC) in both the x and y

directions to express the 1-b binary information for watermarking. We used the phase of HC to

express binary data i.e., “0” or “1.”

Transform Techniques:

Here we apply transform techniques like DCT, DWT , DUAL TREE COMPLEX

WAVELET TRANSFORM, etc., to extract the watermarking embedded.

Calculating Accuracy:

This is the last step of our experiment this stage calculates the number of watermarked

pixels detected correctly to the whole watermarked pixels. The accuracy of detection of

embedded data read out from the watermarked image we obtained was evaluated with the rate of

correctly read out data to whole embedded data in the watermarked image where blocks of “0”

and “1” were alternately positioned like those in a checkerboard pattern.

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4.3 FLOW CHART

Start

Watermarked Image

4*4,8*8 16*16 Size

Divide image into N*N Pixel Blocks

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5. Tools Required

For the proposed system generic tools required are matlab, and matlab coding, a still image,

projector and digital cam.

MATLAB:

The name MATLAB stands for MATrix LABoratory. MATLAB was written originally

to provide easy access to matrix software developed by the LINPACK (linear system package)

and EISPACK (Eigen system package) projects.

Apply Inverse WHT

Extract the watermark for Diff HC Values

Compare the Accuracy

Extract the watermark for

different HC Values

Extract the watermark for

Different HC Values

Apply Inverse DCT

Apply Dual Tree Complex

Conclude

End

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MATLAB is a high-performance language for technical computing. It integrates

computation, visualization, and programming environment. Furthermore, MATLAB is a modern

programming language environment it has sophisticated data structures, contains built-in editing

and debugging tools, and supports object-oriented programming. These factors make MATLAB

an excellent tool for teaching and research.

MATLAB has many advantages compared to conventional computer languages (e.g.,C,

FORTRAN) for solving technical problems. MATLAB is an interactive system whose basic data

element is an array that does not require dimensioning. The software package has been

commercially available since 1984 and is now considered as a standard tool at most universities

and industries worldwide. It has powerful built-in routines that enable a very wide variety of

computations. It also has easy to use graphics commands that make the visualization of results

immediately available. Specific applications are collected in packages referred to as toolbox.

There are toolboxes for signal processing, symbolic computation, control theory, simulation,

optimization, and several other fields of applied science and engineering. In industry MATLAB

is the tool of choice for high productivity research, development and analysis

Matlab as a high-performance language for technical computing, integrating computation,

visualization, and programming in an easy- to-use environment where problems and solutions

are expressed in familiar mathematical notation. Typical uses include

Mathematics and computation

Algorithm development

Data acquisition

Modeling, simulation, and prototyping

Data analysis, exploration, and visualization

Scientific, engineering and financial graphics

Application development, including graphical user interface building.

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CONCLUSION

We proposed an optimal condition for the size of pixel blocks of an orthogonal transform

that was used for a technique of robust optical watermarking. The experimental results proved

that it was practical and that the accuracy of detection of data embedded with optical

watermarking could be improved with more pixels in each block. They revealed that under

conditions of very weak embedded watermarking, the accuracy of detection using a block with

16 16 pixels reached 100%, except when Haar DWT was used to produce watermarked images

anda complicated structured image was used as an object image. We also clarified that

robustness against various disturbances became a trade-off in optimizing embedded

watermarking data, as the volume of information using blocks with 16 16 pixels that could be

embedded into data for the watermarked image was lower than that using blocks with 4 4 or 8 8

pixels. As a result, we concluded that the maximum volume of embedded bits per unit block size

under conditions of 100% accuracy of detection could be determined in optical water marking.

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When Haar DWT was used, the accuracy of detection was rather inferior to that with DCT and

WHT. However, as the general features of DWT indicated that the pixel resolution in real space

and the spatial-frequency resolution in frequency space were independent, the accuracy of

detection could be improved when more pixels were used in a block of the conversion base for

DWT. We next intend to evaluate the optimal pixel size in the conversion base to obtain

sufficiently accurate detection with

DWT.

REFERENCES

1. Journal of Electronic Imaging by Komori and Uehira: Optical watermarking technology

for protecting portrait rights

2. Y. Ishikawa, K. Uehira, and K. Yanaka, “Optical watermarking technique robust to

geometrical distortion in image,” in Proc. ISSPIT2010, 2010, pp. 67–72.

3. Y. Ishikawa, K. Uehira, and K. Yanaka, “Illumination watermarking technique using

orthogonal transforms,” in Proc. IAS2009, 2009, pp. 257–260.

4. O. Matoba et al., “Optical techniques for information security,” Proc. IEEE 97(6), 1128–

1148 (2009).

5. International Journal of Advanced Computer and Mathematical Sciences ISSN 2230-

9624. Vol 3, Issue 1, 2012, pp 194-204

6. IEEE Signal Processing Magazine 1053-5888/05/$20.00©2005IEEE

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