project report on eda manufacturing

Reversible Watermarking using DCT algorithm

ABSTRACT

A watermark embeds an imperceptible signal into data such as audio, video and image for variety of purposes like captioning and copyright control. We first outline the desired characteristics of watermarks. Previous work in digital watermarking is then reviewed. A reversible watermarking approach recovers the watermarked image to source image after extracting the embedded watermarks. This report represents a reversible image watermarking approach that works on quantized DCT coefficients. We also show visible watermarking that helps in protection of publicly available images. This report introduces basic concepts of digital watermarking. We also discuss about invisible watermarking technique where watermarked image is produces that is no different than the original image. We also discuss about DCT algorithm and various types of DCT. By adjusting the block DCT coefficients of image watermark is hidden. The report also discusses about the advantages and disadvantages of this watermarking technique with respect to other technique. Various applications of this reversible watermarking technique is also stated in the report.

1 Department of EXTC, BVCOE, Navi Mumbai


CHAPTER 1

INTRODUCTION

Digital images are easily transmitted, modified and reproduced now days. Thus, how to protect an important image from malicious attack or from unauthorized usage is a very important issue. Recently watermarking is an important issue to overcome this image authentication problem for keeping important images safely. A watermarking image modifies an image by embedding some information in it. But this kind of watermarking is irreversible and the original image is lost and cannot be acquired back after extracting the watermark. However, some application needs to reconsider the original image from the watermarked image. Thus, reversible watermarking approaches are represented. In visible watermarking of images, the secondary image (watermarked image) is embedded in primary (host) image in such a way that the watermark is intentionally perceptible to human eye. Whereas in the case of invisible watermarking the embedded data is not visible but can be extracted or detected by the computer program. Currently digital watermarking techniques can be divided into two categories by embedding position- spatial domain and transform domain watermark. Spatial domain techniques were developed earlier and are easier to implement, but are limited in robustness, while transform domain techniques, which embed watermark in hosts transform domain, is more sophisticated and robust. With the development of digital watermarking, spatial techniques due to their weakness in robustness, are generally abandoned and frequency algorithm based on DCT and DWT are used.. To check the fragility of DCT watermark we embed the watermark in the high frequency domain. A fragile watermark is said to be fragile if it is not detected after slightest of modification. After watermarking the original image can be produced by using IDCT technique.



CHAPTER 2

LITERATURE REVIEW

2.1 BACKGROUND AND RELATED WORK

Digital watermarking is an extension of this concept in the digital world. It is a technique that

provides a solution to the longstanding problems faced with copyrighting digital data. Digital

watermarks are pieces of information added to digital data (audio, video, or still images) that can

be detected or extracted later to make an assertion about the data. This information can be textual

data about the author, its copyright, etc; or it can be an image itself.

Current main applications of watermarking include the following:

Copyright protection: The objective is to embed information about the

Source /owner of the digital media in order to prevent other parties from claiming the

ownership of the media.

Fingerprinting: The objective of fingerprinting is to convey information about the

recipient of the digital media (rather than the owner) in order to identify every single

distributed copy of the media. This concept is very similar to serial numbers of software

products.

Copy protection: Watermarking can be used to control data copying devices and

prevent them from copying the digital media in case the watermark embedded in the

media indicates that media is copy-protected.

Image authentication: The objective is to check the authenticity of the digital media.

This requires the detection of modifications to the data.



Following is a list of attacks against which watermarking system could be judged.

JPEG compression: JPEG is currently one of the most widely used compression

algorithms for images and any watermarking system should be resilient to some degree of

compression.

Geometric transformations:

Horizontal flip: Many images can be flipped without losing any value. Although

resilience to flipping is usually straightforward to implement only very few systems do

survive it.

Rotation: Small angle rotation, often in combination with cropping, does not usually

change the commercial value of the image but can make the watermark undetectable.

Rotations are used to realign horizontal features of an image and it is certainly the first

modification applied to an image after it has been scanned.

Cropping: In some cases, infringers are just interested by the “central" part of the

copyrighted material, moreover more and more Web sites use image segmentation, which

is the basis of the “Mosaic" attack. This is of course an extreme case of cropping.

Scaling: This happens when a printed image is scanned or when a high resolution digital

image is used for electronic applications such as Web publishing. This should not be

neglected as we move more and more toward Web publishing. Scaling can be divided

into two groups, uniform and non-uniform scaling. Under uniform scaling we understand

scaling which is the same in horizontal and vertical direction. Non-uniform scaling uses

different scaling factors in horizontal and vertical direction (change of aspect ratio). Very

often digital watermarking methods are resilient only to uniform scaling.

Deletion of lines or columns: This attack is very efficient against any straightforward

implementation of spread-spectrum techniques in the spatial Domain. Removing k

samples at regular intervals in a pseudo random sequence (hence shifting the next ones)

typically divides by k the amplitude of the cross correlation peak with the original

sequence.



Generalized geometrical transformations: A generalized geometrical transformation is

a combination of non-uniform scaling, rotation, and shearing.

Random geometric distortions (Stir Mark): Image-watermarking tools, which do not

survive them, should be considered unacceptably easy to break.

Geometric distortions with JPEG: Rotation and scaling alone are not enough they

should be also tested in combination with JPEG compression. Since most artists will first

apply the geometric transformation and then save the image in a compressed format. It

makes sense to test robustness of watermarking system to geometric transformation

followed by compression.. However experience from professionals shows that “quality

factors" down to 70% are reasonable. Artists seem to use JPEG extensively as well as

resizing.

2.2 EXISTING SYSTEM

Generally, larger amounts of data can be embedded in the spatial domain than in the frequency

domain. But hiding information in the frequency domain is more robust than in the spatial

domain. By using the Least Significant Bit (LSB) embedding method large amount of data can

be stored. The security system is very low in existing system, by using LSB embedding method

large data loss will occur.

LSB method is type of spatial domain. Spatial domain requires large amount of memory to store

image information than frequency domain.

So, existing system is not resist to attacks such as addition of noise, JPEG compression etc.

2.3 PROPOSED SYSTEM

In this system, Frequency Domain is used to embed a secret image into the cover image. The

fractal image compression is used to compress a secret image. The main advantage of this fractal

image compression is resolution independent. When zooming process is performed in the

original image, the quality of the original image has slight changes. After the compression is

over, the compressed image is encrypted by using DES method. Then the encrypted secret image



is hided into the cover image by using DCT process. The secret image is hidden behind the cover

image by using frequency domain. Because of this DCT based embedding method, no one can

steal the secret image. DCT system is used to provide good robustness.

DCT Process for Embedding :

In this system the encrypted file is given as the input for the embedding process. In the

embedding process, a cover image is taken for hiding the secret image. First the cover image is

segmented by using the Region based Segmentation method. In this Region merging the picture

is divided into 4/8 parts, if the resulting picture doesn’t meet a homogeneity criteria, it is further

divided. The next process is converting of the image signals into elementary frequency

coefficients by applying the DCT formula.

The DCT formula is applied in each row and column.

DCT Extraction Process:

The received image is divided into blocks of 8×8 pixels each and each block is transformed

independently. After each block is transformed independently and for each block a 64 DCT

coefficients is received. Following the embedding order is the embedding procedure, the

embedded DCT coefficients are selected out. In order to get the 3 hidden bits, an AND operation

is done on one of the selected DCT coefficients with the bit stream”00011100”.

2.4 EXISTING SYSTEM VS PROPOSED SYSTEM

1. Proposed system uses DCT which is in the frequency domain which ensures robustness

against JPEG compression.

2. As proposed system uses frequency domain compared with existing system ensures less

memory is required to store image information.



CHAPTER 3

Motivation

We are using reversible watermarking technique using DCT algorithm. This technique helps to get the image after the extraction of watermark which is exactly same as the original image before watermark was added to it. This technique restores the original image and also helps us to identify, if any tampering was done to the original image. This is a very simple and efficient technique done to protect digital data from external attack and to protect its copyright.

Why DCT is preferred over other techniques?

When watermarked image is affected with scaling and translation attack, individual high frequency coefficients and middle frequency coefficients of DCT domain image changes, but overall addition sum (Si) do not change. Hence we got 100% similar watermark and recovered image. This method gives less PSNR than any other method as well as it gives less embedding capacity. But no any other method is suitable for geometric attack. Mean Squared Error (MSE) is defines as the square of difference in the pixel values between corresponding pixels of the two images. For DCT the MSE increases with increase in pixel size whereas for DWT the MSE first decreases with increase in window size and then increases. Compression increases with increase in window size in DCT and in DWT the compression decreases with increase in window size.

DCT is used in transformation for JPEG standard. DCT performs efficiently at medium bit rates. DWT is used as basis of transformation in JPEG 2000 standard; it provides high quality compression at lower bit rates .DWT provides lower quality than JPEG at low compression rate. It requires longer compression time as compared to DCT. The use of larger DWT basis functions or wavelet filters produces blurring near the edges in the images.



CHAPTER 4

Problem Definition

Since the rise in digitalization there have been rise in copyright infringement and various kind of attacks on data. We require digital techniques to protect our data from such attacks and to protect its copyright. Our aim is to provide an easy and efficient technique to serve this purpose. The technique we are using is called reversible watermarking using DCT algorithm.

Objectives

1) The primary objective is to convert an image into frequency domain.2) The watermark is then inserted in the image using DCT algorithm.3) The watermark inserted can be visible or invisible as per the choice.4) Then the watermarked image is compared with the original image.5) Watermark is again extracted from the image using IDCT.6) Image recovered after extraction of watermark is compared with the original image.



CHAPTER 5

REQUIREMENT ANALYSIS

The working of watermarking system can be showed to the user with the help of interfaces. So,

system have some graphical user interfaces to interact with the user.

5.1 FUNCTIONAL REQUIREMENTS :

Insertion module: This module insert digital watermark in cover image.

Extraction module: This module allow to extract inserted watermark from secret image.

5.2 NON FUNCTIONAL REQUIREMENTS :

Accessibility: Since the system would be developed as PC client application, this will

able to access only single user at a time.

Availability: User can access the system by using executable file or shortcut.

Accuracy: It is required to have over 90% accuracy rate.

Response Time: Response Time will depend upon insertion and extraction module.

5.3 RESOURCE REQUIREMENTS:

HARDWARE DETAILS

Processor: PC with minimum requirement of Pentium 2 processor

Disk Space: Minimum 20MB

RAM: Minimum 512 MB of RAM

SOFTWARE DETAILS

Operating System: Windows XP, VISTA, 7, 8.

Developing Tool: MATLAB 7.0, 8.0, 9.0, 11a, 13b



Why MATLAB?

MATLAB provides a comprehensive set of reference-standard algorithms and graphical tools for

image processing, analysis, visualization, and algorithm development. You can restore noisy or

degraded images, enhance images for improved intelligibility, extract features, analyse shapes

and textures, and register two images. Most toolbox functions are written in the open MATLAB

language, giving you the ability to inspect the algorithms, modify the source code, and create

your own custom functions.

MATLAB provides a number of features for documenting and sharing your work. You can

integrate your MATLAB code with other languages and applications, and distribute your

MATLAB algorithms and applications.

Key Features:

High-level language for technical computing

Development environment for managing code, files, and data

Interactive tools for iterative exploration, design, and problem solving

Mathematical functions for linear algebra, statistics, Fourier analysis, filtering, optimization, and

numerical integration



CHAPTER 6

TYPES OF WATERMARK

6.1 VISIBLE WATERMARK

(Fig6.1.a-visible watermark)

(Fig6.1.b-visible watermark)



6.2 INVISIBLE WATERMARK:

(Fig6.2-Invisible watermark)



6.3 PROPERTIES OF WATERMARK:

Some of the properties that should be satisfied by watermarks are:

1) Invisible: A watermarking system is of no use if it distorts the cover image and also it should

not be noticeable to the viewer.

Ideally the watermarked imaged should look indistinguishable from the original even on the

highest quality equipment.

2) Robust: The watermark should be resistant to distortion introduced during either normal use

(unintentional attack), or a deliberate attempt to disable or remove the watermark present

(intentional, or malicious attack). Unintentional attacks involve transforms that are commonly

applied to images during normal use, such as cropping, resizing, contrast enhancement…etc.

3) Unambiguous: Retrieval of the watermark should unambiguously identify the owner.

Furthermore, the accuracy of owner identification should degrade gracefully in the face of attack.

4) Unobtrusive: Invisible enough not to degrade data quality and to prevent an attacker from

finding and deleting it.

5) Readily Detectable: the data owner or an independent control authority should easily detect

it.

6) Innumerable: It should be able to generate a large number of watermarks, all distinguishable.



CHOICE OF WATERMARK OBJECT:-

The first question we need to ask with any watermarking system, is that in what form embedded

message should be taken?

The most straight-forward approach would be to embed text strings into an image, allowing an

image to directly carry information such as author, title, date…and so forth. The drawback

however to this approach is that ASCII text in a way can be considered to be a form of LZW

compression, which each letter being represented with a certain pattern of bits. By compressing

the watermark-object before insertion, robustness suffers.

Due to the nature of ASCII codes, a single bit error due to an attack can entirely change the

meaning of that character, and thus the message. It would be quite easy for even a simple task

such as JPEG compression to reduce a copyright string to a random collection of characters.

Rather than characters, why not embed the information in an already highly redundant form,

such as an image .Not only do images lend themselves to image watermarking applications, but

the properties of the HVS can easily be exploited in recognition of a degraded watermark.

Fig.6.2.a - Ideal Watermark- Object vs. Object with 25% Additive Gaussian Noise

Despite the high number of errors made in watermark detection, the retrieved watermark is still

highly recognizable.



CHAPTER 7

DIFFERENT TECHNIQUES AVAILABLE

7.1 SPATIAL DOMAIN TECHNIQUE (Least Significant Bit Modification):-

The easiest way of watermark embedding, is to embed the watermark into the least-significant-

bits of the cover object .Given the extraordinarily high channel capacity of using the entire cover

for transmission in this method, a smaller object may be embedded multiple times. Even if most

of these are lost due to attacks, a single surviving watermark would be considered a success.

Disadvantage:-

a) It may survive transformations such as cropping but any addition of noise or lossy

compression will defeat the watermark.

b) It can also be modified by simply setting the LSB bits of each pixel to one thus fully defeating

the watermark with negligible impact on the cover object and the embedded watermark could be

easily modified by anyone.

c) It lacks the basic robustness property that watermarking applications requires.

7.2 WAVELET WATERMARKING TECHNIQUE

In this watermark embedding is done in wavelet domain. The DWT (Discrete Wavelet

Transform) separates an image into a lower resolution approximation image (LL) as well as

horizontal (HL), vertical (LH) and diagonal (HH) detail components. The process can then be

repeated to computes multiple “scale” wavelet decomposition.



(Fig.7.2.a Scale 2-Dimensional Discrete Wavelet Transform)

Disadvantages:-

1) The cost of computing DWT is higher.

2) Longer compression time.

3) The use of larger DWT basis function or wavelet filters produces blurring and ringing noise

near edge regions in images or video frames.

2) Cost of computing is less than DWT.

7.3 DCT Based Watermarking:

In this method we are going to perform DCT operation on original image. After applying DCT

and watermark will be embed into the DCT applied image. This method is also known as blind

watermarking as we can retrieve back original image from watermarked image.

Advantages:

1) It has higher compression efficiency than DFT.

2) Cost of computing is less than DWT.



CHAPTER 8

DISCRETE COSINE TRANSFORM (DCT)

A discrete cosine transform (DCT) expresses a sequence of finitely many data points in terms

of a sum of cosine functions oscillating at different frequencies.

DCTs are important to numerous applications in science and engineering, from lossy

compression of audio (e.g. MP3) and Images (e.g. IPEG) (where small high-frequency

components can be discarded), to spectral methods for the numerical solution of partial

differential equations.

In the last decade, Discrete Cosine Transform (DCT) has emerged as the de-facto image transformation in most visual systems. DCT has been widely deployed by modern video coding standards, for example, MPEG, JVT etc.

8.1 Types of DCT:

There are four types of DCT viz DCT-I, DCT-II, DCT-III & DCT-IV.

1. DCT-I : DCT-I is equivalent to DFT 2N-2 real numbers with even symmetry, however it should be noted that DCT-I is not defined for N less than 2 whereas all other DCT forms are defined for every positive value of N

2. DCT-II : DCT-II is the most commonly used transform, and is often simply referred to as the Discrete Cosine Transform

3. DCT-III : because it is the inverse of the DCT-II, it is simply referred as Inverse DCT

4. DCT-IV : DCT-IV is called Modified DCT where the data from different transforms are overlapped

In principle, there are actually four additional types of discrete cosine transform (Mariucci, 1994), corresponding essentially to real-even DFTs of logically odd order, which have factors of N =±½ in the denominators of the cosine arguments.

Equivalently, DCTs of type’s I-IV imply boundaries that are even/odd around either a data point for both boundaries or halfway between two data points for both boundaries. DCTs of types V-VIII imply boundaries that even/odd around a data point for one boundary and halfway between



two data points for the other boundary. However, these variants seem to be rarely used in practice. One reason, perhaps, is that FFT algorithms for odd-length DFTs are generally more complicated than FFT algorithms for even-length DFTs

8.2 The DCT EQUATION:

The DCT equation (Eq.1) computes the i, jth entry of the DCT of an image

p (x, y) is the x ,yth element of the image represented by the matrix p. N is the size of the block

that the DCT is done on. The equation calculates one entry (i, j th) of the transformed image

from the pixel values of the original image matrix. For the standard 8x8 block that JPEG

compression uses, N equals 8 and x and y range from 0 to 7.



( Fig 8.2.aTwo dimensional DCT basis functions (N = 8). Neutral gray represents zero,white represents positive amplitudes, and black represents negative amplitude )

8.3 PROPERTIES OF DCT:

Some properties of the DCT which are of particular value to image processing applications

a) Decorrelation:The principle advantage of image transformation is the removal of redundancy between neighboring pixels. This leads to uncorrelated transform coefficients which can be coded independently. It can be inferred that DCT exhibits excellent decorrelation properties.

b) Energy Compaction:Efficacy of a transformation scheme can be directly gauged by its ability to pack input data into as few coefficients as possible. This allows the quantizer to discard coefficients with relatively small amplitudes without introducing visual distortion in the reconstructed image. DCT exhibits excellent energy compaction for highly correlated images c) Symmetry:This is an extremely useful property since it implies that the transformation matrix can be recomputed offline and then applied to the image thereby providing orders of magnitude improvement in computation efficiency.



8.4 DCT Implementation on an image

(Fig 8.4.a-Implementation of DCT)[1]



CHAPTER 9

PROJECT DESIGN

9.1 METHODOLOGY:

We are using the waterfall model.

Sometimes called the classic life cycle or the waterfall model, the linear sequential model

suggests a systematic, sequential approach to software development that begins at the system

level and progresses through analysis, design, coding, testing, and support. After a conventional

engineering cycle, the linear sequential model encompasses the following activities:

Fig 9.1.A: Waterfall Model


Preliminary Investigation

Requirement Analysis

System Design

Code Generation

Testing


System/information engineering and modelling:

Because software is always part of a larger system, work begins by establishing requirements for

all system elements and then allocating some subset of these requirements to software. This

system view is essential when software must interact with other elements such as hardware,

people, and databases. System engineering and analysis encompass requirements gathering at the

system level with a small amount of top level design and analysis. Phases included are described

below.

Software requirements analysis:

The requirements gathering process is intensified and focused specifically on software.

To understand the nature of the program(s) to be built, the software engineer ("analyst")

must understand the information domain for the software, as well as required function,

behaviour, performance and interface. Requirements for both the system and the software

are documented and reviewed with the customer. In this phase, various ideas and

concepts related to watermarking techniques are reviewed

Design:

Software design is actually a multistep process that focuses on four distinct attributes of a

program: data structure, software architecture, interface representations, and procedural

(algorithmic) detail. The design process translates requirements into a representation of

the software that can be assessed for quality before coding begins. Like requirements, the

design is documented and becomes part of the software configuration. In design phase,

overall watermarking system is described with the help of data flow diagrams as show

below.



DATA FLOW DIAGRAM:

LEVEL 0 DFD:

DFD just gives the idea about the how the system will look likes.

LEVEL 1 DFD:

Cover Image

Secret Image

Secret Image


Original Image Display

Stage 1

Embedding

Watermarked Image (W’)

Stage 2

Extraction

Watermarking System


Level 1 DFD gives the overall working of system. First the cover image and secret

image are given to embedding system to produce watermarked image. This watermarked

image is then displayed,

From This watermarked image, watermark is extracted which compared with the inserted

watermark.

9.2 PROCESS DESCRIPTION:

1) ORIGINAL IMAGE:

Suppose that an image with the size of M×N (suppose that M and N both are the integral times of

eight) we want to watermarked. Then we first divide this image into blocks of 8×8 pixels, and

then the total number of blocks is defined as L, which equals (M×N) /64. Suppose that Dire

presents the ith (i=1, 2… L) block.

2) DCT TRANSFORM:

Using the DCT equation, DCT coefficient of each block is calculated and are represented by Di

DCT EQUATION:

M-1 N-1

y(u,v)= a(u)a(v) ∑ ∑ x(m,n) cos[(2m+1)uP/2M] cos[(2n+1)vP/2N]

u=0 v=0

Where, u, v =0, 1, 2…..N-1.

Then each DCT coefficient of Di will be arranged in sequential order.

DCT coefficients whose orders are from num1 to num2

(48≤num1<num2≤63) in each Di compose a set of high frequency



Coefficients called GPi. The number and the total value of elements in each GPi are respectively

named Num(=num2-num1+1) and Si.

First, each block of original images will be DCTed, and the Si’s interval, which is [T1, T2], of

each block will be achieved easily. Vis defined as V= (T1+T2)/2

3) WATERMARK: The operations which we have done on original image are done on

watermark but we are not calculating the DCT coefficient of it.

4) EMBEDDING OF WATERMARK/WATERMARKED IMAGE:

Each bit of watermarks is embedded into a block. If the embedded bit is 0 watermarks is 0, then

the corresponding block is not changed; if the bit is 1, then the block to be watermarked will be

made such changes:

1) If Si ≥V, then all the elements in GPi will be added by the value named val1 so that Si>T2.

2) If Si<V, then all the elements in GPi will be subtracted by the value named val2 so that Si<T1.

The purpose of above measures is to make the value of Si out of the interval of [T1, T2] after the

element’s coefficients of GPi are modified.

5) INVERSE DCT TRANSFORM:

The opposite act to the embedding is extraction. The original image is not needed in the

extraction of watermarks. The main ideas of extracting watermarks are as

Follows:

M-1 N-1

x(m,n)= ∑ ∑ a(v)a(u) y( u, v) cos[(2m+1)uP/2M] cos[(2n+1)vP/2N]

u=0 v=0

First, each block of watermarked images is DCTed ; then the Si of each block is achieved.

According to the embedding procedure of watermarks, if the embedded bit of watermarks is 1,

the Si of this block won’t be in the interval



Of [T1, T2], by which the embedded bit 1 of watermarks will be extracted; otherwise, if the Si of

this block is in the interval of [T1, T2], then the extracted bit of watermarks will be 0. Because

the modified value of DCT coefficients is fixed when the watermarks are embedded, the

watermarks will be reversed if the operations opposite to the embedding procedures are made

and original image can be achieved.

VISIBLE WATERMARKING

Visible watermarks change the signal altogether such that the watermarked signal is totally different from the actual signal, e.g., adding an image as a watermark to another image. Stock photography agencies often add a watermark in the shape of a copyright symbol ("©") to previews of their images, so that the previews do not substitute for high-quality copies of the product included with a license.

Visible watermarks can be used in following cases:

* Visible watermarking for enhanced copyright protection.

In such situations, where images are made available through Internet and the content owner is concerned that the images will be used commercially (e.g. imprinting coffee mugs) without payment of royalties. Here the content owner desires an ownership mark, that is visually apparent, but which does not prevent image being used for other purposes (e.g. scholarly research).

* Visible watermarking used to indicate ownership originals.

In this case images are made available through the Internet and the content owner desires to indicate the ownership of the underlying materials (library manuscript), so an observer might be encouraged to patronize the institutions that own the material.



CHAPTER 10

IMPLEMENTATION PLAN

In Digital Image watermarking project, test will be performed to see if the watermark satisfies

the desired properties explained in chapter 1.

Since a watermark is merely a sequence of pseudo-random numbers, error free detection may be

possible by using linear block codes. With the exception of most watermarking schemes do not

employ error-correction. This project will attempt to implement a new watermarking method

using error-correction techniques.

In implementing this, we will have to learn and create the processes to add a watermark and

extract a watermark from digital images.

Image processing and other computations will be handled by MATLAB. Furthermore,

this project seeks to determine if the error-correcting watermark scheme will hold any

advantages over traditional watermarking methods.

Architectural Design

Data Design

Coding

Testing

Report Generation

Maintenance

Architectural Design

Architectural design represents the structure of data and program components that are required to

build a mobile-based system. It considers the architectural style that the system will take, the

structure and properties of the components that comprise the system, and the interrelationships

that occur among all architectural components of a system.



Data Design

This stage will include the basic design as well as the flow of information .The DFDs that have

been implemented in the earlier stage needs to be materialized within this stage. This stage will

also include good user interface design that would enable the customer to use multiple tags

which will increase user friendliness of the software.

Coding

A comprehensive coding standard encompasses all aspects of code construction using

MATLAB. Complete source code should enable us to get desired results.

Testing

This phase should accomplish a variety of things such as testing of various modules within the

project. This is a high priority task as we may come across certain error that needs to be resolved

in time.

Report Generation

This stage will include various reports that need to be generated which will be utilized for

documentation purpose.

Maintenance

Maintenance plan will enable us to check the compatibility of the application with various

operating systems. Once the application deployed on the PC if there exists any error that needs to

be sorted out.

CHAPTER 11



ALGORITHM

Watermark Embedding

1) Original images are divided into blocks of 8×8 Pixels.

2) On each block DCT is applied.

3) Set of high frequency coefficients called GPi is calculated.

4) Intervals called [T1, T2] of Si are obtained, and define V= (T1+T2)/2.

5) Binary sequence of watermark is produced i.e. WI=1/0.

6) If Wi =0, keep GPi as it is.

7) If Wi≠0 A] If V≤Si≤T2, then each element of GPiis added by val1, which equals (T2-V)/Num, so we have T2≤Si≤2T2-V.

B] If T1≤Si<V, then each element in GPi is subtracted by val2, which equals (V-T1)/Num, so we have 2T1-V≤Si<T1.

8) All the blocks are applied IDCT to get watermarked image.

Watermark Extracting

1) Watermarked image is divided into blocks of 8x8 pixels, and then each block is applied DCT.

2) Achieve Si of each block.

3) IfT2 ≤Si≤2T2-V, then we have Wi*=1. Each element in GPi is subtracted by val1, by which the watermarks are reversed.

4) If 2T1-V≤Si<T1, then we still have Wi*=1. Each element in GPi is added by val2, by which the watermarks are reversed.

5) Otherwise, we have Wi*=0.

6) All the blocks are applied IDCT to get original image.

INVISIBLE WATERMARK EMBEDDING CODES:



function varargout = GUI1(varargin)global wimg;global oimg;global watermarked_image_int; gui_Singleton = 1;gui_State = struct('gui_Name', mfilename, ... 'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @GUI1_OpeningFcn, ... 'gui_OutputFcn', @GUI1_OutputFcn, ... 'gui_LayoutFcn', [] , ... 'gui_Callback', []);if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1});end if nargout [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});else gui_mainfcn(gui_State, varargin{:});end% End initialization code function GUI1_OpeningFcn(hObject, eventdata, handles, varargin)% This function has no output args, see OutputFcn.% hObject handle to figure% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% varargin command line arguments to GUI1 (see VARARGIN) % Choose default command line output for GUI1handles.output = hObject; % Update handles structureguidata(hObject, handles); % UIWAIT makes GUI1 wait for user response (see UIRESUME)% uiwait(handles.figure1); % --- Outputs from this function are returned to the command line.function varargout = GUI1_OutputFcn(hObject, eventdata, handles) % varargout cell array for returning output args (see VARARGOUT);% hObject handle to figure% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA) % Get default command line output from handles structurevarargout{1} = handles.output; % --- Executes on button press in pushbutton2.function pushbutton2_Callback(hObject, eventdata, handles)% hObject handle to pushbutton2 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)global wimg;global oimg;



[ans,pathname]=uigetfile( ...{'*.*'... }, ... 'Select an IMAGE'); if isequal(ans,0);warndlg('ACTION CANCELLED',' ');end tryoimg=imread([pathname ans]); oimg=imresize(oimg,[256 256]); save aa oimg;handles.currentdata=oimg;axes(handles.axes1)imshow(oimg)guidata(hObject,handles);catchwarndlg('INCORRECT FILE FORMAT',' ') end % --- Executes on button press in pushbutton3.function pushbutton3_Callback(hObject, eventdata, handles)% hObject handle to pushbutton3 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)global wimg;global oimg; [ans,pathname]=uigetfile( ...{'*.bmp'... }, ... 'Select an WATERMARK'); if isequal(ans,0);warndlg('ACTION CANCELLED',' ');end trywimg=imread([pathname ans]); wimg=imresize(wimg,[32 32]);handles.currentdata=wimg;axes(handles.axes2)imshow(wimg)guidata(hObject,handles);catchwarndlg('INCORRECT FILE FORMAT',' ') end % --- Executes on button press in pushbutton4.function pushbutton4_Callback(hObject, eventdata, handles)% hObject handle to pushbutton4 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)



global wimg; global oimg; global watermarked_image_int;

k=50; % set minimum coeff difference blocksize=8;% set the size of the block in cover to be used for each bit in watermark % read in the cover objectfile_name=oimg; cover_object=double(oimg); % determine size of cover imageMc=size(cover_object,1); %HeightNc=size(cover_object,2); %Width % determine maximum message size based on cover object, and blocksizemax_message=Mc*Nc/(blocksize^2);message=double(wimg); Mm=size(message,1); %HeightNm=size(message,2); %Width % reshape the message to a vectormessage=reshape(message,Mm*Nm,1);message=message(1:max_message);message_pad(1:length(message))=message; % generate shell of watermarked imagewatermarked_image=cover_object; % process the image in blocks% encodes such that (5,2) > (4,3) when message(kk)=0% and that (5,2) < (4,3) when message(kk)=1x=1;y=1;for (kk = 1:length(message_pad)) % transform block using DCT dct_block=dct2(cover_object(y:y+blocksize-1,x:x+blocksize-1)); % if message bit is black, (5,2) > (4,3) if (message_pad(kk) == 0) % if (5,2) < (4,3) then we need to swap them if (dct_block(5,2) < dct_block(4,3)) temp=dct_block(4,3); dct_block(4,3)=dct_block(5,2); dct_block(5,2)=temp; end % if message bit is white, (5,2) < (4,3) elseif (message_pad(kk) == 1)



% if (5,2) > (4,3) then we need to swap them if (dct_block(5,2) >= dct_block(4,3)) temp=dct_block(4,3); dct_block(4,3)=dct_block(5,2); dct_block(5,2)=temp; end end % now we adjust the two values such that their difference >= k if dct_block(5,2) > dct_block(4,3) if dct_block(5,2) - dct_block(4,3) < k dct_block(5,2)=dct_block(5,2)+(k/2); dct_block(4,3)=dct_block(4,3)-(k/2); end else if dct_block(4,3) - dct_block(5,2) < k dct_block(4,3)=dct_block(4,3)+(k/2); dct_block(5,2)=dct_block(5,2)-(k/2); end end % transform block back into spatial domain watermarked_image(y:y+blocksize-1,x:x+blocksize-1)=idct2(dct_block); % move on to next block. if (x+blocksize) >= Nc x=1; y=y+blocksize; else x=x+blocksize; endend % convert to uint8 and write the watermarked image out to a filewatermarked_image_int=uint8(watermarked_image); axes(handles.axes3)imshow(watermarked_image_int)guidata(hObject,handles); GUI2;

INVISIBLE WATERMARK RECOVERY CODES:

function varargout = GUI2(varargin)global wimg;global oimg;global watermarked_image_int;% GUI2 M-file for GUI2.fig% GUI2, by itself, creates a new GUI2 or raises the existing% singleton*.



%% H = GUI2 returns the handle to a new GUI2 or the handle to% the existing singleton*.%% GUI2('CALLBACK',hObject,eventData,handles,...) calls the local% function named CALLBACK in GUI2.M with the given input arguments.%% GUI2('Property','Value',...) creates a new GUI2 or raises the% existing singleton*. Starting from the left, property value pairs are% applied to the GUI before GUI2_OpeningFcn gets called. An% unrecognized property name or invalid value makes property application% stop. All inputs are passed to GUI2_OpeningFcn via varargin.%% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one% instance to run (singleton)".%% See also: GUIDE, GUIDATA, GUIHANDLES % Edit the above text to modify the response to help GUI2 % Last Modified by GUIDE v2.5 09-Apr-2014 14:10:43 % Begin initialization code - DO NOT EDITgui_Singleton = 1;gui_State = struct('gui_Name', mfilename, ... 'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @GUI2_OpeningFcn, ... 'gui_OutputFcn', @GUI2_OutputFcn, ... 'gui_LayoutFcn', [] , ... 'gui_Callback', []);if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1});end if nargout [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});else gui_mainfcn(gui_State, varargin{:});end% End initialization code - DO NOT EDIT % --- Executes just before GUI2 is made visible.function GUI2_OpeningFcn(hObject, eventdata, handles, varargin)% This function has no output args, see OutputFcn.% hObject handle to figure% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% varargin command line arguments to GUI2 (see VARARGIN) % Choose default command line output for GUI2handles.output = hObject; % Update handles structureguidata(hObject, handles); % UIWAIT makes GUI2 wait for user response (see UIRESUME)



% uiwait(handles.figure1); % --- Outputs from this function are returned to the command line.function varargout = GUI2_OutputFcn(hObject, eventdata, handles) % varargout cell array for returning output args (see VARARGOUT);% hObject handle to figure% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA) % Get default command line output from handles structurevarargout{1} = handles.output; % --- Executes on button press in pushbutton1.function pushbutton1_Callback(hObject, eventdata, handles)% hObject handle to pushbutton1 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)global watermarked_image_int;axes(handles.axes1)imshow(watermarked_image_int)guidata(hObject,handles);% --- Executes on button press in pushbutton2.function pushbutton2_Callback(hObject, eventdata, handles)% hObject handle to pushbutton2 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)

global watermarked_image_int;blocksize=8; % set the size of the block in cover to be used for each bit in watermarkwatermarked_image=watermarked_image_int;% read in the watermarked object % determine size of watermarked imageMw=size(watermarked_image,1); %HeightNw=size(watermarked_image,2); %Width % determine maximum message size based on cover object, and blocksizemax_message=Mw*Nw/(blocksize^2); % process the image in blocksx=1;y=1;for (kk = 1:max_message) % transform block using DCT dct_block=dct2(watermarked_image(y:y+blocksize-1,x:x+blocksize-1)); % if dct_block(5,2) > dct_block(4,3) then message(kk)=0 % otherwise message(kk)=1 if dct_block(5,2) > dct_block(4,3) message_vector(kk)=0; else



message_vector(kk)=1; end % move on to next block. if (x+blocksize) >= Nw x=1; y=y+blocksize; else x=x+blocksize; endend % reshape the embeded messagemessage=reshape(message_vector(1:1024),32,32);axes(handles.axes2)imshow(message)guidata(hObject,handles); % --- Executes on button press in pushbutton3.function pushbutton3_Callback(hObject, eventdata, handles)% hObject handle to pushbutton3 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)close(gcbf); % --- Executes on button press in pushbutton4.function pushbutton4_Callback(hObject, eventdata, handles)% hObject handle to pushbutton4 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)global watermarked_image_int;load aa oimg; [psnr mse]=psnrr(oimg,watermarked_image_int);set(handles.edit1,'String',mse);set(handles.edit2,'String',psnr); function edit1_Callback(hObject, eventdata, handles)% hObject handle to edit1 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA) % Hints: get(hObject,'String') returns contents of edit1 as text% str2double(get(hObject,'String')) returns contents of edit1 as a double % --- Executes during object creation, after setting all properties.function edit1_CreateFcn(hObject, eventdata, handles)% hObject handle to edit1 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles empty - handles not created until after all CreateFcns called % Hint: edit controls usually have a white background on Windows.% See ISPC and COMPUTER.



if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) set(hObject,'BackgroundColor','white');end function edit2_Callback(hObject, eventdata, handles)% hObject handle to edit2 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA) % Hints: get(hObject,'String') returns contents of edit2 as text% str2double(get(hObject,'String')) returns contents of edit2 as a double % --- Executes during object creation, after setting all properties.function edit2_CreateFcn(hObject, eventdata, handles)% hObject handle to edit2 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles empty - handles not created until after all CreateFcns called % Hint: edit controls usually have a white background on Windows.% See ISPC and COMPUTER.if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) set(hObject,'BackgroundColor','white');end

Using the Invisible Watermarking codes we also get the PSNR and the MSE value of the images used for the watermarking .Now in order to get the efficiency and accuracy of invisible watermarking technique, we implement the visible watermarking codes and compare the results of both. So below given codes are implementing the visible watermarking technique on the same images which were taken in the previous technique.

VISIBLE WATERMARK EMBEDDING CODES:

clcclear all%file_name='lena.bmp';[f p]=uigetfile('*.*','Select cover image');file_name=strcat(p,f);



save a file_name;% file_name='beach.jpg';cover_object=double(imread(file_name));Mc=size(cover_object,1);Nc=size(cover_object,2);[f p]=uigetfile('*.*','Select watermark');file_name=strcat(p,f);%file_name='watermark.bmp';message=double(imread(file_name));Mm=size(message,1);Nm=size(message,2);[ca,ch,cv,cd]=dwt2(cover_object,'haar');cal=ca; for i=1:Mm for j=1:Nm if(message(i,j)==0) cal(i,j)=ca(i,j)+50; end end endwatermarked_image=idwt2(cal,ch,cv,cd,'haar');watermarked_image_round=round(watermarked_image);watermarked_image_uint8=uint8(watermarked_image_round);imshow(watermarked_image_uint8,[]);figure;imshow(watermarked_image_uint8,[]);title('watermarked image');imwrite(watermarked_image_uint8,'dwt_watermarked.bmp','bmp');

VISIBLE WATERMARK RECOVERY CODES:

clear all% [f p]=uigetfile('*.*','Select watermarked image');% file_name=strcat(p,f);%file_name='dwt_watermarked.bmp';watermarked_image=double(imread('dwt_watermarked.bmp'));watermarked_image(200:250,200:250)=watermarked_image(200:250,200:250)+1;[ca1,ch1,cv1,cd1]=dwt2(watermarked_image,'haar');load a;cover_object=double(imread(file_name));[ca,ch,cv,cd]=dwt2(cover_object,'haar');watermarkR=ca1-ca;[Mm,Nm]=size(ca);watermark=ones(Mm,Nm);for k=1:Mm for h=1:Nm



if(watermarkR(k,h)~=0) watermark(k,h)=0; end end end figure(2)imshow(watermark,[]);title('Recovered watermark');imwrite(watermark,'watermark_R.bmp','bmp');[psnr mse]=psnrr(imread(file_name),imread('dwt_watermarked.bmp'))

PSNR CODES:

function [psn mse] = psnrr(x,p)si=size(x);m=si(1);n=si(2);x=double(x);p=double(p);mse=0;for i=1:m for j=1:n mse=mse+(x(i,j)-p(i,j))^2; endendmse=mse/(m*n);psn=10*log10((255^2)/mse);

CHAPTER 12

RESULTS



Fig 12.a Lena

Fig 12.b Beach



Fig 12.c baboon

Fig 12.d Jersey

Fig 12.e Sample



INVISIBLE WATERMARKING RESULTS IN TERMS OF PSNR AND MSE:

IMAGE WATERMARK MSE PSNR

Lena W 28.1652 33.6337

Beach W 28.074 33.6478

Baboon W 46.3062 31.4744

Jersey W 25.8055 34.0137

Sample W 25.4328 34.0769

VISIBLE WATERMARKING RESULTS IN TERMS OF PSNR AND MSE:

IMAGE WATERMARK MSE PSNR

Lena W 41.7413 31.9251

Beach W 68.2267 29.7912

Baboon W 41.7413 31.9251

Jersey W 55.6546 30.6758

Sample W 36.5599 32.5008



CHAPTER 13

APPLICATIONS

1) Copyright Protection:-

Through this we can prevent copying of digital images. In this use, a copy device

retrieves the watermark from the signal before making a copy; the device makes a

decision whether to copy or not, depending on the contents of the watermark.

2) Identity Cards:-

. The identity number "123456789" is written in clear text on the card and hidden as a

digital watermark in the identity photo. Therefore switching or manipulating the identity

photo will be detected.

(Fig 12.1-Example of hidden digital watermark)

3) In medical and military fields:-

Important Images can be send using digital watermarks.



BIBLIOGRAPHY

1) A Reversible and Fragile Watermarking Algorithm Based on DCT, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

Yi Du, School of Computer and Information, Shanghai Second Polytechnic University, Shanghai, China.

[email protected]

Ting Zhang, the 28th Research Institute, China Electronics Technology Group Corporation, Nanjing, China.

[email protected]

2) ECE 802 – 602: Information Theory and Coding The Discrete Cosine Transform: Theory and Application

Syed Ali Khayam , Department of Electrical & Computer Engineering, Michigan State University.

3) 3rd International Conference on Industrial Informatics (INDIN 2005)©2005 IEEE.

Vidyasagar M. Potdar, Song Han, Elizabeth Chang A Survey of Digital Image Watermarking Techniques School of Information Systems, Curtin University of Technology, Perth, Western Australia

4) International Journal of Engineering and Scientific Research Volume 2

Anitha S

5)Ingemar J. Cox, Matthew L. Miller, Jeffrey A. Bloom, Jessica Fridrich and TonKalker, “Digital Watermarking and Steganography" (Second Edition), Morgan Kaufmann, 2008

6) Digital Image Processing, 3rd edition, Rafael C .Gonzalez and Richard E. Woods.

7) Image compression and DCT, Ken Cabeen and Peter Gent.


mailto:[email protected]

mailto:[email protected]

project report on eda manufacturing

Documents

watermarking image

original image

kind of watermarking

visible watermarking

domain watermark

case of invisible watermarking

image authentication

primary host image