Issue 4 December 2015 International Journal of Informative ... · PDF fileInternational...

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Available online through - http://ijifr.com/searchjournal.aspx Published On: December 30, 2015

Copyright©IJIFR 2015

International Journal of Informative & Futuristic Research ISSN: 2347-1697

Volume 3 Issue 4 December 2015 Reviewed Paper

Abstract

A secure image transmission strategy is proposed, which changes consequently a given substantial volume mystery images into a purported mystery piece unmistakable mosaic images of the same size. The mosaic images, which seems to be like a subjectively chose target images and may be utilized as a disguise of the mystery images, is yielded by isolating the mystery images into parts and changing their shade qualities to be those of the relating pieces of the target images. Capable strategies are intended to direct the shade change process so that the mystery images may be recouped about lossless. A plan of taking care of the floods/undercurrents in the changed over pixels' shade values by recording the color contrasts in the untransformed shade space is additionally proposed. The data needed for recuperating the mystery images is inserted into the made mosaic images by a lossless information concealing plan utilizing a key. Prodigious exploratory results demonstrate the attainability of the proposed system.

1. Introduction

Images from different sources are every now and again used and transmitted through the web for

different applications, for example, online individual photo collections, private venture chronicles,

report stockpiling frameworks, the rapeutic imaging frameworks, and military Images databases.

These Images typically contain private or secret data so that they ought to be secured from spillages

amid transmissions. As of late, numerous routines have been proposed for securing Images

A New Secure Image Transmission Technique

Via Secret Fragment Visible Mosaic Images

By Nearly Reversible Color Transmission Paper ID IJIFR/ V3/ E4/ 070 Page No. 1423-1432 Subject Area

Elec. & Comm.

Engineering

Keywords Encryption, Decryption, Mosaic Images, Uniformity, Variance

1st Jayampu Manohar

Associate Professor,

Department Of Elec. & Comm. Engineering

Jagan’s College of Engineering & Technology,

Chavatapalem, Nellore(Andhra Pradesh)-India

2nd Dr .K.V.Ramanaiah

Professor, Department Of Elec. & Comm. Engineering Yogi Vemana University , Proddutur, Kadappa (Andhra Pradesh)-India

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ISSN: 2347-1697 International Journal of Informative & Futuristic Research (IJIFR)

Volume - 3, Issue -4, December 2015 Continuous 28th Edition, Page No.:1423-1432

Jayam pu Manohar, Dr .K.V.Ram anaiah :: A New Secure Im age Transm ission Technique Via Secret Fragm ent Visible Mosaic Im ages By

Nearly Reversible Color Transm ission

transmission, for which two regular methodologies are Images encryption and information stowing

away. Images encryption is a strategy that makes utilization of the common property of an image,

for example, high repetition and solid spatial relationship, to get an encoded image based on

Shannon's disarray and dissemination properties.

2. Proposed Method

2.1 Color Transformations

Color can be described by its red (R), green (G) and blue (B) coordinates (the well-known RGB

system), or by some its linear transformation as XYZ, CMY, YUV, IQ, among others. The CIE

adopted systems CIELAB and CIELUV, in which, to a good approximation, equal changes in the

coordinates result in equal changes in perception of the color.

The present color models have some disadvantages in practical use. E.g. we convert an

image in some image processing application into some brightness-hue-saturation model and we

would like to work with individual components (coordinates) as with separate images. There is

desirable regarding to the back conversion to have all combinations of the values. It means we need

such model, where the range of values of saturation is identical for all hues. From this point of

view, the GLHS color model is probably the best from the current ones, particularly for wmin = wmid

= wmax = 1/3.

2.2 Image Encryption Techniques

Due to the rapid growth of digital communication and multimedia application, security

becomes an important issue of communication and storage of images. Encryption is one of the ways

to ensure high security images are used in many fields such as medical science, military.

The process of

encoding plain text messages

into cipher text messages is

called encryption. And the

reverse process of

transforming cipher text back

to plain text is called as

decryption. Image and video

encryption have applications

in various fields including

internet communication,

multimedia systems, medical

imaging, Tele-medicine and

military communication. Figure 1: Image Encryption

2.3 Techniques For Data hiding

Data hiding, a form of steganography, embeds data into digital media for the purpose of

identification, annotation, and copyright. Several constraints affect this process: the quantity of data

to be hidden, the need for invariance of these data under conditions where a “host” signal is subject

to distortions, e.g., lossy compression, and the degree to which the data must be immune to

interception, modification, or removal by a third-party.

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2.4 Trade-Offs Exists Between The Quantities Of Data And The Immunity To Modification

Since the information is of a critical nature and the signal may face intelligent and

intentional attempts to destroy or remove it, the coding techniques used must be immune to a wide

variety of possible modifications. A second application for data hiding is tamper-proofing. It is used

to indicate that the host signal has been modified from its authored state. Image and audio captions

(or annotations) may require a large amount of data.

Figure 2: Conceptual data-hiding problem spaces

2.5 Data hiding in still images:

Data hiding in still images presents a variety of challenges that arise due to the way the

human visual system (HVS) works and the typical modifications that images undergo. Additionally,

still images provide are natively small host signal in which to hide data. Fairly typical 8-bit Images

of 200 pixels provide approximately 40 kilobytes (kb) of data space in which to work. This is

equivalent to only around 5seconds of telephone-quality audio or less than a single frame of NTSC

television.

2.6 Patchwork: A statistical approach

The statistical approach, which we refer to as Patchwork , is based on a pseudorandom,

statistical process. Patchwork invisibly embeds in a host image a specific statistic, one that has a

Gaussian distribution.

Table. 2.1: Degree of certainty of encoding given deviation from that expected in a Gaussian

distribution ∆=2

Standard deviations away Certainty N

0 50.00% 0

1 84.13% 679

2 97.87% 2713

3 99.87% 6104

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So each step of the way we accumulate an expectation. Thus after n repetitions, we expect S1nto be:

Figure 3: As Δ or n increases, the distribution of shiftsS1n further to the right

Figure 4: The contour of a patch largely determines which frequencies will be modified by the

application of patchwork

Figure 5: Patch placement affects patch visibility

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2.7 Uniformity

A simplifying assumption of a uniform luminance histogram was made above, but this is

not a requirement of Patchwork. The only assumption Patchwork makes is that the expected value

of Si = ai-bi is zero.

It can be shown that this condition is always met through the following argument:

1. Let ar be the time reversed series of a.

2. Ar= A* by definition (A* is the complex conjugate of A).

3. F (a*ar) = AA* (F is the Fourier transform).

4. AA* is everywhere real by definition of the complex conjugate.

5. F–1(AA*) is even by definition.

6. Even sequences are symmetric around zero by definition.

An image histogram is some-what random distribution. The result of taking the complex conjugate

is symmetric around zero.

Figure 6: A histogram and its auto correlation

Figure 8: A histogram of the variance of the luminance of 365 associated press photos

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Figure 9: Histograms of pictures with minimum and maximum variance

2.8 Variance When searching through a large number of images with data embedded using the Patch

work method, such as when a robot is looking for copyright violations on the Internet World Wide

Web (WWW), the use of a generic estimation of variance is desirable. This avoids the necessity of

calculating the variance of every image. Suspect images can then be examined thoroughly.

2.10 Calibration Of Image Imperfections

We bought a very expensive microscope and camera so the results have got to be perfect. First of

all, all imaging systems suffer to a greater or lesser extent from barrel or pincushion distortion, or

combinations thereof.

Figure 10: Examples of increasing amounts of barrel (negative) and pincushion (positive) geometric

distortion in an image

It is straight forward to correct this type of distortion if the shape of the distortion function is

known. For simplicity and in practice this is more than adequate, a symmetric distortion function

can be assumed (i.e. symmetric about the center of the image).

3. Matlab Working Environments

Matlab Desktop

Matlab Desktop is the main Matlab application window. The desktop contains five sub windows,

the command window, the workspace browser, the current directory window, the command

history window, and one or more figure windows, which are shown only when the user displays a

graphic.

Getting Help

The principal way to get help online is to use the MATLAB help browser, opened as a

separate window either by clicking on the question mark symbol (?) on the desktop toolbar, or by

typing help browser at the prompt in the command window. The help Browser is a web browser

integrated into the MATLAB desktop that displays a Hypertext Markup Language (HTML)

documents.

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4. Digital Image Processing

Digital image processing is an area characterized by the need for extensive experimental work to

establish the viability of proposed solutions to a given problem. An important characteristic

underlying the design of image processing systems is the significant level of testing &

experimentation that normally is required before arriving at an acceptable solution. An image may

be defined as a two-dimensional function f(x, y), where x & y are spatial coordinates & the

amplitude at any pair of coordinates (x, y) is called the intensity or gray level of the image at that

point. When x, y & the amplitude values of f are all finite discrete quantities, we call the image a

digital image. An image is represented as a two dimensional function f(x, y) where x and y

are spatial co-ordinates and the amplitude of „f‟ at any pair of coordinates (x, y) is called

the intensity of the image at that point.

Gray scale image:

A grayscale image is a function I(xylem) of the two spatial coordinates of the image

plane. I(x, y) is the intensity of the image at the point (x, y) on the image plane. I (xylem)takes non-

negative values assume the image is bounded by a rectangle[0, a] [0, b]I: [0, a] [0, b] [0, info)

Color image:

It can be represented by three functions, R (xylem) for red, G (xylem) for green and B

(xylem) for blue. An image may be continuous with respect to the x and y coordinates and also in

amplitude.

Coordinate convention:

The result of sampling and quantization is a matrix of real numbers. We use two principal

ways to represent digital images. Assume that an image f(x, y) is sampled so that the resulting

image has M rows and N columns.

5. Outcomes

Figure 11:A simple target image

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Figure 12: Secret image to be sent

Figure 13: Color transformed image

Figure 14: Command window showing matching of key

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Figure 15: Mosaic image

Figure 16: Recovered image

6. Conclusion

A secure Image transmission technique has been proposed, which not just can make genuine

mosaic Images at the same time likewise can change a mystery Images into a mosaic one with the

same information size for utilization as a disguise of the mystery Images. By the utilization of

fitting pixel color changes and an adroit plan for taking care of floods and undercurrents in the

changed over estimations of the pixels' colors, mystery fragment visible mosaic Images with high

visual similitude‟s to subjectively chose target Images can be made with no need of a target Images

database. Likewise, the first mystery Images can be recouped about lossless from the made mosaic

Images. Great test results have demonstrated the plausibility of the proposed technique. Future

studies may be guided to applying the proposed technique to Images of shade models other than the

RGB.

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7. References

[1] Digital image processing by R.C.Gonzalez&R.E.Woods - 1st

edition.

[2] .Digital image processing by R.C.Gonzalez&R.E.Woods- 2nd

edition.

[3] Digital image processing by R.C.Gonzalez&R.E.Woods - 3rd

edition.

[4] Digital image processing using MATLAB by R.C.Gonzalez&R.E.Woods.

[5] Fundamentals of image processing by A.K.Jain.

[6] Digital image processing by S Jayaraman& S Esakkirajan.

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