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More Secured Steganography Model With High Concealing Capacity by Using Genetic Algorithm, Integer Wavelet Transform and OPAP

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Steganography is an art of writing for conveying message inside another media in a secret way that can only be detected by its intended recipient. There are security agents who would like to fight these data hiding systems by steganalysis, i.e. discovering covered secret messages and rendering them useless. Steganalysis is the art of detecting the message's existence, message length or place of message where it is to be hidden in covered media and blockading the covert communication. There is currently no more secured steganography system which can resist all steganalysis attacks such as visual attack, statistical attack (active and passive) or structural attack. The most notable steganalysis algorithm is the Reversible Statistical attack which detects the embedded message by the statistic analysis of pixel values. To maintain the security against the Reversible Statistical analysis, the proposed work presents a new steganography model based on Genetic Algorithm using Integer Wavelet Transform. We present a novel approach to resolve such problems of substitution technique of image steganography. Using the proposed Genetic Algorithm and Reversible Statistical analysis Algorithm, the system is more secured against attacks and increases robustness. The robustness would be increased against those attacks which try to reveal the hidden message and also some unintentional attacks like noise addition as well. In this proposed work, we studied the steganographic paradigm of data hiding in standard digital images. In recent literature, some algorithms have been proposed where marginal statistics are preserved for achieving more capacity and more security. This proposed system presents a novel technique to increase the data hiding capacity and the imperceptibility of the image after embedding the secret message. In proposed work Optimal Pixel Adjustment Process also applied to minimize the error difference between the cover and stego image. By this work best results have been obtained as compared to existing works. The proposed steganography model reduces the embedding error and provides higher embedding capacity. Detection of message existence will be very hard for those stego images that produced using the proposed method. This work shows the highest embedding capacity and security against Reversible Statistical attack.
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  • International Journal on Recent and Innovation Trends in Computing and Communication ISSN 2321 8169 Volume: 1 Issue: 4 394 408

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    More Secured Steganography Model with High Concealing Capacity by using

    Genetic Algorithm, Integer Wavelet Transform and OPAP

    Jyoti 1

    M.Tech. Scholar, Digital Communication, Rajasthan Technical University-Kota

    Department of Electronics & Communication Engineering

    Sobhasaria Engineering College, Sikar, Rajasthan, India

    E-mail: [email protected]

    Md. Sabir 2

    Assistant Professor

    Department of Electronics & Communication Engineering

    Sobhasaria Engineering College, Sikar, Rajasthan, India E-mail: [email protected]

    Abstract: Steganography is an art of writing for conveying message inside another media in a secret way that can only be detected

    by its intended recipient. There are security agents who would like to fight these data hiding systems by steganalysis, i.e.

    discovering covered secret messages and rendering them useless. Steganalysis is the art of detecting the message's existence,

    message length or place of message where it is to be hidden in covered media and blockading the covert communication. There is

    currently no more secured steganography system which can resist all steganalysis attacks such as visual attack, statistical attack

    (active and passive) or structural attack. The most notable steganalysis algorithm is the Reversible Statistical attack which detects

    the embedded message by the statistic analysis of pixel values. To maintain the security against the Reversible Statistical analysis,

    the proposed work presents a new steganography model based on Genetic Algorithm using Integer Wavelet Transform. We

    present a novel approach to resolve such problems of substitution technique of image steganography. Using the proposed Genetic

    Algorithm and Reversible Statistical analysis Algorithm, the system is more secured against attacks and increases robustness. The

    robustness would be increased against those attacks which try to reveal the hidden message and also some unintentional attacks

    like noise addition as well. In this proposed work, we studied the steganographic paradigm of data hiding in standard digital

    images. In recent literature, some algorithms have been proposed where marginal statistics are preserved for achieving more

    capacity and more security. This proposed system presents a novel technique to increase the data hiding capacity and the

    imperceptibility of the image after embedding the secret message. In proposed work Optimal Pixel Adjustment Process also

    applied to minimize the error difference between the cover and stego image. By this work best results have been obtained as

    compared to existing works. The proposed steganography model reduces the embedding error and provides higher embedding

    capacity. Detection of message existence will be very hard for those stego images that produced using the proposed method. This

    work shows the highest embedding capacity and security against Reversible Statistical attack.

    Keywords: Genetic Algorithm, IWT, OPAP, RS Analysis.

    _____________________________________________________*****______________________________________________________

    I. INTRODUCTION

    The standard and thought of What You See Is What You Get (WYSIWYG) which we have a tendency to encounter typically while printing images or other

    materials, is no longer precise and would not mislead a

    steganographer as it does not always hold true. Images are

    over what we see with our Human Visual System (HVS);

    therefore, they can convey over 1000 words [1].

    Steganography, the art of hiding messages inside other

    messages, is now gaining more popularity and is being

    used on various media such as text, images, sound, and signals. However, none of the existing schemes can yet

    defend against all type of detection attacks. Using GAs that are based on the procedures of natural genetics and

    the theory of evolution, we can design a general method

    to guide the steganography process to the best position for

    data hiding [2].

    Steganography is the art of hiding information

    imperceptibly in a cover media. The word

    "Steganography" is Greek word which means concealed writing. Where Stegano means "protected or covered and graphy - to write". Steganography is the art and science of hiding communication; a steganographic

    system so embeds hidden content in unremarkable cover

    media so as not to arouse an eavesdroppers suspicion. In the past, individuals used hidden tattoos or invisible ink to

    convey steganographic content. Today, personal computer (PC) and network technologies give easy-to-use

    communication channels for steganography.

    Essentially, the information-hiding process in a

    steganographic system starts by identifying a cover

    mediums redundant bits (those that can be modified without destroying that mediums integrity) [3]. The

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    embedding process creates a stego medium by replacing

    these redundant bits with data from the hidden message.

    Modern steganographys goal is to stay its mere presence undetectable, but steganographic systems, thanks to their

    invasive nature, leave behind detectable traces within the

    cover medium. Although secret content is not discovered,

    the very existence of it is: modifying the cover medium changes its statistical properties, thus eavesdroppers can

    notice the distortions within the resulting stego mediums statistical properties. The strategy of finding these

    distortions is named statistical steganalysis.

    The purpose of steganography is to hide the presence of

    communication while the purpose of cryptography is to

    make the communication incomprehensible by modifying

    the bit streams using secret keys. The advantage of

    steganography, over cryptography is that the attackers are

    not attracted towards communicating messages between

    sender and receiver while the encrypted messages attract

    the attackers. Steganalysis is a method of detecting the message hidden in a cover media and to extract it.

    Changes will be apparent in the statistical property of

    image if the secret message bits are inserted in image. The

    strength of the steganography is measured by

    steganalysis. RS steganalysis is one of the most reliable

    steganalysis which performs statistical analysis of the

    pixels to successfully detect the message hidden in the

    image. However, steganography method to detect the

    presence of secret message by RS attack/analysis is

    difficult in case of color images. Retention of visual

    quality of the image is also imperative. It is worth to note that genetic algorithm optimizes security and also the

    quality of the image. It belongs to class of evolutionary

    algorithms, which imitates the process of natural

    evolution. The proposed work introduces a genetic

    algorithm based steganography method to protect against

    the RS attack in color images.

    II. LITERATURE SURVEY

    M.F.Tolba, M.A.Ghonemy and A.Taha [4] proposes an

    algorithm by which the information capacity can reach

    50% of the original cover image. It provides high quality of stego image over the existing LSB based method.

    R. O., El.Sofy and H.H.Zayed [5] provide high hiding

    capacity up to 48% of the cover image size. In this paper,

    they have tried to optimize these two main requirements

    by proposing a novel technique for hiding data in digital

    images by combining the use of adaptive hiding capacity

    function that hides secret data in the integer wavelet

    coefficients of the cover image with the optimum pixel

    adjustment (OPA) algorithm.

    Ali Al-Ataby and Fawzi Al-Naima [6] propose a modified

    high capacity image steganography technique that

    depends on wavelet transform with acceptable levels of imperceptibility and distortion in the cover image and

    high level of overall security.

    Souvik Bhattacharya, Avinash Prashad and Gautam

    Sanyal [7] incorporate the idea of secret key for

    authentication at both the ends in order to achieve high

    level of security. In this paper, a specific image based

    steganography technique for communicating information

    more securely between two locations is proposed.

    H. S. Manjunatha Reddy and K. B. Raja [8] propose a

    high capacity and security steganography using discrete

    wavelet transform (HCSSD). In this paper the two level

    wavelet transform is applied as cover and payload. The payload wavelet coefficients are encrypted and fused with

    wavelet coefficients of cover image to generate stego

    coefficients based on the embedding strength parameters

    alpha and beta.

    Elham Ghasemi, Jamshid and Brahram [9] propose a

    novel steganography scheme based on Integer Wavelet

    Transform and Genetic Algorithm. Simulation results

    show that the scheme outperforms adaptive

    steganography technique based on integer wavelet

    transform in terms of peak signal to noise ratio and

    capacity i.e. 35.17 dB and 50% respectively.

    T. C. Manjunatha and Usha Eswaran [10] use embedding process stores up to 4 message bits in each integer co-

    efficient for all the transform sub-bands. This paper

    presents a conceptual view of the digital steganography &

    exploits the use of a host data to hide a piece of

    information that is hidden directly in media content, in

    such a way that it is imperceptible to a human observer,

    but easily be detected by a computer.

    Amitav Nag, Sushanta Biswas, Debasree Sarkar and

    Partha Pratim Sarkar [11] present a technique for image

    steganography based on DWT. This paper presents a

    novel technique for Image steganography based on DWT, where DWT is used to transform original image (cover

    image) from spatial domain to frequency domain. First,

    two dimensional Discrete Wavelet Transform (2-D DWT)

    is performed on a gray level cover image of size M N

    and Huffman encoding is performed on the secret

    messages/image before embedding. Then each bit of

    Huffman code of secret message/image is embedded in

    the high frequency coefficients resulted from Discrete

    Wavelet Transform. Image quality is to be improved by

    preserving the wavelet coefficients in the low frequency

    sub-band also. Yedla Dinesh and Addanki Purna Ramesh [12] perform a

    multi-resolution analysis and space frequency

    localization. As compared to the current transform

    domain data hiding methods this scheme can provide an

    efficient capacity for data hiding without sacrificing the

    original image quality.

    Saddaf Rubab and M.Younus [13] derive a new algorithm

    to hide our text in any colored image of any size using

    wavelet transform. It improves the image quality and

    imperceptibility. Their method sustains the security

    attacks. This new method gives better invisibility and

    security of communication. This method provides double security by involving blowfish, which satisfies the need of

    imperceptibility.

    S.Priya and A.Amsaveni [14] give LSB based edge

    adaptive image steganography. Edge adaptive

    stenography on frequency domain improves security and

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    image quality compared to the edge adaptive stenography

    on spatial domain.

    Rastislav Hovancak, Peter Foris and Dusan Levicky [15]

    propose a new method of steganography technique based

    on DWT transform. The proposed method has ability to

    hide secret message in a digital image. The secret

    message is embedded into the image by changing wavelet co-efficient. The quality of the stego image of the

    proposed method is very close to that of the original one.

    Arezoo Yadollahpour and Hossein Miar Naimi [16]

    proposed a steganalysis technique using auto-correlation

    coefficients in colour and grayscale images. They suggest

    that insertion of secret message weakens the correlation

    between the neighbour pixels and thereby enabling one to

    detect the message.

    Fridrich et al. [17] proposed an effective steganalysis

    technique popularly known as RS steganalysis, which is

    reliable even in the detection of non-sequential LSB

    embedding in digital images. Andrew D Ker [18] has proposed a general framework for

    structural steganalysis of LSB replacement for detection

    and length estimation of the hidden message. He has

    suggested the use of previously known structural

    detectors and recommended a powerful detection

    algorithm for the aforementioned purpose.

    Tao Zhang and Xijian Ping [19] have proposed a

    steganalysis method for detection of LSB steganography

    in natural images based on different histograms. This

    method ensures reliable detection of steganography and

    estimate the inserted message rate. However, this method is not effective for low insertion rates.

    Fridrich and Goljan [20] have considered many

    steganalysis techniques and proposed a steganalysis

    technique based on images biplanes correlation. They state that LSB plane can be estimated from 7 planes out of

    8 planes in a pixel of the image. They feel that the

    performance of the suggested steganalysis method

    reduces as the LSB planes content is further randomized. Kong et al. [21] proposed a new Steganalysis approach

    based on both complexity estimate and statistical filter. It

    is based on the fact that the bits in the LSB plane are randomized when secret bits are hidden in LSB plane.

    Amirtharajan et al. [22] proposed a novel and adaptive

    method for hiding the secret data in the cover image with

    high security and increased embedding capacity. They

    feel that by using this method the receiver does not

    require the original image to extract the information.

    Umamaheswari et al. [23] proposed analysis of different

    steganographic algorithms for secure data hiding. They

    recommend compressing the secret message and

    encrypting it with receiver public key along with the stego

    key. They have analyzed different embedding algorithms

    and used cryptographic technique to increase the security. Taras Holotyak e.t. al [24] propose a new method for

    estimation of the number of embedding changes for non-

    adaptive k embedding in images. The same author [25]

    has also advocated a new approach to blind steganalysis,

    based on classifying higher-order statistical features

    derived from an estimation of the stego signal in the

    wavelet domain.

    Agaian and Perez [26] propose a new steganographic

    approach for palette-based images. This recently approach

    has the advantage of secure data embedding, within the

    index and the palette or both, using special scheme of

    sorting. The presented technique also incorporates the use color model and cover image measures, in order to select

    the best of the candidates for the insertion of the stego

    information.

    Chen and Lin [27] propose a new steganography

    technique which embeds the secret messages in frequency

    domain to show that the PSNR is still a satisfactory value

    even when the highest capacity case is applied. By

    looking at the results of simulation, the PSNR is still a

    relaxed value even when the highest capacity is applied.

    This is due to the different characteristics of DWT

    coefficients in different sub-bands. Since, the most

    essential portion (the low frequency part) is kept unchanged while the secret messages are embedded in the

    high frequency sub-bands (corresponding to the edges

    portion of the image), good PSNR is not a imaginary

    result. In addition, corresponding security is maintained

    as well since no message can be extracted without the

    Key matrix and decoding rules. Kathryn Hempstalk [28] investigates using the covers original information to avoid making marks on the stego-

    object, by hiding the basic files of electronic reside digital

    color images. This paper has introduced two image

    steganography techniques, FilterFirst and BattleSteg. These two techniques attempt to improve on the

    effectiveness of hiding by using edge detection filters to

    produce better steganography.

    Wang and Moulin [29] provided that the independent and

    identical distributed unit exponential distribution model is

    not a sufficiently accurate description of the statistics of

    the normalized periodogram of the full-frame 2-D image

    DFT coefficients.

    Park e.t. al [30] proposed a new image steganography

    method to verify whether the secret information had been

    removed, forged or altered by attackers. This proposed method covers secret data into spatial domain of digital

    image. In this paper, the integrity is verified from

    extracted secret information using the AC coefficients of

    the discrete cosine transform (DCT).

    Ramani, Prasad, and Varadarajan [31] proposed an image

    steganography system, in which the data hiding

    (embedding) is realized in bit planes of subband wavelets

    coefficients obtained by using the Integer Wavelet

    Transform (IWT) and Bit-Plane Complexity

    Segmentation Steganography (BPCS).

    Farhan and Abdul [32] have presented their work in

    message concealment techniques using image based steganography.

    Anindya e.t. al [33] presented further extensions of yet

    another steganographic scheme (YASS) which is a

    method based on embedding data in randomized locations

    so as to resist blind steganalysis. YASS is a technique of

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    JPEG steganographic that hides data in the discrete cosine

    transform (DCT) coefficients of randomly chosen image

    blocks.

    Adnan Gutub e.t. al. [34] depicts the random pixel

    manipulation methods and the stego-key ones in the

    propose work, which takes the least two significant bits of

    one of the channels to indicate existence of data in the other two channels. This work showed good results

    especially in the capacity of the data-bits to be hidden

    with relation to the RGB image pixels.

    Mohammed and Aman [35] used the Least Significant

    Bits (LSB) insertion method to hide data within encrypted

    image data.

    Aasma Ghani Memon e.t. al. [36] provides a new horizon

    for safe communication through XML steganography on

    Internet.

    Zaidan e.t. a.l. [37] has presented a model for protection

    of executable files by securing cover-file without

    limitation of hidden data size using computation between cryptography and steganography.

    Vinay Kumar and Muttoo [38] have discussed that graph

    theoretic approach to steganography in an image as cover

    object helps in retaining all bits that participate in the

    color palette of image.

    Wang e.t. al. [39] presented a new steganography based

    on genetic algorithm and LSB.

    In recent research works few algorithms have been

    proposed which consist of the marginal statistics that are

    preserved for achieving more security. Previous methods

    have less data hiding capacity and security against Reversible Statistical attack. As we increase the secret

    data length distortion increases in the final stego image as

    compared with cover image. All the previous works

    provide the basic idea to hide the data behind the image

    by using LSB substitution. There is no idea discussed

    about the increasing capacity of data so no effect on

    image and how to ban the RS attack. This is a critical

    issue in steganography model that how we increase the

    hiding capacity of an image or cover media without any

    distortion in the image quality and how to protect the

    method against the RS attack.

    III. PROPOSED SYSTEM ARCHITECTURE

    Design is a necessary phases of code development. The

    design is a methodology throughout that a system

    organization is established that is able to satisfy the

    sensible and non-functional system wants. Large Systems

    are divided into sub-systems that offer few connected set

    of services. The design process output is an architecture

    description. With regular analysis and improvement in

    style of algorithmic program, steganography is taken as a

    significant meaning to cover information and additionally

    the current work appears that it is efficient in hiding a

    large amount of information. GA is applied to realize associate optimum mapping function to cut back the error

    distinction between the input cover and the stego image

    and use the block mapping methodology to preserve

    native image properties and to cut back the complexness

    of algorithmic program. Optimal pixel adjustment process

    is applied to increase the hiding capability of this

    algorithmic program compared to other existing systems.

    In this high level system design the whole system design

    and development is to be administered. The system

    development with the correct sequence and therefore the

    synchronization with the all connecting modules measure

    aiming to be lined within the tactic of high level coming up with. The Genetic algorithm implementation is in

    addition one of the necessary steps for the high level

    system design. During this development method the GA

    has been used for the RS analysis.

    Design issues

    The proposed work presents a replacement

    steganographic technique in order to embed large amount

    of data in colored images whereas keeping the activity

    degradation to a minimum level using integer wavelet

    transform (IWT) and Genetic algorithm (GA). This

    technique permits concealment of a data in uncompressed

    color image. Our motivation to cover data in images is to provide security to images that contain crucial data.

    Proposed approach relies on LSB technique which is able

    to replace more than one bit from every pixel to cover

    secret message, but the security of the secret data can be

    improved by combining the least significant bit and

    wavelet transform. The aim of the design is to plan the

    solution of a given problem by the document needs. It is

    the beginning in moving from drawback to the solution

    domain. The design of the system is the most vital issue

    affecting the quality of the computer code package and

    contains a major impact on the coming phases such as testing and maintenance. The proposed work is basically

    experimental test-bed for analysis of RS-attack using LSB

    furthermore as genetic algorithm. So the design to be

    thought of during this work ought to be a framework

    application in MATLAB in integrated development

    setting considering all the parameters to protect the data

    using advance steganography.

    Assumptions and dependencies

    The primary assumption of the work is that the user is taking the input of original image and not from any

    processed or manipulated image.

    The user is predicted to use the standard cryptography algorithmic program in an exceedingly most secure

    system and network.

    The basic dependency of the work is to run the application, user needs the MATLAB setting and to

    use application and appraise its basic conception, user

    needs associate noise free image and knowledge in

    plain text format solely.

    Constraints

    The application relies on optimization using genetic rule

    within the current steganographic applications. Here

    limitation is that it's been found that whenever a picture input is subjected to such forms of process then there is

    loss of actual quality of image. So on resist RS analysis,

    the impact on the relation of pixels must be stipendiary

    which cannot be achieved by adjusting totally different bit

    planes. The implementation procedure may be

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    unworkable in non theoretical application. Therefore to

    overcome this limitation, GA is applied to calculate the

    higher adjusting mode that the image quality is not

    degraded.

    Proposed system architecture

    The planned work ensures the safety against the RS

    analysis. The application should be designed in such a way so as to overcome all the limitation considered within

    the previous analysis work. The present aim is to style the

    architecture of the planned work which depends

    completely on a sturdy process of safeguarding the input

    to the application. This strategy incorporates

    implementing least necessary bit for embedding the key

    message of the quilt image. Successive issue could be the

    loss of quality of the image and therefore the planning is

    done for safeguarding the standard of the image which is

    achieved by implementing Genetic algorithmic rule. It is a

    way of search employed in computing to search out exact

    or approximate solutions to optimization and search issues.

    This work presents a completely unique steganography

    technique which will ultimately increase the capability of

    data embedding and therefore the imperceptibility of the

    image after embedding. The proposed system architecture

    is highlighted as below:

    Fig. 4.1. Proposed system architecture

    Fig. 1. Proposed System Architecture

    The complete process can be expressed as follows:

    Fig. 2. Complete flow of proposed work

    The above mentioned figure represents the general system

    functionalities and the real operative steps of the

    developed design. In the processing, the program helps so

    as give a program to handle the developed model and to

    access the developed module. At the origination, the

    cover image is selected for embedding the message. Then

    the text data is to be selected so, as to accomplish the

    motive of steganography the stego key applied so at the

    opposite terminal the message can be retrieved by the same key. Once the Key is provided, the real application

    development for the RS analysis will be started with the

    strong GA improvement. In this technique, the message is

    to be embedded in cover image. Genetic algorithm is

    playing an important role for embedding more and more

    data in the image. In the architecture of the developed

    system the integer to integer wavelet transform is applied.

    Once the message is embedded into the image file, then

    embedding the image is again recovered so that it is now

    able to be transmitted over the channel. On the other

    hand, at the receiver terminal or at the extraction terminal with the accurate stego key, the message is retrieved

    accurately.

    IV. PROPOSED WORK

    Detailed design of the proposed steganography gives

    exhaustive image of the foremost parts described in the

    system design. Meantime this chapter describes the detail

    design of the system. In this section details and flow chart

    of each module has been described. The structure chart

    show control flow, the useful descriptions of that are

    conferred in the flow chart diagrams.

    Module specification

    Selection Mutation

    Input Data

    Input Cover Image

    Secret Text Message

    Integer Wavelet Transform (IWT)

    Blocking

    Genetic Algorithm (GA)

    Chromosome

    Initialization Cro

    ssov

    er

    Perform 2D-IWT

    Evaluate Regular and Singular Block Values

    (RM, R-M, SM, S-M)

    Block Flipping

    Perform RS Analysis

    Graphical User Interface

    Select the Input Cover Image

    Select the Secret Text to be Embedded

    Insert the Secret Key

    GA Design Based RS Parameters

    Message Embedding

    Inverse Wavelet Transform

    Fitness Function

    Embedded Message

    OPAP Algorithm

    2D Inverse IWT

    Message Extraction

    RS Analysis

    Mapping Function

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    The proposed model is prepared by using two

    fundamental modules: A) Embedding module: The main task of this module is to

    embed a secret text within the cover colored image using encryption key. The complete cover image is divided into 8x8 blocks before any further processing. The frequency domain representation of the respective created blocks is

    estimated by two dimensional Integer wavelet transform in order to accomplish 4 sub bands LL1, HL1, LH1, and HH1. This way 1 to 64 genes are generated containing the pixels numbers of each 8x8 blocks of the mapping function. The message bits in 4-LSBs coefficients of IWT in each pixel according to mapping function are embedded. Fitness evaluation based, Optimal Pixel Adjustment Process on the Image is applied. At last, inverse 2D IWT is computed in this module in order to generate the stego image.

    B) Extraction module: The main task of this module is the extraction of the actual secret text from the stego image to understand the effectiveness of process of message embedding. It takes the stego image as input with key for decrypting the hidden message from the stego image. Once the data has been transmitted over the communication channel and when the receiver receives the embedded image file, then it becomes necessary to again segment the image

    data and then take out the text data available at the space covered by the text data at the time of message embedding. The extraction can be summarized in a simple sentence as to take out the data that has been embedded.

    Genetic algorithm utilization process

    A Structure Chart (SC) in software engineering and

    organizational theory is a chart, which shows the

    deviation of the system configuration to the lowest

    manageable levels. Steganalysis is the art and science of

    detecting messages hidden using steganography; this is

    analogous to cryptanalysis applied to cryptography. The objective of steganalysis is to find suspected packages,

    identified that they have a payload encoded into them or

    not, and, if it is possible, then resolve that payload.

    Unlike cryptanalysis, where it is obvious that intercepted

    data contains a message (though that message is

    encrypted), generally steganalysis begins with a pile of

    suspect data files, but few information about which of the

    files, if anyone, contain a payload of information. The

    steganalyst is usually something of a forensic statistician,

    and should begin by minimizing this set of data files

    (which is often quite large; in a lot of cases, it may be the whole set of files on a computer) to the subset most likely

    to have been altered. In computing, the smallest amount

    of important bit (LSB) is that the bit position in a very

    binary number giving the units price, that is, decisive

    whether or not the quantity is even or odd. The LSB is

    usually remarked because the right-most bit, as a result of

    the convention in number system of writing lesser digit

    any to the correct. It is analogous to the smallest amount

    figure of a decimal number, that is that the digit within the

    ones (right-most) position. A genetic algorithm (GA) is a

    search technique used in computing to find exact or

    approximate solutions to optimization and search problems. Genetic algorithms are divided as world search

    heuristics. Genetic algorithms are a basic category of

    evolutionary algorithms (EA) that use techniques

    galvanized by organic process biology like inheritance,

    mutation, selection, and crossover.

    The following figure represents the structural chart

    representation for the proposed system development. Here

    it represents the overall processing and the step by step

    presentation of the proposed work.

    Fig. 3. GA utilization process

    Module design

    This section contains a detailed description of

    components of software, components of low-level and

    other sub-components of the proposed work. Module

    N

    o

    N

    o

    N

    o

    Y

    e

    s

    Secret

    Text

    Input Image

    Final

    Image

    Embedded

    GA

    Blocking

    Labels

    Chromoso

    me

    Selection

    Check Labels

    Reproducti

    on

    Mutation

    Crossover

    Crossover>2

    RS Condition

    Next Block

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    design helps for the implementation of the modules.

    Modules input requirements and outputs generated by the modules are described in this phase.

    Data embedding

    This is the process flow diagram for data embedding

    module to illustrate the initiation of security features

    along with implementation of IWT and Genetic Algorithm. The main purpose of this application is to

    show the flow of data embedding operation involved in

    the process. The frequency domain representation of the

    respective created blocks is estimated by two dimensional

    Integer wavelet transform in order to accomplish 4 sub

    bands LL1, HL1, LH1, and HH1. 1 to 64 genes are

    generated containing the pixels numbers of each 8x8

    blocks as the mapping function. The bits of message in 4-

    LSBs IWT coefficients each pixel according to mapping

    functions are embedded. According to fitness evaluation,

    Optimal Pixel Adjustment Process applied on the Image.

    At the end, inverse 2D IWT is computed in this module in order to generate the stego image. The input for this

    processing is basically a cover image and user text

    message for embedding purpose. Stego image is

    generated as a output after this process. This module

    interacts with all the components of the application

    responsible for selection of parameters for performing

    encryption.

    Fig. 4. Flow chart of the data embedding process

    Data extraction

    Figure 5 shows the process flow diagram for message

    extraction module to illustrate the decryption hidden text

    in the stego image. The main purpose of this application

    is to show the flow of message extraction operations

    involved in the process. This algorithm basically takes the

    input of the generated stego image from the embedding process and applies IWT along with decryption key to

    extract the secret text which has been hidden inside the

    stego image. The input for this processing is basically a

    stego image and decryption key for message extraction

    purpose. Original user text is generated as output after

    this process. This module mainly interacts with the

    previously implemented message embedding process for

    performing extraction.

    Fig. 5. Flow chart of the data extraction process

    LSB implementation

    Figure 6 shows the flow chart will show the section where

    LSB is implemented. The major operation takes place when the application starts getting the size of the cover

    image and then it creates a tree structure for ease in

    computation. After it gets filter value of the pixels, where

    the application start the filter and configure the starting

    and ending bits, that last set the match image. After

    performing this operation, LSB algorithm will be

    implemented in the cover image, where the pixels values

    of the stego-image are modified by the genetic algorithm

    to keep their statistic characters. Inputs are embedding

    original message with cover image. Output of the process

    is actual implementation of LSB algorithm. This module interacts with LSB module and genetic algorithm along

    with input files of cover image.

    Start

    Divide Image in 8x8

    Blocks

    Stego Image

    Extract Coefficient

    LSB Implementation

    Pixel Sequence

    Secret Key

    Actual Data

    Stop

    Start

    Take Input Cover

    Image

    Take Secret Text Data

    IWT Process

    Divide the

    Input Image in 8x8

    Blocks Gather all Coefficients

    Store Coff.

    4 Sub Bands

    Permutations Pixel

    Information

    (Each

    Block)

    Mapping Func.

    LSB Process

    Fitness Func.

    OPAP

    2D-I-IWT

    Stego Image

    Stop

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    Fig. 6. Flow chart of the LSB implementation

    Wavelet applications

    In mathematics, a wavelet series is an illustration of a

    square-integrable real number or complex number or

    complex valued function by a certain orthonormal series

    generated by a wavelet.

    Wavelet transform

    Wavelet domain techniques are becoming very popular because of the developments in the wavelet stream in the

    recent past years. Wavelet transform is employed to

    convert a spatial domain into frequency domain. The

    employment of wavelet in image stenographic model lies

    in the fact that the wavelet transform clearly separates the

    high frequency and low frequency information on a pixel

    by pixel basis. A continuous wavelet transform (CWT) is

    used to divide a continuous-time function into wavelets.

    Integer wavelet transform

    The proposed algorithm employs the wavelet transform

    coefficients to embed messages into four subbands of two dimensional wavelet transform. To avoid problems with

    floating point precision of the wavelet filters, we used

    Integer Wavelet Transform. The LL subband in the case

    of IWT appears to be a close copy with smaller scale of

    the original image while in the case of DWT the resulting

    LL subband is distorted (figure 7) [9]. Thus Integer

    Wavelet Transform (IWT) is preferred over Discrete

    Wavelet Transform (DWT).

    (a) Lena image and analyze in wavelet domain

    (b) One level 2D-DWT in subband LL (c) One level 2D-IWT in

    subband LL

    Fig. 7. Comparison of LL subband for 2D-DWT and 2D-IWT

    In 2D IWT transform, first apply one step of the one dimensional transform to all rows and then repeat to

    whole columns. This decomposition outputs into four

    classes or band coefficients. The Haar Wavelet Transform

    is the easiest of all wavelet transform. In this transform,

    the low frequency wavelet coefficient are generated by

    averaging the two pixel values and high frequency

    coefficients are generated by taking half of the difference

    of the same two pixels. The 4 bands produced are (i)

    Approximate band (LL), (ii) Vertical Band (LH), (iii)

    Horizontal band (HL), (iv) Diagonal detail band (HH).

    The approximation band consists of low frequency wavelet coefficients, which have important parts of the

    spatial domain image. The last band consists of high

    frequency coefficients, which contain the edge details of

    the spatial domain image. This IWT decomposition of the

    signal continues until the desired scale is achieved .Two-

    dimensional signals, like images, are converted using the

    2D IWT. The two-dimensional IWT operates in the same

    manner, with only minor variations from the one-

    dimensional transform. Given a two-dimensional array of

    samples, the rows of the array are processed first with

    only one level of decomposition. This essentially divides

    the array into two vertical halves; with the first half taking the average coefficients, while the second vertical half

    stores the detailed coefficients. This process is again

    performed with the columns, resulting in 4 sub bands

    within the array defined by filter output.

    Integer wavelet transform through lifting scheme

    The lifting scheme is for both designing wavelets and

    performing the discrete wavelet transforms. Basically it is

    worthwhile to merge these steps and design the wavelet

    filters while performing the wavelet transform. The

    method was introduced by Wim Sweldens [40]. The

    lifting scheme is an algorithm to calculate wavelet transforms in an effective way. It is also a generic

    technique to create so-called second-generation wavelets.

    They are much more flexible and can be used to define

    LL

    LH

    HL

    HH

    Start

    Cover Image

    Size

    Pixel

    Capacity

    Halfway

    Computation

    Filter value

    Match Image

    LSB

    Implementat

    ion

    Set Filter

    Start Bits &

    End Bits

    LSB=Match Stop

    DWT

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    wavelet basis on an interval or on an irregular grid, or

    even on a sphere. The wavelet lifting scheme is a method

    for decomposing wavelet transform into a set of stages.

    An advantage of lifting scheme is that they do not require

    temporary storage in the calculation steps and require less

    no of computation steps. The lifting procedure consists of

    three phases: (i) split phase, (ii) predict phase and (iii) update phase.

    Fig. 8. Lifting scheme forward wavelet transformation

    Splitting: Divide the signal x into even samples and odd

    samples: xeven : si x2i, xodd : di x2i+1

    Prediction: Analyze the odd samples using linear

    interpolation:

    di di (si+si+1)/2 Update: Update the even samples to maintain the mean

    value of the samples:

    si si + (di1+di)/4 The output from the s channel provides a low pass filtered

    version of the input where as the output from the d

    channel provides the high pass filtered version of the

    input. The inverse transform is obtained by reversing the

    order and the sign of the operations performed in the forward transform [40].

    Fig. 9. Lifting scheme inverse wavelet transformation

    Lifting scheme Haar transform

    In the lifting scheme version of the Haar transform,

    predicts that the odd element will be equal to the even

    element. The difference among the predicted value (the even element) and the actual value of the odd element

    replaces the odd element. For the forward transform

    iteration j and element i, the new odd element, j+1,i

    would be: oddj+1,i = oddj,i evenj,i. In the lifting scheme version of the Haar transform the update step replaces an

    even element with the average of the even /odd pair (e.g.

    the even element si and its odd successor si+1) is evenj+1,i =

    (evenj,i+oddj,i)/2 . The original value of the oddj,i element

    has been replaced by the difference between this element

    and its even predecessor. The original value is :oddj,i =

    evenj,i + oddj+1,i.Substituting this into the average, we get

    evenj+1,i = (evenj,i+evenj,i+oddj+1,i)/2 [45].

    Genetic algorithm based steganography method

    The proposed method embeds the message inside the cover image with the minimal distortion. Use a mapping

    function to LSBs of the cover image according to the

    content of the message. Genetic Algorithm is used to find

    a mapping function for all the image blocks. Block based

    strategy preserve local image property and reduces the

    algorithm complexity as compared to single pixel

    substitution. The genetic algorithm optimizes the image

    quality and security of the data.

    Chromosome design

    In our GA method, a chromosome is encoded as an array

    of 64 genes containing permutations 1 to 64 that point to

    pixel numbers in each block. Each chromosome produces a mapping function (figure 10).

    59 47 1 33 .. 41 16 9 60

    Fig. 10. Chromosome with 64 genes

    Each pixel in a block is considered as a chromosome.

    Some chromosomes are considered for forming an initial

    population of the first generation in genetic algorithm.

    Several generations of chromosomes are created to select

    the best chromosomes by applying the fitness function to

    replace the original chromosomes. Reproduction

    randomly duplicates some chromosomes by flipping the second or third lowest bit in the chromosomes. Several

    second generation chromosomes are generated. Crossover

    is applied by randomly selecting two chromosomes and

    combining them to generate new chromosomes. This is

    done to eliminate more duplication in the generations.

    Mutation changes the bit values in which the data bit is

    not hidden and exchanges any two genes to generate new

    chromosome. Once the process of selection, reproduction

    and mutation is complete, the next block is evaluated.

    GA operations

    Mating and mutation functions are applied on each

    chromosome. The mutation process causes the inversion of some bits and produces some new chromosomes, then,

    we select elitism which means the best chromosome will

    survive and be passed to the next generation.

    Fitness function

    Selecting the fitness function is one of the most important

    steps in designing a Genetic Algorithm based method.

    Whereas Genetic Algorithm aims to improve the image

    quality, Peak Signal to Noise Ratio (PSNR) can be an

    appropriate evaluation test.

    The fitness function enables to optimize the value through

    several iterations. Fitness is calculated by the probability of regular and singular groups when positive flipping and

    negative flipping is applied. Ultimately, the stego-image

    undergoes RS analysis and the values between original

    and stego-image are compared.

    Block flipping

    Odd Values

    Even Values

    Split Predict Update

    +

    -

    Odd

    Value

    s

    Even

    Values

    Merge Update Predict

    +

    -

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    RS steganalysis classifies block flipping into three types.

    They are positive flipping F1, negative flipping F-1, and

    zero flipping F0. RS steganalysis analyses three primary

    colors namely red, green and blue individually for color

    images. Initially, the image is divided into several blocks.

    Subsequently, flipping functions such as positive flipping

    and negative flipping are applied on each block of pixels. Later, the variations between original and flipped blocks

    are calculated. Based on the variation results, the blocks

    are categorized into regular and singular groups. Let RM

    denote relative number of regular group and SM denote

    relative numbers of singular groups. According to the

    statistical hypothesis of the RS steganalysis method in a

    typical image, the expected value of RM is equal to that of

    RM, and the same is true for SM and SM:

    RM RM and SM SM With application of positive flipping, RM denotes regular

    group and SM is singular group. Similarly, R-M and S-M are

    regular and singular group when negative flipping is

    applied. The difference between regular groups, RM and R-M and the difference between singular groups, SM and S-

    M increases with the increase in length of the secret

    message.

    V. IMPLEMENTATION AND EXPERIMENTAL

    RESULTS DISCUSSION

    The important phase of a research work is its

    implementation which shows the actual direction of

    implementing the scenario, methods and step by step

    development. The implementation part of any

    development is the implementation part as the same yields

    the ultimate solution, which solves the matter in hand. The phase of implementation involves the actual

    materialization of the ideas, which are show in the

    document analysis and are developed in the phase of

    design. Implementation should be the best mapping of the

    design document in a suitable programming language in

    order to achieve the necessary final product. Usually the

    product is ruined due to incorrect programming language

    adopted for implementation or unsuitable method of

    programming. It is better for the phase of coding to be

    directly connected to the design phase in the sense if the

    design is in terms of object oriented terms then implementation should be preferably carried out in a

    object oriented way. The implementation of the system

    developed has been performed on the MATLAB software

    platform.

    Implementation Implementation of proposed steganography application is

    always preceded by important decisions regarding

    selection of the platform, the language used, etc. These

    types of decisions are often influenced by several factors

    such as real environment in which the system works, the

    speed required, the security issues, and implementation related details. These major implementation decisions are

    there that have been made before the implementation of

    the work.

    Proposed work implementation requirements

    The implementation of the proposed work requires an

    input cover image with a data file for performing the

    message embedding process. However the software

    requirements for performing the implementation are:

    MATLAB 7.10.0.499 (R2010a)

    Microsoft windows XP

    .NET framework 3.5 Guidelines to perform coding

    The following guidelines have been used during the

    implementation of the proposed work:

    Initialize local variables and all pointers initialized to the defined values or NULL.

    Use tracing statements at critical points in the code.

    For all the data types, type definitions are used.

    All the message formats are stored in header file.

    All the functions should not exceed more than 100 lines.

    Function pointers are not used.

    All the codes should be properly indented.

    Use conditional compilation statements, wherever required.

    Implementation of algorithm

    Data embedding algorithm

    The proposed method for data hiding comprises of the

    following:

    Take the input standard cover image.

    Take the secret text message.

    Apply the secret key (in digits only).

    Perform the Integer Wavelet Transform of the input cover image using lifting scheme.

    Add primal ELS to the lifting scheme.

    Perform integer lifting wavelet transform on image.

    Divide the input cover image in 8x8 blocks.

    Select any of the wavelet coefficients (redundant coefficients) from the obtained high frequency

    coefficients.

    Generate 64 genes containing the pixels numbers of each 8x8 blocks as mapping function.

    Initialize empty matrix to store the wavelet values.

    Obtain 8x8 blocks for R G B.

    Concatenate all coefficients together.

    Store the coefficient in new image.

    Embed in K-LSBs IWT coefficients in each pixel according to mapping function.

    Select any one of the pixels from RGB.

    Now the selected coefficients are processed to make it fit for modification or insertion.

    Fitness evaluation is performed to select the best mapping function.

    The secret message plus the message length is embedded into the processed coefficients.

    This modified coefficient is now merged with the unmodified coefficients.

    Calculate embedded capacity.

    Apply Optimal Pixel Adjustment Process on the image.

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    Convert image to binary.

    Finally, the inverse 2D IWT on each 8x8 block is applied to obtain the Stego image.

    Stego image to be obtained. Data extraction algorithm

    The proposed method for data extraction comprises of the

    following:

    Take the desired stego image.

    Apply the same secret key as given in embedding process.

    Divide the stego image into 8x8 blocks.

    Extract the transform domain coefficient by 2D IWT of each 8x8 blocks.

    Find the pixel sequences.

    Select the desired pixels for process.

    Extract K-LSBs in each pixel.

    Process the selected pixels coefficient to make it fit, for extraction.

    Now extract the message length and the secret message from these processed coefficients.

    Secret message to be obtained. RS-analysis algorithm

    The proposed method for RS analysis comprises of the

    following:

    Create function for non-positive flipping (Fn).

    Create function for non-negative flipping (Fp).

    Change LSB as per flipping.

    Initialize Relative number of regular block after positive flipping (R+) = 0.

    Initialize Relative number of Singular block after positive flipping (S+) = 0.

    Divide Stego Image into 8x8 blocks.

    For a modified block B, apply the non-positive flipping F and the non-negative flipping F+ on the block. The flipping mask M+ and M are generated randomly. The result is B'+ and B'.

    Estimate F (B'+), F (B') and F (B).

    Define four variables to divide the blocks by comparison of F (B'+), F (B') and F (B).

    Initially P+R = 0, P+S = 0, P-R = 0 and P-S = 0.

    Do the following steps for 100 times

    For nn = 1:100

    Apply the non-positive flipping F-.

    Fn = non_positive_flipping (B).

    Apply non-negative flipping F+.

    Fp = non_negative_flipping (B).

    Calculate f (B0+), f (B0-) and f (B).

    C = calculate_correlation (B).

    Correlation for non positive flipping.

    Cn = calculate_correlation (Fn).

    Correlation for non positive flipping.

    Cp = calculate_correlation (Fp).

    Estimate P+R, the count of the occurrence when the block is regular under the non-negative flipping.

    Estimate P+S, the count of the occurrence when the block is singular under the nonnegative flipping.

    Estimate PR, the count of the occurrence when the block is regular under the non-positive flipping.

    Estimate PS, the count of the occurrence when the block is singular under the non-positive flipping.

    If Cn>C, then increase PR (Regular).

    PR = PR +1.

    Else, increase PS (Singular).

    PS = PS +1.

    End

    If Cp>C, then increase P+R (Regular).

    P+R = P+R +1.

    Else, increase P+S (Singular).

    P+S = P+S +1.

    End

    Compare P+R to P+S and PR to PS, the blocks label are determined, str = [].

    If P+R / P+S >1.8, then str = 'R+'.

    disp ('R+'), Label of the block R+.

    Rp = Rp+1.

    End

    If P+S / P+R > 1.8, then str = 'S+'.

    disp ('S+'), Label of the block 'S+'.

    Sp = Sp+1.

    End

    If PR/PS > 1.8, then str = [str 'R-'].

    disp ('R-'), Label of the block 'R-'.

    Rm = Rm+1.

    End

    If PS / PR > 1.8, then str = [str 'S-'].

    disp('S-'), Label of the block 'S-'.

    Sm = Sm+1.

    End

    At last, the blocks are categorized into 4 groups (R+R), (R+S), (S +R), (S +S).

    Reject the block which doesnt fall in 4 groups.

    Now use genetic algorithm for minimizing R- block. The blocks, which are not included in the 4 categories, are

    not processed in following steps. Compared to the

    original image, the values of R+ R and S+ S blocks are increased in the stego-images. This phenomenon can be

    detected by the RS analysis. The main aim of the

    proposed algorithm is to decrease the amount of R blocks. Therefore genetic algorithm is deployed to adjust

    them to maintain the visual quality of image as given in

    follow section.

    Optimization technique or genetic algorithm

    The proposed method for genetic algorithm comprises of the following:

    Perform Chromosome Initialization Steps.

    From the first pixel, select every 4 pixels.

    B1 = B (:)

    crossover = 0.

    Initialize Alpha as 0.88.

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    For kk = 1: length (B1) - 2.

    do Chrom = B1 (kk:kk+2).

    Initialize maximum Fitness as 0.

    Flip second lowest bit randomly for number of time.

    For kk1 = 1:100

    Cp = non_negative_flipping (Chrom).

    Cn = non_positive_flipping (Chrom).

    C = calculate_correlation (Chrom).

    Cn = calculate_correlation (Cn).

    Initialization, e1 = 0 and e2 = 0.

    If Cn C, then e2 = 1.

    End

    PSNR = snr (Chrom-Cn).

    fitness = alpha*(e1+e2)+PSNR.

    If fitness>maxfitness, then maxfitness = fitness.

    Chrommax = Cp.

    crossover = crossover+1.

    End

    Replace chromosome with new one.

    B1 (kk:kk+2) = Chrommax.

    Calculate P-s and P-r.

    For qq = 1:100

    Apply the non-positive flipping F-.

    Fn = non_positive_flipping(B1).

    Calculate f (B0+), f (B0-) and f (B).

    C = calculate_correlation (B1).

    Correlation for non positive flipping.

    Cn = calculate_correlation (Fn).

    If Cn>C, then Regular.

    P-R = P-R +1.

    Else, Singular.

    P-S = P-S +1.

    End

    If P-S > P-R, then disp ('block is successfully adjusted').

    End

    If crossover>2, then break

    End

    P+R = 0, P+S = 0, P-R = 0, P-S = 0.

    Do the following steps for 100 times

    For nn = 1:5

    Apply the non-positive flipping F-.

    Fn = non_positive_flipping (B).

    The non-negative flipping F+.

    Fp = non_negative_flipping (B).

    Calculate f (B0+), f (B0-) and f (B).

    C = calculate_correlation (B).

    Correlation for non positive flipping.

    Cn = calculate_correlation (Fn).

    Correlation for non positive flipping.

    Cp = calculate_correlation (Fp).

    If Cn>C, then Regular.

    P-R = P-R +1.

    Else, Singular.

    P-S = P-S +1.

    End

    If Cp>C, then Regular.

    P+R = P+R +1.

    Else, Singular.

    P+S = P+S +1.

    End

    diff1 = abs (P+R P-R).

    diff2 = abs (P+S P-S).

    If difference is more than 5% then.

    If diff1>0.05*diff2.

    Successful then replace.

    I (ii:ii+7,jj:jj+7) = reshape (B1,8,8).

    Break the loop and go for next block. In the proposed technique, the blocks are labeled before

    the adjustment. Thus, the computational complexity is minimized. Genetic method use avoids the exhaustive

    searching and the algorithm is easy to be implemented.

    Proposed work implementation

    The proposed implementation of RS-analysis using

    genetic algorithm for the robust security in Steganography

    application is done on standard 32-bit windows OS with

    1.84 GHz processor and 2 GB RAM. The method is

    applied on 512x512 colored images Lena and Baboon as shown in Figure 11.

    a) Lena (JPG, 512x512) b) Baboon (JPG, 512x512)

    Fig. 11. Input cover images

    Experimental result analysis and discussion

    The proposed work is done on 2 set of data image as

    shown in previous section. Both cover images have

    utilization of 100% and their respective accomplished results of reversible statistical analysis are as follows:

    TABLE 1

    VARIOUS VALUES FOR LENA IMAGE

    For Lena Initial Value After

    Embedding

    After

    OPAP

    Rm-R-m 0.0097783 0.0076353 0.0057934

    Sm-S-m 0.0029662 0.011807 0.0093702 TABLE 2

    VARIOUS VALUES FOR BABOON IMAGE

    For Baboon

    Initial Value

    After Embedding

    After OPAP

    Rm-R-m 0.0059805 0.0076353 0.0056089

    Sm-S-m 0.0076634 0.011807 0.0023989

    The tables 1 and 2 have shown the values of |Rm-R-m| and

    |Sm-S-m| that represent the RS-steganalysis on the regular

    and singular block. It can be seen that the value of |Rm-R-

    m| and |Sm-S-m| increases from initial value before

    embedding and after embedding that exhibits a strong

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    correlation in potential of RS-analysis and the designed

    module. At initial stage, the values are less, after

    embedding the message, values increases and finally after

    applying optimal pixel adjustment process values are

    decreasing. Human visual system is not able to

    differentiate the colored images with PSNR more than 36

    dB. This proposed work embedded the messages in the k-LSBs, for k=4 and have received PSNR more than 40

    (Table 3) which is considered to be a good achievement. TABLE 3

    COMPARISON OF HIDING CAPACITY AND PSNR FOR 4-LSBS

    Cover

    Image

    Hiding Capacity

    (bits)

    Data Size

    (KB)

    PSNR

    (dB)

    Lena 2137696 (4-LSBs) 260 46.83

    Baboon 2137696 (4-LSBs) 260 49.65

    Figure 12 shows the images after embedding with 4-

    LSBs. As we compare these embedded images with the

    input cover images (figure 11), we realize that there are

    no significant changes in images. The embedded images

    look like the same as cover images. So the attackers

    cannot realize in between the communication of two parties that secret message is embedded in these images.

    (a) Lena image after embedding with 4-LSBs (b) Baboon image after embedding 4-LSBs

    Fig. 12. Images after embedding the secret data

    VI. CONCLUSIONS

    Steganography is a method that provides secret

    communication between two parties. It is the science of

    hiding a data, message or information in such a secure

    way that only the sender and recipient are aware about the

    presence of the message. The main advantages of this

    type of secure communication or we can say steganography is that it does not make any attention about

    the message to attackers or we can say does not attract the

    attackers. Strongest steganalysis method which is known

    as RS analysis detects the secret hidden message by using

    the statistical analysis of pixel values.

    The main aim of this work is to develop a steganography

    model which is highly RS-resistant using Genetic

    algorithm and Integer Wavelet Transform. This proposed

    work introduces a novel steganography technique to

    increase the capacity and the imperceptibility of the image

    after embedding. This model enables to achieve full

    utilization of input cover image along with maximum security and maintains image quality. GA employed to

    obtain an optimal mapping function to lessen the error

    difference between the cover and the stego image and the

    use the block mapping method to preserve the local image

    properties. In this proposed method, the pixel values of

    the stego image are modified by the genetic algorithm to

    retain their statistical characteristics. So, it is very difficult

    for the attacker to detect the existence of the secret

    message by using the RS analysis technique. We have

    applied the OPAP to increase the hiding capacity of the

    algorithm in comparison to other established systems.

    However, the computational complexity of the new algorithm is high. Further, implementation of this

    technique improves the visual quality of the stego image

    which is almost same as the input cover image. But, as we

    increase the length of the secret message, the chance of

    detection of secret hidden message by RS analysis also

    increases. The simulation results show that capacity and

    imperceptibility of image has increased simultaneity.

    Also, we can select the best block size to reduce the

    computation cost and in order to increase the PSNR using

    optimization algorithms such as GA. However, future

    works focus upon the improvement in embedding

    capacity and further improvement in the efficiency of this method.

    Future scope

    This proposed work is restricted to specific functionality

    only. The proposed work in this dissertation has been

    experimented on a single computer system and not on any

    network. Standard input cover image is only used in this

    steganography module. Proposed method is not applicable

    on audio, video and other biometrics etc. Large message

    steganography cannot be performed as the embedding

    capacity is confine to the data feed.

    Future work can be performed on the following:

    Improvement in data embedding capacity and more security against all types of attacks.

    Security design experimented over multiple computers / network.

    The data hiding technique can be applied to video, speech and other biometrics.

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