LITERATURE SURVEY: Steganography Using …static.ijcsce.org/wp-content/uploads/2014/04/IJCSCE020714.pdfLITERATURE SURVEY: Steganography Using Redundant Bit Replacement By Neural Network

ISSN 2319-7080 International Journal of Computer Science and Communication Engineering Volume 3 issue 1(February 2014 issue)

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LITERATURE SURVEY: Steganography Using Redundant

Bit Replacement By Neural Network Jasmeet Kaur

1, Nitika Kapoor

2, Harish Kundra

3

1Research Scholar,

2,3Assistant Professor

1,2,3 Department of Computer Science and Engineering, Rayat Institute of Engineering and Information Technology

Railmajra, SBS Nagar, (Punjab) INDIA [email protected], [email protected], [email protected]

Abstract: - The paper describes the progress in the field of

Steganography. The idea behind this technique is to hide

the information in the media. The challenge is to make the

hidden information untraceable. The concept originate from

spatial domain to more enhanced technique. The proposed

technique in this paper is Neural Network. By using this

technique we make hide the information in better way than

simpler techniques in spatial domain.

I. INTRODUCTION

Classic methods of securing communication mainly base on

cryptography, which encrypts plain text to generate cipher text.

However, the transmission of cipher text may easily arouse

attackers‟ suspicion, and the cipher text may thus be

intercepted, attacked or decrypted violently. In order to make up

for the shortcomings of cryptographic techniques, steganography

has been developed as a new covert communication means in

recent years. It transfers message secretly by embedding it into a

cover medium with the use of information hiding techniques.

Cryptography and Steganography are two important branches of

information security. Cryptography provides encryption

techniques for a secure communication. Cryptography is the

science that studies the mathematical techniques for keeping

message secure and free from attacks [6]. Steganography is the

art and science of hiding communication. The word

steganography is derived from the Greek word stegos‖ meaning

cover‖ and grafia‖ meaning writing‖ defining it as ―covered

writing .Steganography involves hiding information so it appears

that no information is hidden at all. Steganalysis is the science of

detecting hidden information. The goal of steganalysis is to

break steganography. Steganalysis deals with three important

attacks. (a) Visual attacks: one can identify the stego image with

the naked eyes (b) Statistical attacks: they reveal the smallest

alterations in an image statistical behaviour. It is further

subdivided into (i) Passive attack: identifying the presence or

absence of a covert messages or embedding algorithm used (ii)

Active attacks: used to investigate embedded message length or

hidden message location or secret key used in hidden process (c)

Structural attacks: identifying the changes in the cover file.

Steganography is employed in various useful applications, e.g.,

copyright control of materials, enhancing robustness of image

search engines and smart IDs (identity cards) where individuals‟

details are embedded in their photographs. Other applications are

video-audio synchronization, companies‟ safe circulation of

secret data, TV broadcasting, TCP/IP packets (for instance a

unique ID can be embedded into an image to analyze the

network traffic of particular users), and also checksum

embedding [8]. One method of common Steganography

technique is to hide the secret message in the least significant

bits of pixels of the cover image. The image quality of stego

image achieved by applying the LSB technique is very closer to

the original one. But the drawback is it cannot survive image

processing manipulations. One method of LSB Steganography

involves manipulating the LSB plane from direct replacement of

the cover image with message bits to some type of logical or

arithmetic combination between two. Several examples of LSB

techniques are found. This technique achieves both high

capacity and low perceptibility. But it is not very sophisticated

and subject to extraction by unwanted persons. Masking and

filtering techniques usually restricted to 24 bits or grayscale

images. These methods are effectively similar to „paper

watermarks‟, creating markings in an image. This can be

achieved for example by modifying the luminance of parts of the

image. While masking does change the visible properties of an

image, it can be done in such a way that the human eye will not

notice the anomalies. Least Significant Bit maintains a good

visual quality of stego-image, it can hide little information.

Considering the drawback of LSB, some methods begin to

take account of the visual identity that human eyes are

insensitive to edged and textured areas when embedding secret

information, such as BPCS(biplane complexity

segmentation),PVD(pixel value differencing), MBNS (multiple

base notational system ), SOC, Side Match and WCL. The

capacity of embedded information is thereby greatly improved

while the quality of visual imperceptibility is maintained. As

human vision sensitivity is complex, it is hard to exactly

decide whether a pixel is in less sensitivity areas or not.

Thus, based on the contrast and texture sensitivity, we train

self-organizing map Neural Networks (NNs) trained to

distinguish pixels in less sensitive areas from pixels in more

sensitive areas. So, NNs trained is the secret key. Then,

we use NNs trained to classify pixels, and select pixels

in less sensitive areas to embed more secret data. On the

receiving side, the original image is not needed for

extracting the embedded data. Neural approach adds the

complexity for the hackers accessing and also presents high

potentiality in defense operations. Neural Steganography is a

powerful tool that enables people to communicate without

possible eavesdroppers even knowing there is a form of

communication. Basic elements of steganography in images are

shown in Figure 1. The carrier image in steganography is

mailto:[email protected]

mailto:[email protected]


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called the "cover image" and the image which has the

embedded data is called the "stego image". The embedding

process is usually controlled using a secret key shared

between the communicating parties.

Fig:1 Typical Element Of Steganography System.

II. RELATED WORK

Adel Almohammad , Gheorghita Ghinea and Robert M. Hierons

in 2009 proposed a High Capacity Steganographic Method

Based Upon the JPEG standard which uses 8x8 quantization

tables, but it does not specify any default or standard values for

quantization tables. However, the JPEG standard provides a pair

of quantization tables as examples tested empirically and found

to generate good results. Dividing this quantization table they

get a new quantization table. Using this new quantization table

generates reconstructed images almost identical to the source

image. Therefore, this table will be used with Jpeg-Jsteg method

in the experiment. Since the values of these tables could be an

arbitrary choice, some researchers modified these quantization

tables for their research purposes. A quantization table can

arbitrarily be generated. Consequently, They produced a 16x16

quantization table by simulating and stretching the scaled

quantization table [3]. Kousik Dasgupta1, J.K. Mandal2 and

Paramartha Dutta in April 2012 suggested a Hash Based LSB

Technique for Video Steganography that deals with hiding secret

data or information within a video. A spatial domain technique

where the secret information is embedded in the LSB of the

cover frames. Eight bits of the secret information is divided into

3,3,2 and embedded into the RGB pixel values of the cover

frames respectively. A hash function is used to select the

position of insertion in LSB bits. The proposed method is

analyzed in terms of both Peak Signal to Noise Ratio (PSNR)

compared to the original cover video as well as the Mean Square

Error (MSE) measured between the original and steganography

files averaged over all video frames. The proposed technique is

compared with existing LSB based steganography and the

results are found to be encouraging [1]. Some of few researchers

have already implemented NEURAL NETWORK in their

approach for the same here are some reviews about them. Usha

B A1, Dr. N K Srinath2, Dr. N K Cauvery in May 2013

proposed a Data Embedding Technique using Neural Network.

According to them the neural approach to embed information

satisfies a secure steganography. Neural approach adds the

complexity for the hackers accessing and also presents high

potentiality in defense operations. Neural Steganography is a

powerful tool that enables people to communicate without

possible eavesdroppers even knowing there is a form of

communication [5]. Imran Khan in August 2013 suggested an

Efficient Neural Based Algorithm of Steganography for Image.

To provide large capacity of the hidden secret data and to

maintain a good visual quality of stego-image a novel

steganography method based on neural network and random

selection of edged areas of pixels is proposed . Firstly a cover

image is divided into a non-overlapping two pixels block and

this pixel block generates a set of edged non-overlapping

regions. After this a neural network is applied which generates a

stego-image which has been immune against conventional attack

and performs good perceptibility compared to other

steganography approaches. From our experimental results it can

be shown that the proposed method hides information in edged

regions and maintains a better visual display of steganography

image than the traditional methods [8]. Bhavneet Kaur, Pooja &

Harish Kundra in December2013 proposed a Performance

Enhancement of a Transform Domain based Steganograhic

Technique using Segmentation. This Method involves

combining the DCT algorithm along with NEURAL

NETWORK in such a way that the IMAGE QUALITY which is

measured in terms of PSNR increases and the data remains safe

within the image [9].

III. TAXONOMYOF STEGANOGRAPHIC

TECHNIQUES

There are quite a lot of approaches in classifying steganographic

techniques. These approaches can be classified in accordance

with the type of covers used with secret communications [10].

Steganographic techniques that modify image files for hiding

information include the following:

• Spatial domain

•Transform domain

• Distortion techniques

File Embedding technique

1. SPATIAL DOMAIN TECHNIQUE

Spatial domain steganography techniques, also known as

substitution techniques, are a group of relatively simple

techniques that create a covert channel in the parts of the cover

image in which changes are likely to be a bit scant when

compared to the human visual system (HVS).One of the ways to

do so is to hide information in the least significant bit (LSB) of

the image data.

1.1LSB TECHNIQUE: This embedding method is basically

based on the fact that the least significant bits in an image can be

thought of as random noise, and consequently they become not

responsive to any changes on the image. The disadvantage of

this technique is that it uses each pixel in the image. As a result,


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if lossy compression is used, some of the hidden information

might be lost [10].

Limitation of LSB

LSB technique in the spatial domain is a practical way to

conceal information but, at the same time, it is vulnerable to

small changes resulting from image processing or lossy

compression [7]. Although LSB techniques can hide large

quantities of information i.e., high payload capacity, they often

compensate the statistical properties of the image and thus

indicate a low robustness against statistical attacks as well as

image manipulation.

2. TRANSFORM DOMAIN TECHNIQUE

Transform domain embedding can be defined as a domain of

embedding techniques for which a number of algorithms have

been suggested. The process of embedding data in the frequency

domain of a signal is much stronger than embedding principles

that operate in the time domain. It is worth saying that most of

the strong steganographic systems today operate within the

transform domain. Transform domain techniques have an

advantage over LSB techniques because they hide information in

areas of the image that are less exposed to compression,

cropping, and image processing. Some transform domain

techniques do not seem dependent on the image format and they

may outrun lossless and lossy format conversions.

2.1 JPEG COMPRESSION: If an image is to compress into

JPEG format, the RGB color space is first turned into a YUV

representation. Through this representation, the Y component

represents brightness (or luminance) and the U and V

components stand for color (or chrominance). It is known that

the human eye is more sensitive to changes in the brightness of a

pixel than to changes in its color. Down sampling the color

information is taken as an advantage of the JPEG to reduce the

size of the file where the color components (U and V) are

splitted in the horizontal and vertical directions and

consequently reducing the file size by a factor of 2. Then, the

image is transformed. For JPEG images, the discrete cosine

transform (DCT) is used; the pixels can be converted with such

mathematical processing by simply “spreading” the position of

the pixel values over the image or part of it [12]. With DCT

transformation, a signal is transformed from the representation

of an image into the frequency domain, this is done by sorting

the pixels into (8 × 8) pixel blocks and transforming these blocks

into 64-DCT coefficients which are affected by any modification

of a single DCT coefficient.

2.2 Wavelet transform technique: Wavelets transform (WT)

converts spatial domain information to the frequency domain

information. Wavelets are used in the image steganographic

model because the wavelet transform clearly partitions the high-

frequency and low-frequency information on a pixel by pixel

basis. The discrete wavelet transform (DWT) method is favored

over the discrete cosine transform (DCT) method, owing to the

resolution that the WT provides to the image at various levels

.Wavelets are mathematical functions that divide data into

frequency components, which makes them ideal for image

compression. In contrast with the JPEG format, they are far

better at approximating data with sharp discontinuities .

Researchers use vector quantization, called Linde-Buzo-Gray

(LBG), associated with block codes, known as BCH codes, and

one-stage discrete Haar wavelet transforms. They emphasize that

modifying data by using a wavelet transformation produces good

quality with few perceptual artifacts. A group of scientists at

Iowa State University are developing an advanced application

called artificial neural network technology for steganography

(ANNTS), with the aim of detecting all current steganography

methods, which include DCT, DWT, and DFT. They found that

the inverse discrete Fourier transform (IDFT) includes a

rounding error that makes DFT inappropriate for steganography

applications [12]. The promising techniques such as DCT, DWT

and the adaptive steganography are not tended to attacks,

especially when the hidden message is small. This can be

justified in relation to the way they change the coefficients in the

transform domain, thus, image distortion is kept to a minimum.

Generally speaking, such techniques tend to have a lower

payload when they are compared to the spatial domain

algorithms [8]. The experiments on the discrete cosine transform

(DCT) coefficients have introduced some promising results and

then they have diverted the researchers‟ attention towards JPEG

images. Working at some level like that of DCT turns

steganography much more powerful and less prone to statistical

attacks. Embedding in the DWT domain reveals a sort of

constructive results and outperforms DCT embedding, especially

in terms of compression survival

3. DISTORTION TECHNIQUES

Distortion techniques require knowledge of the original cover

image during the decoding process where the decoder functions

to check for differences between the original cover image and

the distorted cover image in order to restore the secret message.

The encoder, on the other hand, adds a sequence of changes to

the cover image. So, information is described as being stored by

signal distortion. Using this technique, a stego-object is created

by applying a sequence of modifications to the cover image.

This sequence of modifications is selected to match the secret

message required to transmit. The message is encoded at

pseudo-randomly chosen pixels. If the stego-image is different

from the cover image at the given message pixel, then the

message bit is a “1.”[10] Otherwise, the message bit is a “0.”

The encoder can modify the “1” value pixels in such manner that

the statistical properties of the image are not affected (which is

different from many LSB methods). However, the need for

sending the cover image limits the benefits of this technique.

4. FILE EMBEDDING TECHNIQUE

Different image file formats are known for having different

header file structures. In addition to the data values, such as

pixels, palette, and DCT coefficients, secret information can also

be hidden in either a header structure or at the end of the file

[12]. For example, the comment fields in the header of JPEG

images usually contain data hidden by the invisible Secrets and


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Steganozorus. Camouflage, JpegX, PGE10, and PGE20 add data

to the end of a JPEG image.

Limitation Of File Embedding Technique

File formatting techniques can store large amounts of

information, but they are easily detected and attacked.

LSB TRANSFORM

DOMAIN

FILE EMBEDDING DISTORTION

IMPERCEPTIBILITY High High High Low

ROBUSTNESS Low High Low Low

PAYLOAD

CAPACITY

High Low High Low

Table 1: Comparison Of Different Techniques.

IV. TECHNIQUE INVOLVED

NEURAL NETWORK

An Artificial Neural Network (ANN) is an information

processing paradigm that is inspired by the way biological

nervous systems, such as the brain, process information. The key

element of this paradigm is the novel structure of the

information processing system. It is composed of a large number

of highly interconnected processing elements (neurons) working

in unison to solve specific problems. ANNs, like people, learn

by example. An ANN is configured for a specific application,

such as pattern recognition or data classification, through a

learning process. Learning in biological systems involves

adjustments to the synaptic connections that exist between the

neurons. This is true of ANNs as well.

Why use neural networks?

Neural networks, with their remarkable ability to derive meaning

from complicated or imprecise data, can be used to extract

patterns and detect trends [8] that are too complex to be noticed

by either humans or other computer techniques. A trained neural

network can be thought of as an "expert" in the category of

information it has been given to analyze. This expert can then be

used to provide projections given new situations of interest and

answer "what if" questions.

Other advantages include:

1. Adaptive learning: An ability to learn how to do tasks based

on the data given for training or initial experience.

2. Self-Organization: An ANN can create its own organization

or representation of the information it receives during learning

time.

3. Real Time Operation: ANN computations may be carried out

in parallel, and special hardware devices are being designed and

manufactured which take advantage of this capability.

4. Fault Tolerance via Redundant Information Coding: Partial

destruction of a network leads to the corresponding degradation

of performance. However, some network capabilities may be

retained even with major network damage.

A simple neuron

An artificial neuron is a device with many inputs and one output.

The neuron has two modes of operation; the training mode and

the using mode. In the training mode, the neuron can be trained

to fire (or not), for particular input patterns. In the using mode,

when a taught input pattern is detected at the input, its associated

output becomes the current output. If the input pattern does not

belong in the taught list of input patterns, the firing rule is used

to determine whether to fire or not.

aa s

A simple neuron

V. CONCLUSION AND FUTURE WORK

This paper provides an overview of steganography & reviewed

the main steganographic techniques. Each of these techniques

tries to satisfy the three most important factors of steganographic

design (imperceptibility or indefectibility, capacity, and

robustness). We can deduce that while one technique may lack

in payload capacity, another may lack in robustness. For

example, file formatting techniques can store large amounts of

information, but they are easily detected and attacked. Likewise,

LSB techniques in a spatial domain have a high payload

capacity, but they often fail to prevent statistical attacks and are

thus easily detected. Besides, file and spatial domain approaches

are considered not to be robust against lossy compression and

filtering. Transform domain techniques are considered more

robust for lossy compression image formats, but this advantage

is achieved at the expense of payload capacity. However, it is

possible to defeat the transform domain techniques using Neural

Network. Neural network generates a stego-image which has

been immune against conventional attack and performs good

perceptibility compared to other steganographic approaches.


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