An Overview & Analysis of Image

Steganography Techniques

Aashish Shrivastav1, Amit Tak1, Anirudh Chouhan1, Naman Jain1

1Department of Computer

Engineering & Information

Technology,

Government Engineering

College, Ajmer, Rajasthan,

India [email protected]

Abstract: Steganography is an importunate area of research in recent years. It is defined as the art and technique of imperceptible communication. It is a technique of embedding information into palisade image via, text, or multimedia content for armament communication, substantiation and for many other animi. It deals with the ways of battering the connecting message and its actuality from the inadvertent customer. In image steganography, converted communication is accomplished through lodging a message into an image as palisade file and generates a stego-image having clandestine erudition so, in short; we can say that Steganography intrinsically bestows with the techniques of encrypting the communication data which is in perseverance in such a way that it sojourns arcane. There are sundry image steganography techniques are used each have its aye and nay. In this paper we will discusses the sundry image steganography techniques such as (LSB), Discrete wavelet transformation (DWT), Pixel value differencing, Discrete cosine transformation (DCT), Masking and filtering etc.

Keywords: Steganography, stego-image, Spatial turf methods, Revamp turf techniques, Distortion techniques, Cryptography.

1. Introduction

In today’s world, with the advancement of computer, communication and the rise of internet, the information is facilely transferred from one location to another. But in some cases, it is needed to keep the information must travel secretly. One of the grounds discussed in information aegis is the exchange of information through the palisade signatures, spread spectrum etc. that conceal the existence of information. But now day’s digital accessions are used so the steganography is mostly used on digital data. Thus, steganography stifles the existence of data so that no one can detect its presence. In steganography the process of veiling information content inside any multimedia content like image, audio, video is alluding as a “Embedding”. For increasing the confidentially of communicating data both the techniques may be combined. In this paper, we describe brief review and analysis of several image steganography techniques.

2. Image Steganography Techniques

Image Steganography is the process of adumbrate the sensitize cue into the palisade simulacre with no ignominy of the simulacre and providing better aegis so that unauthorized user cannot access the obscure information. Veiling the data by taking the palisade image is allude as image steganography. In image steganography pixel vehemence are used to stifle the data. In digital steganography, images are extensively used palisade source because there is cardinal of bits coeval in digital adumbration of an image.

Figure 1 shows the sundry image steganography techniques. Image steganography techniques are predominantly divided into following: –

Figure 1: Sundry Image Steganography Techniques.

Image steganography terminologies are as follows: –

Palisade-Simulacre: Autochthonous simulacre which is used as carrier for obscure information.

Message: Actual information which is used to stifle into simulacres. Message could be cinch text or some other simulacre.

Stego-Image: after embedding message into palisade simulacre is known as stego-image.

Stego-Key: A key is used for embedding or extracting the messages from palisade-simulacre and stego-images.

Figure 2: Image Steganography

Generally, image steganography is categorized in following aspects 23 and Table-1 shows the best steganographic measures: –

High Capacity: Maximum size of information can be ingrained into simulacre.

Perceptual Transparency: After veiling process into palisade simulacre, perceptual quality will be degraded into stego-image as compared to palisade simulacre.

Heftiness: After embedding, data should stay intact if stego-image goes into some redamation such as cropping, scaling, filtering and addition of noise.

Temper Resistance: It should be difficult to alter the message once it has been ingrained into stego-image.

Computation Covolatility: How much expensive it is computationally for embedding and extracting a obscure message?

Table 1. Image Steganography Algorithm Measures

Measures Advantage Detriment

High Capacity High Low

Perceptual Transparency High Low

Heftiness High Low

Temper Resistance High Low

Computation Covolatility Low High

2.1 Spatial Turf Methods

In spatial turf steganography accession, for veiling the data some bits are directly changed in the simulacre pixel values. There are many versions of spatial steganography, all directly change some bits in the simulacre pixel values in veiling data. Least Significant Bit (LSB) – stationed steganography is one of the simplest techniques that stifles a closet message in the LSBs of pixel values without many perceptible distortions. Novelty in the value of the LSB are imperceptible for human eyes. Most used accession in this category is least significant bit Spatial turf techniques are classified into following-

Least Significant Bit (LSB)

Pixel Value Differencing (PVD)

Edges Based Data Embedding Method (EBE)

Random Pixel Embedding Method (RPE)

Mapping Pixel to Obscure Data Method

Labelling or Connectivity Method

Pixel Intensity Based Method

Texture Based Method

Histogram Shifting Methods

General advantages of spatial turf LSB technique are:

There is less chance for degradation of the autochthonous simulacre.

More information can be stored in an simulacre.

Detriments of LSB technique are:

Less hefty, the obscure data can be lost with simulacre manipulation.

Obscure data can be facilely destroyed by simple attacks.

2.1.1 Least Significant Bit (LSB)

LSB interposeion is a common and simple accession for embedding information in a palisade file. Since LSB is replaced there is no effect on palisade simulacre and hence unintended user will not get the idea that some message is obscure behind the simulacre 3. However, a little change in level of intensity of autochthonous and modified pixel, but it cannot be detected visually.

Digital simulacres used as palisade file are mainly of two types- 24-bit simulacres and 8-bit simulacres. In 24-bit simulacres we can embed three bits of information in each pixel. In 8-bit simulacres, one bit of information can be obscure into simulacres. After employing the LSB algorithm the simulacre obtained having closet message is known as stego-image. LSB technique as the name implies replaces the least significant bit of the pixel with the information to be obscure.

The following example explain how the letter A can be obscure into the three pixels i.e. eight bytes of a 24-bit simulacre.

Pixels: (00100111 11101011 11001010)

(00100111 11011000 10101001)

00110111 11011001)

A: 010100111

Result: (00100110 11101011 11001010)

(00100111 11011000 10101000)

00110111 11011001)

The main advantage of LSB accession is easy to implement and high message payload and there is less chance of degradation of quality of autochthonous simulacre. The detriments are that the information can be facilely extracted or destroyed by simple attacks and it is less hefty, vulnerable to simulacre manipulation.

2.1.2 Pixel Value Differencing (PVD)

In PVD, gray scale simulacre is used as a palisade simulacre with a protracted bit-stream as the closet data 4. It was actually proposed to stifle closet information into 256 gray valued simulacres. The accession is stationed on the gospel that human eyes can facilely scrutinize minuscule novelty in the smooth wilderness but they cannot scrutinize relatively larger novelty at the edge wilderness in the simulacres. PVD uses the aberration between the pixel and its acquaintance to determine the cardinal of ingrained bits. The bigger the aberration whopper is, the more closet bits can be ingrained into the palisade simulacre.

This accession is proposed to aggrandize the embedding extent without incongruous visual novelty in stego-image. But the detriment of the accession is consistently the pixel value in the stego-image may surmount the territory 0-255 which leads to incongruous visualization of the stego simulacre. It has also weak aegis fruition due to non-adaptive quantization, embedding some information in smooth wilderness etc

It scans the simulacre starting from the upper left edge in a crinkled manner. Then, it simply divides the palisade simulacre into cardinal of oblongs where each oblong consists of two successive non-overlapping pixels. The aberration of the two pixels in the oblong is used to categorize the sleekness properties of the palisade simulacre. A minuscule aberration value indicates that the pixels are at smooth area although pixels around edge area have large aberration values. The data is ingrained mostly in the edge wilderness because the novelty of the pixel values is more facilely observed by human eyes. Therefore, in PVD accession a territory table has been designed with z contiguous territory Rl (Where l = 1,2,3……z) where the territory is between 0 to 255.The lower and upper bound are betoken as Il & Ul respectively, then Rl €Il ,Ul. The width Wl and Rl is calculated by WL= UlIl+1 which elect how many bits can be obscure in pixel oblong. When extracting the ingrained data from stego-image autochthonous territory table is required.

2.1.3 Histogram Shifting Method

Histograms are used for graphical adumbration of simulacre. It epitomizes the pixel value and denseness at a particular pixel. It maneuver the pixel for each part of the simulacre. A histogram is useful to determine pixel distribution, density of colours along with tonal distribution. A histogram bestows the apical along with undermost pixel values in graph. Histogram shifting is the method which is used to alter or to extract a certain faction of pixels from a simulacre 11. In histogram the apical value is known as maxima along with the undermost value is known as minima. When the pixel value is altered for embedding process it should not cross the minima and maxima range. There are n-number of algorithms which supports histogram functionality in order to wield the simulacre. The cardinal of the pixels constituting the crest in the histogram of a palisade simulacre is equal to the veiling capacity because a lone crest in a palisade simulacre is used 5.

Many histograms shifting techniques are aggrandized by dividing the palisade simulacre into oblongs to generate a respective crest for each oblong which bestow more veiling capacity into the multiple oblongs.

2.2 Transformation Turf Technique

Transformation turf accessions stifles message in the significant wilderness of the palisade simulacre which makes them heftier against sundry simulacre processing operations like compression, cropping and aggrandizement. There are n-number of revamping turf accessions exists. The basic accession used for veiling information is to revamp the palisade simulacre, tweak the coefficients along with then interpose the revamping.

This is a more convoluted way of veiling information in an simulacre. Sundry algorithms along with alteration are used on the simulacre to stifle information in it. Revamp turf method can be termed as a turf of embedding techniques for which a cardinal of algorithms has been suggested 17. The process of embedding data in the frequency turf of a signal is much tenacious than embedding principles that engage in the time turf. Most of the tenacious steganographic systems today engage within the revamp turf techniques have an advantage over spatial turf techniques as they stifle information in wilderness of the simulacre that are dependent on the simulacre format and they may outrun lossless and lossy format conversions.

Revamp turf techniques are predominantly classified into: –

Discrete Fourier Transformation Technique (DFT)

Discrete Cosine Transformation Technique (DCT)

Discrete Wavelet Transformation Technique (DWT)

Lossless or reversible Accession

Embedding in coefficient Bits

2.2.1 Discrete Fourier Transformation (DFT) Technique

In DFT all the interposeion of obscure message is done in the frequency turf. It is a more convoluted way of veiling message into frequency turf of the simulacre. The Discrete Fourier Revamp of spatial value f (x, y) for an simulacre of size M × N is defined in equation for frequency turf transformation 6.

(1)

Similarly, inverse discrete Fourier transform (IDFT) is used to convert frequency component of each pixel value to the spatial turf value and the equation for transformation from

frequency to spatial turf is

(2)

When DFT is applied it proselyte the palisade simulacre from spatial turf to frequency turf and each pixel in spatial turf is revamped into two parts: real and imaginary part. The obscure message bits are interposed in real part of frequency turf omitting first pixel. After embedding IDFT is performed frequency turf converted into spatial turf. During the eradication or decoding of the message simulacre from spatial turf is revamped to frequency turf. After employing DFT and eradication algorithm the autochthonous source simulacre is retrieved.

2.2.2 Discrete Cosine Transformation (DCT) Technique

The DCT revamps the simulacre from spatial to frequency turf and separates the simulacre into spectral sub-bands with respect to visual quality of the simulacre, i.e. low, middle and high frequency components as shown in fig. 2. Here FL and FH are used to denote the undermost frequency components and higher frequency components respectively. FM is used as embedding region to provide additional resistance to lossy compression techniques, while avoiding significant modification of the palisade simulacre 13.

Figure 2: DCT Regions

It is used in the JPEG compression algorithm to revamp successive 8 × 8-pixel oblongs of the simulacre into 64 DCT coefficients each in frequency turf. Each DCT coefficient F(u, v) of an

8 × 8 pixel oblong of simulacre pixels f(x, y) is calculated by

(3)

Where C(x)=1/ when x=0 and C(x)=1 otherwise. The following quantization operation is performed after calculating the coefficients:

(4)

Where Q(u,v) is a 64-element quantization table. The obscure message is ingrained into the redundant bits, i.e. the least significant bits of the quantized DCT coefficients. A modification of a lone DCT coefficient affects all 64 simulacre pixels. In DCT stationed techniques, the closet data is ingrained in the carrier simulacre for DCT coefficients lower than the threshold value 7. Pixels having DCT coefficient value below threshold are known as potential pixels. Hence to avoid the visual distortion in simulacre the potential pixels are used for data veiling.

2.2.3 Discrete Wavelet Transformation (DWT) Technique

The Discrete Wavelet Transformation Technique is the new idea in the applications of the wavelets. The standard technique of storing in the least significant bit of pixel still applies but the only aberration is the information is stored into the wavelet coefficients, instead of changing the bits of actual pixels in the simulacre. DWT have advantage over Fourier Transformation, it performs local analysis and multi-resolution analysis. Wavelet analysis can reveal signal aspects like discontinuities, breakdown points etc. more clearly than Fourier Transformation.

The DWT splits the signal into two parts- high and low frequency. The information about the edge component is in high frequency part and the low frequency part is further split again into high and low frequency parts. A one-dimensional DWT uses filter bank algorithm 12 and the information is convolved with high pass filter and low pass filter. Human eyes are less sensitive to high frequency so high frequency components are used for steganography.

In two dimensional applications, for each level of decompositions, we first perform the DWT in the vertical direction, followed by DWT in the horizontal direction 8. As we can see in the fig.3, the first level of decomposition results into four classes or sub-band: approximate band(LL1), vertical band(LH1), horizontal band(HL1), diagonal detail band(HH1). The approximation band consists of low frequency wavelet coefficients which contains the significant part of the spatial turf simulacre. The other bands consist of high frequency coefficients, which contain the edge details of the spatial turf simulacre. For each successive level of decomposition, the approximate band of the previous level is used as the input. In second level of decomposition, the DWT is applied on LL1 band which decomposes it into four sub-bands: LL2, LH2, HL2 and HH2.

Figure 3: Three phase decomposition using DWT

2.3 Distortion Technique

In distortion techniques the information is stored by signal distortion. These techniques require the knowledge of the autochthonous palisade simulacre during the decoding process. The encoder applies series of modifications to the palisade simulacre and the decoder functions to check for the sundry aberrations between the autochthonous palisade simulacre and distorted palisade simulacre to recover the closet message. Using this technique, a stego object is created by the sender by employing a sequence novelty to the palisade simulacre. This sequence of modification corresponds to a specific closet message required to transmit. The message is encoded at pseudo- randomly chosen pixels in the simulacre. If the stego-image differ from the palisade simulacre at the given message pixel, the message bit is a “1” otherwise “0”. The sender can modify the “1” value pixels in such a way that the statistical properties of the simulacre should not affected.

Distortion techniques need knowledge of the autochthonous palisade simulacre during the decoding process where the decoder functions to check for aberrations between the autochthonous palisade simulacre and the distorted palisade simulacre in order to restore the closet message. The encoder adds a sequence of novelty to the palisade simulacre. So, information is described as being stored by signal distortion 18.

Using this technique, a stego object is created by employing a sequence of modifications to the palisade simulacre. This sequence of modifications is use to match the closet message required to transmit 19. The message is encoded at pseudo-randomly chosen pixels. If the stego-image is different from the palisade simulacre at the given message pixel, the message bit is a ‘1’, otherwise, the message bit is a ‘0’. The encoder can modify the ‘1’value pixels in such a manner that the benefits of this technique.

In any steganographic techniques, the palisade simulacre should never be used more than once. If an attacker tampers with the stego-image by cropping, scaling or rotating, the receiver can facilely detect it. In some cases, if the message is encoded with error correcting information, the change can be reversed and the autochthonous message can be recovered 20.

The receiver must have access to the autochthonous palisade for retrieving the message; it limits the benefits of this technique. In every steganography techniques, the palisade simulacre should never be used more than once. If an attacker has access to the palisade simulacre the closet message can be facilely detected by attacker from the stego-image by cropping, scaling or rotating it. In some cases, if the message is encoded with error correcting information, the change can even be reversed and the autochthonous message can be recovered 9.

2.4 Masking and Filtering

This technique is usually applied on 24 bits or grayscale simulacres, uses a different accession to veiling a message. It stifles information by marking an simulacre, similar to paper watermarks. This technique actually extends an simulacre data by masking the closet data over the autochthonous data as opposed to veiling information inside of the data 10. These techniques embed the information in the more significant wilderness of the simulacre than just veiling it into noise level. Watermarking techniques can be applied on the simulacre without the fear of its destruction due to lossy compression as they are more integrated into the simulacre.

This accession is more hefty than LSB modification with respect to compression and different kinds of simulacre processing since the information is obscure into the visible parts of the simulacre. The main drawback of this technique is that it can only be used on gray scale simulacres and restricted to 24-bit simulacres.

These techniques stifle information by marking an simulacre, in the same way as to paper watermarks. These techniques stifle the information in the significant wilderness than just veiling it into the noise level. The obscure message is more integral to the palisade simulacre. Watermarking techniques can be applied without the fear of simulacre destruction due to lossy compression as they are more integrated into the simulacres.

Advantages of Masking and Filtering Techniques: –

This accession is much more hefty than LSB replacement with respect to compression since the information is obscure in the visible parts of the simulacre.

Detriments of Masking and Filtering Techniques: –

Techniques can be applied only to gray scale images and restricted to 24 bits.

3. Analysis of Steganography Techniques

Table 2. Comparison of Steganography Techniques 22

Turf Algorithm Invisibility Capacity Heftyness Aegis Convolutedity

Spatial

Spatial

LSB High 1-3 bpp Low Low Low

PIT Medium >1 bpp Low High Low

OPAP Medium 1 bpp Low High Medium

ShabnamSamina et al. 23 Very High 2 bpp 23 High High Medium

Ratnakirti Roy et al. 26 Very High NA* High High Medium

Transform

Transform

JSteg Medium <1 bpnc Medium High Medium

Outguess High 0.4 bpnc Medium High High

F5 Very High 0.8 bpnc Medium High High

Po-Yuch Chen

et al. 14 Medium <1 bpnc High High Medium

Tanmay

Bhattacharya et al. 21 High <1 bpnc (3 closet simulacres per palisade simulacre) Medium High Medium

SwapnaliZagade et al. 24 High <1 bpnc High High Medium

#- Till capacity <3 bpp. *- capacity varies on the degree of mapping; it cannot be calculated in bpp. bpp- bit per pixel, bpnc- bit per non-zero coefficient.

Table 3. Mechanisms of Algorithms with Limitations

Algorithm Mechanism Limitation

LSB Substitute the LSB. Pair of value in histogram

PIT One colour channel LSB selects embedding for the remaining two. Histogram deviation for embedding >3 bpp

OPAP Adjust the pixels before the embedding pixels for better visibility. Visual distortion (PSNR <

35) for embedding in the

LSB > 3

JSteg Substitute LSB of

JPEGDCT coefficient POV in DCT histogram

Outguess Preserves order – 1 stats, of

DCT histogram Blockings

F5 Uses Matrix Encoding, decrease coefficient absolute value Increased zero coefficients

Po-Yuch Chen et al. 14 Use Key Matrix and decoding rules Visual distortion for large Key Matrix (extra data in the stego-image)

Tanmay Bhattacharya et al. 21 Embedding into the DCT coefficients of high frequency band of Red,

Green and Blue planes. Constraint on the screen simulacre size.

Swapnali Zagade et al.

23 Embedding into the coefficients involving the skin pixels of high frequency sub-bands of Green and Blue layers Eradication quality differs with different types of

Wavelets

Shubham Samima et al.

23 Closet information is mapped in a matrix using one of the planes (RGB) and embedding of mapping information into the other two planes is done using matrix Encoding Technique 23 One of the planes out of the three planes is unused which hampers the capacity

Ratnakirti Roy et al. 26 The palisade-closet map representing the closet simulacre as the Least Common Subsequence of the palisade simulacre is created along with key and the auxiliary map containing the closet embedding information (if any) Overhead of handling map along with the palisade simulacre. Iterative accession of finding LCS for large closet simulacre

may hamper the fruition of the technique.

The above comparison of the different steganography (Table 2) depicts that the spatial turf-based techniques are prone to sundry statistical attacks though they have good carrying capacity. Revamp turf-based techniques are more secure than the spatial turf-based techniques since the former bestow additional layer of aegis by revamping the autochthonous contents of the closet simulacre into a different form. However, the simulacre realization techniques in the spatial turf prove that working with spatial turf is worth to have high carrying capacity, aegis as well as heftyness with acceptance computational overhead.

Table 4

S. No

. Turf Techniq ue

Target to Advantage Disadvanta ge

Capacity

Capacity

Perceptual

Perceptual

Robustness

Robustness

Tempe

r

Tempe

r

Computation

Computation

1 Spatial Adaptiv e LSB Yes Yes No No No Integrity of closet obscure information with High Capacity Stifle extra bits of signature with obscure message

2 Spatial Texture

,

brightn ess and Edge based Adaptiv e LSB Yes Yes No No No High

Obscure capacity with consideratio n of Good Visual

Quality Experiment

al Dataset is limited

3 Spatial Combined Pattern

Bits No No No No No Aegis of

Obscure

Data Obscure

Capacity is

Low

with Closet Message using

LSB 4 Spatial PVD

(on edges) with

Adaptiv e LSB (smooth

) Yes Yes No No Yes High

Obscure Capacity with consideratio n of Good Visual

Quality Computational

Convoluted

5 Spatial MPD

with LSB Yes Yes No No No Better than

general

PVD

accessions Experiment

al Dataset is limited and Threshold

(Stego) Key Required for Both ends

6 Spatial PDV

with

Adaptiv e LSB Yes Yes No No No Histogram of palisade and stego image is almost same Experiment

s are too minuscule

7 Spatial Hybrid (fuzzy + canny) edge detectio n with LSB Yes Yes No No No High PSNR with high obscure capacity Limited Dataset with ideal simulacres and Extensive edge-based simulacres may

failed

8 Spatial LSB

substitu tion with

Rando

m Pixel selectio n No No No No No Aegis of obscure message in Stegoimage Embedding

data without considering Visual

Quality in

Random

Pixel

Selection

9 Spatial Mappin

g pixel No Yes No No No Just

Mapping of Have to keep

to

obscure alphanumeric

letters pixels with letter no need of simulacre processing required Matching

Pattern for Extracting procedure plus; only useful for Letter stationed obscure data

10 Spatial LSB

Substitu tion on Dark region

of

simulacre No Yes No No No Useful for smooth region with solid boundary of objectbased dataset High computatio n required and not tested on high texture wilderness

11 Spatial LSB

filtering

with Median

Filterin g Yes No No No No High

Obscure capacity Computational

Convoluted

filtering + Stego – key requirement

12 Spatial Pixel indicato r with variable

LSB

substitu tion Yes No No No No Almost same Histogram of StegoImage against Palisade

Simulacre Obscure Capacity depend upon Palisade Image Pixel vehemence

13 Spatial Complex and Simple Texture stationed on LSB substitu tion Yes Yes No No No High

Obscure

Capacity High

Obscure Capacity degrade the visual; quality

PSNR

14 Transfor m DWT

Coeffici ent

permute d and

embedding in No No No No No Integrity of Obscure data in StegoImage Computationally convoluted

Spatial Turf 15 Transfor m DCT

Coeffici ent

Stationed No Yes No Yes No High PSNR Noticeable artefact of

Obscure

Data

16 Transfor m Closet

Bits + Bit depth embedded into

Coded

Oblong No Yes No Yes No Useful for binary simulacre Not for colour simulacre support

Comparative analysis of DCT stationed, LSB stationed & DWT stationed steganography has been done on basis of parameters like PSNR, MSE, Heftyness & Capacity on different simulacres and the results are evaluated. If PSNR ratio is high then simulacres are best of quality.

Table 5.1: LSB Substitution Techniques 21

(a) Jet (b) Baboon

Palisade Simulacre PSNR (dB) MSE (dB)

Jet 52.7869 .58505

Baboon 53.7558 .52329

Table 5.2: DCT Transform Techniques 22

(a) Jet (b) Baboon

Palisade Simulacre PSNR (dB) MSE (dB)

Jet 55.6473 .420896

Baboon 58.3766 .30740

Table 5.3: DWT Transform Techniques

(a) Jet (b) Baboon

Palisade Simulacre PSNR (dB) MSE (dB)

Jet 44.76 1.4741

Baboon 44.96 1.4405

Table 5.4: Parameters Analysis of Steganography Methods 24

Features LSB DCT DWT

Invisibility Low High High

Payload Capacity High Medium Low

Heftyness against

Simulacre

Manipulation

Low

Medium

High

PSNR Medium High Low

MSE Medium Low High

3. Conclusion

Simulacre steganography is the way of closet communication through the digital simulacres. In this paper we have discussed about steganography and several simulacre steganography techniques. Every technique has its own importance and use for veiling the data in simulacre. After the study of the all techniques it is easy to decide a particular one for closet communication.

It is an overview of different steganographic techniques its major types and classification of steganography which show that visual quality of the simulacre is degraded when obscure data increased up to certain limit using LSB stationed accessions. And many of them embedding techniques can be broken or shows indication of alteration of simulacre by careful analysis of the statistical properties of noise or perceptually analysis.

In shot we can say that it is the art, science and technique of writing obscure messages in such a way that no one, apart from the sender and intended recipient, suspects the existence of the message. And that’s why we can say that it’s a book on magic.it is emerging in its crest because it does not attract anyone by itself 24.

In this research paper, analysis of LSB, DCT & DWT accessions has been successfully implemented and the reliable results are delivered. The MSE and PSNR of the accessions are also compared and also this paper coevaled a background discussion and the implementation on the major algorithms of steganography deployed in digital imaging.

The PSNR shows the quality of simulacre after veiling the data. From the results, it is clear that PSNR of DCT is high as compared to the other two techniques. This implies that CDT bestow best quality of simulacre. An embedding algorithm is said to be HEFTY if the ingrained message can be extracted after the simulacre has been wieldd without being destroyed. DWT is a highly hefty accession in which the simulacre is not destroyed on extracting the message obscure in it and bestow maximum aegis.

References Références Referencias

J.R. Krenn, “Steganography and Steganalysis”, January 2004. Deshpande Neeta, Kamalapur Snehal, Daisy Jacobs, “Implementation of LSB Steganography and its Evaluation for Various Bits”, 2004.

K.B. Raja, C.R. Chowdary, Venugopal K. R, L.M. Patnaik, “A Secure Image Steganography using LSB, DCT and Compression Techniques on Raw Images”, IEEE-0-7803-9588-3/05/$20.00 ©2005.

Vijay Kumar Sharma, Vishalshrivastava, “A Steganography Algorithm for Hiding Images by improved LSB substitution by minize detection.” Journal of Theoretical and Applied Information Technology, Vol. 36 No.1, ISSN: 1992-8645, 15th February 2012.

Po-Yueh Chen and Hung-Ju Lin, “A DWT Based Approach for Image Steganography”, International Journal of Applied Science and Engineering 4, 3: 275-290, 2006.

Chen Ming, Zhang Ru, NiuXinxin, Yang Yixian, “Analysis of Current Steganography Tools:

Classifications & Features”, International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP’06), IEEE- 0- 7695-2745-0/06 $20.00 © 2006.

Aneesh Jain, Indranil Sen. Gupta, “A JPEG Compression Resistant Steganography Scheme for Raster Graphics Images”, IEEE-1-4244-1272- 2/07/$25.00©2007.

Beenish Mehboob and Rashid Aziz Faruqui, “A Steganography Implementation”, IEEE -4244-2427- 6/08/$20.00 ©2008.

Hassan Mathkour, Batool Al-Sadoon, Ameur Touir, “A New Image Steganography Technique”, IEEE978-1-4244-2108-4/08/$25.00 © 2008.

Nageswara Rao Thota, Srinivasa Kumar Devireddy, “Image Compression Using Discrete Cosine Transform”, Georgian Electronic Scientific Journal: Computer Science and Telecommunications, No.3 (17), 2008.

MamtaJuneja, Parvinder Singh Sandhu, “Designing of Robust Image Steganography Technique Based on LSB Insertion and Encryption”, International Conference on Advances in Recent Technologies in Communication and Computing, 2009.

Dr. EktaWalia, Payal Jain, Navdeep, “An Analysis of LSB & DCT based Steganography”, Global Journal of Computer science ; technology, Vol. 10 Issue 1 (Ver 1.0), April 2010.

K.B. Shiva Kumar, K.B. Raja, R.K. Chhotaray, Sabyasachi Pattnaik, “Coherent Steganography using Segmentation and DCT”, IEEE-978-1-4244- 5967-4/10/$26.00 ©2010.

K Suresh Babu, K B Raja, Kiran Kumar K, Manjula Devi T H, Venugopal K R, L M Patnaik,

“Authentication of Secret Information in Image Steganography” .

Arvind Kumar, Km. Pooja, “Steganography- A Data Hiding Technique”, International Journal of Computer Applications (0975 – 8887), Volume 9, No.7, November 2010.

Atalla I. Hashad, Ahmed S. Madani, “A Robust Steganography Technique Using Discrete Cosine Transform Insertion”.

Vijay Kumar, Dinesh Kumar, “Performance Evaluation of DWT Based Image Steganography”, IEEE- 978-1-4244-4791-6/10/$25.00_c 2010.

Ali Al-Ataby and Fawzi Al-Naima, “A Modified High Capacity Image Steganography Technique Based on Wavelet Transform” The International Arab Journal of Information Technology, Vol. 7, No. 4, October 2010.

T. Narasimmalou, Allen Joseph .R, “Optimized Discrete Wavelet Transform based Steganography”,

IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), 2012.

NedaRaftari and Amir Masoud Eftekhari Moghadam, “Digital Image Steganography Based on Assignment Algorithm and Combination of DCTIWT”, Fourth International Conference on Computational Intelligence, Communication Systems and Networks, 2012.

Ankita Sancheti, “Pixel Value Differencing Image Steganography Using Secret Key” International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-2, Issue-1 and December 2012

Mrs. Kavitha, Kavita Kadam, Ashwini Koshti, Priya Dunghav, “Steganography Using Least Significant Bit Algorithm”, International Journal of Engineering Research and Applications (IJERA), Vol. 2, Issue 3, pp. 338-341, 2012.

Manu Devi, Nidhi Sharma, ” Improved Detection of Least Significant Bit Steganography Algorithm in Color and Gray Scale Images”, IEEE Proceedings of RACES UIET Panjab University Chandigarh, 2014.

Nadeem Akhtar, Pragti Johri, Shabaaz Khan, “Enhancing the Security and Quality of LSB based Image Steganography”, IEEE International Conference on Computer Intelligence and Computer Networks(CICN), pp.385-389, 2013.

H.C. Wu, N.I Wu, C.S Tsai and M.S Hwang, “Image Steganographic scheme based on pixel value differencing and LSB replacement method”, IEEE Proceedings on Vision, Image and Signal processing, Vol. 152, No.5, pp.611-615,2005.

Zhicheng Ni, Yun-Qing Shi, Nirwan Ansari, and Wei Su, ” Reversible Data Hiding”, IEEE Transactions on Circuits and Systems for Video Technology, vol. 16(3), pp. 354–362, 2006.

Inderjeet Singh, Sunil Khullar, Dr.S.C.Laroiya, “DFT Based Image Enhancement And Steganography”,

International Journal of Computer Science and Communication Engineering, Vol.2, Issue 1, February, 2013.

Hardik Patel, Preeti Dave, “Steganography Technique Based on DCT Coefficients”, International Journal of Engineering and Applications(IJERA), Vol.2, Issue 1, pp.713-717, 2012.

Parul, Manju, Dr. Harish Rohil, “Optimized Image Steganography Using Discrete Wavelet Transform”, International Journal of Recent Development in Engineering and Technology (IJRDET), Vol. 2, Issue 2,2014.

Mehdi Hussain, Mureed Husain, “A Survey of Image Steganography Techniques”, International Journal of Advanced Science and Technology(IJAST), Vol.54, May , 2013.

Jammi Ashok, Y.Raju, S.Munishankaralah, K.Srinivas, “Steganography: An Overview”, International Journal of Engineering Science and Technology (IJEST), Vol.2 (10), 2010.

Sapna Saini, Brindha K., “Improved Data Embedding into Images Using Histogram Shifting”, International Journal of Emerging Research in Management ; Technology (IJERMT), Vol. 3, Issue 5, 2014.

Barnali Gupta Banik, Prof. Samir K. Bandyopadhyay, “A DWT Method for Image Steganography”, International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), Vol. 3, Issue 6, 2013.