A Novel Approach for Hiding Text Using Image Steganography

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Vol. 8 No. 7 October 2010 International Journal of Computer Science and Information Security

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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 7, October 2010

A Novel Approach for Hiding Text Using Image
Steganography

Sukhpreet Kaur* Sumeet Kaur
Department of Computer Science and Engineering Department of Computer Engineering
Baba Farid College of Engineering and Technology Yadavindra College of Engineering Punjabi University
Bathinda-151001, Punjab, India Guru Kashi Campus
*
Corresponding Author’s Email: Talwandi Sabo, Punjab, India
preetsukhpreet@gmail.com

Abstract— With the increasing use of internet for problems to which they are applied. Cryptography protects the
communication, the major concern of these days is, the security secret data by making it difficult to understand by the intruder
of data being communicated over it. Steganography is the art and but still the intruder knows that the secret data exists, so he
science of invisible communication. It hides secret information in will try his best to decode the data. Steganography &
other information, thus hiding the existence of the communicated
encryption are both used to ensure data confidentiality
information. In this paper we have discussed a technique of
hiding text messages in the images using image steganography. however the main difference between them is that with
The technique uses matching of secret data with pixel values of encryption anybody can see that both parties are
cover image as base concept. The LSBs of matched pixels are communicating in secret. Steganography hides the existence
changed to mark presence of data inside that pixel. For making of a secret message and in the best case nobody can see that
selection of channels for marking presence of data, a pseudo both parties are communicating in secret. Watermarking is
random number generator is used, which adds another layer of used primarily for identification and entails embedding a
security to the technique and makes the extraction of secret data
unique piece of information within a medium without
very difficult for the intruders. The results show that technique
provides more security against visual and statistical attacks and noticeably altering the medium. Steganography uses a basic
attempts to provide more data hiding capacity by using more bits model to hide data inside the cover objects as shown in Fig. 1.
per pixel.

Keywords- Steganography; image steganography; attacks; PSNR;
security
Secret
Message
I. INTRODUCTION
Steganography can be defined as the technique used to
embed data or other secret information inside some other
Steganography Stego
object commonly referred to as cover, by changing its Cover Algorithm/
properties. The purpose of steganography is to set up a secret Object Technique Object
communication path between two parties such that any person
in the middle cannot detect its existence; the attacker should
not gain any information about the embedded data by simply
looking at cover file or stego file. Steganography is the art of
hiding information in ways that prevent the detection of Stego Key
hidden messages. Steganography, derived from Greek,
literally means “covered writing.” It includes a vast array of
secret communications methods that conceal the message’s Figure 1. Basic steganography model
very existence. These methods include invisible inks,
microdots, character arrangement, digital signatures, covert The basic model of steganography uses a cover object i.e.
channels, and spread spectrum [2]. Steganography is any object that can be used to hold secret information inside,
commonly misinterpreted to be cryptography or the secret message i.e. the secret information that is to be sent
watermarking. While they are related in many ways, there is a to some remote place secretly, a stego key that is used to
fundamental difference in the way they are defined and the encode the secret message to make its detection difficult and a

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Vol. 8, No. 7, October 2010

steganography algorithm/technique i.e. the procedure to hide Image – also known as spatial – domain techniques embed
secret message inside cover object. The outcome of the messages in the intensity of the pixels directly, while for
process is the stego object i.e. the object that has the secret transform – also known as frequency – domain, images are first
message hidden inside. This stego object is sent to the transformed and then the message is embedded in the image
receiver where receiver will get the secret data out from the [1].
stego image by applying decoding algorithm/ technique. In spatial domain methods a steganographer modifies the
In modern era, steganography is implemented by using secret data and the cover medium in the spatial domain, which
digital media. Secret message is embedded inside digital cover involves encoding at the level of the LSBs [6]. The best widely
media like text, images, audio, video or protocols depending known steganography algorithm is based on modifying the
least significant bit layer of images, hence known as the LSB
upon requirement and choice of the sender. Among other types
technique. Spatial domain algorithms embed data by
of steganography, image steganography is most widely used. substituting carefully chosen bits from the cover image pixels
The reason behind the popularity of image steganography is with secret message bits. LSB technique is the most widely
the large amount of redundant information present in the used technique of image steganography. In this technique the
images that can be easily altered to hide secret messages inside least significant bit of all the cover image pixels is replaced
them. with the message bits. In a 24-bit image each pixel contains 3
bytes (one for each Red, Green and Blue component), so we
A. Applications of Steganography can store 3 bits in each pixel. Some algorithms use all pixels to
hide data bits, while others use only specific areas of image.
Steganography has a wide range of applications. The major Our proposed technique is also based on the LSB method to
application of steganography is for secret data communication. show existence of data in a particular channel.
Cryptography is also used for the same purpose but Transform domain techniques first transform the cover
steganography is more widely used technique as it hides the images and then hide the data inside them. Transform domain
existence of secret data. Another application of steganography techniques [7] hide data in mathematical functions that are in
is feature tagging. Captions, annotations, time stamps, and compression algorithms. Discrete Cosine Transform (DCT)
other descriptive elements can be embedded inside an image, technique is one of the commonly used transform domain
such as the names of individuals in a photo or locations in a algorithm for expressing a waveform as a weighted sum of
map. A secret copyright notice or watermark can be embedded cosines. The data is hidden in the image files by altering the
inside an image to identify it as intellectual property. This is DCT coefficient of the image. Specifically, DCT coefficients
the watermarking scenario where the message is the which fall below a specific threshold are replaced with the
watermark. secret bits. Taking the inverse transform will provide the stego
Steganography can be also used to combine explanatory image. The extraction process consists in retrieving those
information with an image (like doctor's notes accompanying specific DCT coefficients. Jpeg Steganography is the most
common example of transform domain technique of image
an X-ray).Steganography is used by some modern printers,
including HP and Xerox brand color laser printers. Tiny steganography.
yellow dots are added to each page. The dots are barely visible A good technique of image steganography aims at three
aspects. First one is capacity, i.e. the maximum data that can be
and contain encoded printer serial numbers, as well as date
stored inside cover image. Second one is the imperceptibility,
and time stamps. The list of applications of image
i.e. the visual quality of stego image after data hiding and the
steganography is very long. last is robustness i.e. security against attacks [4].

II. IMAGE STEGANOGRAPHY
III. PROPOSED TECHNIQUE
Image steganography uses images as the cover object to
LSB encoding is a method that claims to provide good
hide the secret data. Images are the most widely used cover
capacity and imperceptibility. Still the existing methods do not
objects as they contain a lot of redundant information.
use the full capacity of cover image. Many techniques like [8-
Redundancy can be defined as the bits of an object that
13] have been developed to use the more and more number of
provide accuracy far greater than necessary for the object’s
bits per pixel to achieve more data hiding capacity. We have
use and display [3]. The redundant bits of an object are those
developed a technique for hiding text using image
bits that can be altered without the alteration being detected
steganography that use 7 bits per pixel to hide data and still no
easily [5]. Image files fulfill this requirement so they are very
visual changes in the stego image. We convert the messages
commonly used as a medium for steganography. Audio files
into ASCII code and then 7 bit ASCII code of each letter is
also contain redundant information but not used as widely as
matched with pixel values of cover image. To mark the
image files. A number of techniques have been proposed to
presence of data in a particular pixel we use LSB method.
use images as cover files. These techniques can be categorized
Which component of the pixel contains data that will be
in the following two ways:
showed by using different combinations of Least Significant
• Spatial domain techniques
Bits. As we know that each pixel of the BMP image is made
• Transform domain techniques up of three bytes, one for Red, one for Green and one for Blue
component of the pixel. Each character of the secret message

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is converted into ASCII code, which is 7-bit code. So to map a the criteria given in Table 2. As clear from the Table 2, we set
character to a pixel component we need only 7 bits of the the values of indicator channels as 00 if data matches in red
pixel. The least significant bit of every channel is free to be channel, 01 if matches with green and 10 if matches with blue
used as indicator to show that data is present in this channel. channel. If there is no match then the value is set as 11. Then
We will use LSB of two channels to mark presence of data in the same procedure is repeated with the next pixel of the cover
any of the three channels. The basic technique is to convert image.
secret message into ASCII code. To decide which channels
A. Flow Chart of Encoding Process
will act as indicator channels, we will use a pseudo random
number. For every character of secret message, we generate a
pseudo random number, depending upon the value of pseudo
random number we decide that which two channels will act as Read Cover Image. Read Secret Start
Message, Convert into ASCII
indicator channels. After generating the number, convert that d
into binary bit sequence. Count number of 1s present in the
bit sequence and number of zeros present in the bit sequence. Extract Length of Secret Message,
Also calculate the parity of the pseudo random number. Now Store in L. Hide in first row of
cover Image.
depending upon binary bit sequence of pseudo random number
following three cases will be there and one case will be used to
select set of indicator channels. The selection procedure is Start from next row of cover.
shown in table 1.

TABLE I. CRITERIA FOR SELECTION OF INDICATOR CHANNEL Take next character of message,
put in C. Take next Pixel.

Case Indicator Order1(if parity Order2(if
channel is even) parity is odd) Find pair of indicator channels ,
based on pseudo random number
set

If no. of 1s are more RG RG GR Y
Set LSB of both
If 7MSBs of red Indicator Channels equal
than number of 0s Channel==C to Zero, L=L-1.

If no. of 0s are more GB GB BG
N
than number of 1s
Y Set LSB of Indicator
If 7MSBs of Channel1=0 and
If no. of 0s are RB RB BR Green indicator
Channel==C Channel2=1.L=L-1.
equal to number of
1s
N
Y
TABLE II. CRITERIA TO SET VALUE OF INDICATOR CHANNELS Set LSB of Indicator
If 7MSBs of Channel1=1 and
Blue indicator
Channel==C Channel2=0,L=L-1.
Data channel (depending LSB of indicator 1 LSB of indicator 2
upon match) N

RED Channel 0 0 Set LSB of indicator
Channels equal to 1

GREEN Channel 0 1

BLUE Channel 1 0
Go to next pixel

No match 1 1
Y

If L>0

After selecting set of indicator channels we start from the first
row of cover image. We hide length of secret message in first
row using LSB method. Then start tracing from the second
row to match first character of secret message with 7 MSBs of Stop

all three components of first pixel. If there is a match with any
component then value of indicator channels is set according to

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B. Encoding Algorithm D. Flow Chart of Decoding Process

The encoding part of the algorithm is as follows. Read Stego Image. Start

Step 1: Read cover image.
Step 2: Read secret message, Convert into ASCII. Extract Length of Secret
Message, Stored in first row
Step 3: Extract message length and store it into the variable of stego Image.
L.
Step 4: Hide message length into the first row of cover
image using LSB method. Start from next row of cover.

Step 5: Start from second row of cover image. Take first
pixel. Find pair of indicator channel
Step 6: Take next character from C store in a temporary based on pseudo random
number
variable B.
Step 7: Select indicator channel pair, depending upon the
pseudo random number.
Extract LSB of both
Step 8: Match the character with 7 MSBs of all three Indicators.
channels turn wise. If there is a match, set the value
of indicator channels accordingly.
Step 9: Set L = L-1. Go to step 11. Y
If the value Extract data from
Step 10: If data not matched with any channel, set value of extracted is Red channel. store
indicator channels equal to 1. Go to next pixel and =00 in C. L=L-1.

go to step 7.
Step 11: Go to next pixel. N
Step 12: Check if L>0. If yes go to step no.6.
Step 13: Stop when all characters are consumed and L is Y Extract data from
If the value
equal to zero. extracted is Green channel.
=01 store in C. L=L-1.

C. Decoding Algorithm
N
The decoding process will depend upon the value of the
pseudo random number generator function. Number generator
will generate the same numbers as it generated at sender end Y
Extract data from
If the value
Blue channel. store
during decoding process. Depending upon the value of number extracted is
in C. L=L-1.
=10
by using table 2 we will find out the set of indicator channels.
After that depending upon the value of indicator channels we
will find out that data lies in which channel of which pixel. N

The different steps of the decoding process are as follows:
Data does not exist in
this pixel.
Step 1: Read stego image.
Step 2: Read the LSB of first row to find out L.
Step 3: Start from second row of cover image. Take first
pixel of second row. Go to next pixel
Step 4: Select indicator channel pair, depending upon the
pseudo random number.
Y
Step 5: Depending upon the set of indicator channel pair,
If L>0
extract LSB of indicator 1 and indicator 2.
Step 6: Depending upon value of indicator channels, N
extract the data from pixel.
Step 7: If this value is 11 that means data does not exist in Data is in C. Stop
this pixel.
Step 8: Go to next pixel.
Step 9: Check if L>0. If yes go to step no 4. IV. RESULTS
Step 10: Stop when all characters are retrieved and L is To compute the performance of the proposed technique we
equal to zero. have conducted a series of experiments. To calculate the
Step 11: The values of C are in ASCII code, convert them efficiency we have used Peak Signal to Noise Ratio as major
into equivalent characters. parameter. The PSNR measures Peak Signal to Noise Ratio

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between two images, and higher the PSNR, the more the
TABLE III. EFFECT OF INCREASE IN SIZE OF SECRET DATA ON
quality of stego image. To evaluate the results we have applied
PSNR
the technique to a number of colored images out of which
flowers.bmp having size 32700 has been shown here to Image Message Length PSNR
demonstrate the results achieved.
flowers.bmp 100 59.638

flowers.bmp 50 61.698

flowers.bmp 25 63.372

TABLE IV. EFFECT OF INCREASE IN SIZE OF SECRET DATA ON
MEAN
Image Length of Mean Mean Difference
Secret Data (Cover) (Stego)
Figure 1. Cover image flowers.bmp
Flowers.bmp 100 75.354 75.422 0.068

Flowers.bmp 50 75.354 75.397 0.043

Flowers.bmp 25 75.354 75.384 0.030

TABLE V. EFFECT OF INCREASE IN SIZE OF SECRET DATA ON
STANDARD DEVIATION

Image Length of Std. Dev. Std. Dev. Difference

Figure 2. Stego image flowers.bmp Secret Data (Cover) (Stego)

Flowers.bmp 100 72.174 72.159 0.015

Flowers.bmp 50 72.174 72.163 0.011

Flowers.bmp 25 72.174 72.166 0.008

Figure 3. Histogram of cover image flowers.bmp

V. CONCLUSION & FUTURE WORK
In this paper, we have presented a new technique to hide
text inside images. The main objective was to achieve more
security against statistical and visual attacks. The results show
that we have been successful in achieving the same. The
technique provides more security against visual attacks as the
cover and stego images does not show the visible differences.
Figure 4. Histogram of stego image flowers.bmp
The technique is also statistically secure for small text
messages as there is no visible difference in the histograms of
Fig 3. Shows the histogram of cover image and Fig 4. Shows
cover and stego images. We have tried to achieve more
the histogram of stego image. It is clear from the histograms
capacity by using the 7 bits per pixel to hide data. Results
that there is negligible change in the histogram of stego image.
show a very good value of PSNR that means technique shows
So, proposed technique is secure fom statistical attacks. Table
better imperceptibility.
3 shows value of PSNR after hiding messages of different
The future work includes increasing the capacity further by
sizes in the cover image flower.bmp. The results show a
modifying the technique. Technique can be modified to hide
higher value of PSNR is achieved by the technique. Table 4
more data without noticeable visual changes. Some type of
and 5 show statistical results achieved in terms of mean and
mapping table can be used to increase the chances of matching
standard deviation values of cover and stego images.
data with pixel values. Hence focus of the future work is to

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achieve more capacity while retaining the robustness against [11] Nameer N. EL-Emam , “Hiding a large amount of data with high
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Computer Journal, vol. 31,pp. 26-34, February 1998. AUTHORS PROFILE
[3] D.L. Currie, & C.E. Irvine, “Surmounting the effects of lossy
compression on steganography”, 19th National Information Systems Sukhpreet Kaur received her B.Tech in Computer
Security Conference, pp. 194-201, 1996. Science & Engineering from Punjab Technical
[4] H. Zhang and H. Tang, “A novel image steganography algorithm against University, Punjab, India in 2007. She is pursuing her
statistical analysis,” proceeding of the IEEE, vol. 19, pp. 3884-3888, M.Tech in Computer Engineering from Punjabi
August 2007. University, Patiala. There are more than 8 research
[5] R.J. Anderson, F.A.P.Petitcolas, “On the limits of steganography”, papers in various national and international conferences
IEEE Journal of selected Areas In Communications, vol 16, pp. 474-481, in the credit of Ms Kaur. Her interest areas include fields
May 1998. of network security and steganography. Currently, she is working as lecturer
in computer science in the Department of computer science and engineering,
[6] A. Cheddad, J. Condell, K. Curran, P. Kevitt, “Digital image Baba Farid College of Engineering and Technology, Bathinda, Punjab State,
steganography- survey and analysis of current methods,” Signal India.
Processing, vol. 90, pp. 727–752, 2010.
[7] D. Bhattacharyya, A. Roy, P. Roy, T. Kim “Receiver compatible data Sumeet Kaur received her B.Tech in Computer
hiding in color image,” International Journal of Advanced Science and Engineering from Sant Longowal Institute of Engineering
Technology, vol. 6, pp. 15-24, May 2009. & Technology(Deemed University) Punjab in 1999 and
[8] Adnan Gutub, Ayed Al-Qahtani, Abdulaziz Tabakh “Triple-A: secure her M.Tech from Punjabi University, Patiala in 2007. She
RGB image steganography based on randomization” AICCSA, has more than 10 research papers in different national and
IEEE/ACS International Conference on Computer Systems and international conferences. Currently,she is working as
Applications, Rabat, Morocco, pp. 400-403 , 2009. lecturer in computer science in the Department of
[9] A. Kaur, R. Dhir, G. Sikka,” A new image steganography based on first computer engineering, Yadavindra College of Engineering
component alteration technique”, International Journal of Computer Punjabi University Guru Kashi Campus, Talwandi Sabo, Punjab State, India.
Science and Information Security (IJCSIS), vol. 6, pp. 53-56, 2009. Her interest areas include encryption, network security, image processing and
steganography.
[10] M.T. Parvez , A. Gutub , "RGB intensity based variable-bits image
steganography", APSCC 2008 –Proceedings of 3rd IEEE Asia-Pacific
Services Computing Conference, Yilan, Taiwan, December 2008.

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