A Novel Steganography Technique that Embeds Security along with Compression

Anuradha, Nidhi & Rimple
International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (5) : 2011 469
A Novel Steganography Technique That Embeds Security Along
With Compression
Anuradha anu107sharma@gmail.com
Student/CSE DCRUST,Murthal
Sonepat, 131039, India
Nidhi nidhi.sharma.1012@gmail.com
Student/IT Banasthali Vidyapeeth
Bansathali, 304022, India
Rimple rimple.gilhotra@hotmail.com
Student/CSE Banasthali Vidyapeeth
Bansathali, 304022, India
Abstract
Problem faced by today’s communicators is not only security but also the speed of
communication. This paper provides a mechanism that increases the speed of communication by
reducing the size of content; for this data compression method is used and security factor is
added by using Steganography. Firstly, the focus has been made on Data Compression and
Steganography. Finally, proposed technique has been discussed. In Proposed technique first
data is compressed to reduce the size of the data and increase the data transfer rate. Thereafter
on compressed data state table operation is applied to improve the security. Then, this is used as
the input to the LSB technique of Steganography. At receiver end, the LSB extraction technique is
used, thereafter the state table operation in reverse form is applied and finally the original data is
obtained. Hence our proposed technique is effective that can reduce data size, increases data
transfer rate and provides the security during communication.
Keywords: Compression, Arithmetic Coding, Steganography, Hexadecimal, One Time Pad,
Least Significant bit (LSB).
1. INTRODUCTION
The present network scenario demands exchange of information with more security and reduction
in both the space requirement for data storage and the time for data transmission [9]. This can be
accomplished by compression and data hiding. Now a day’s people use the network as the basic
transport for transmitting their personal, secure and day to day information. Hence they need
some form of security from the third person during transmission which is provided by
Steganography where Steganography refers to the science of invisible communication [10, 11].
Along with that sometimes the data that is to be sent is in huge amount that requires lots of space
and bandwidth, so we must have a mechanism with the help of which we can reduce the size of
data as well as time and bandwidth is saved; this is done by using Arithmetic Coding.
Proposed mechanism embeds the compressed data behind the cover data; this mechanism is
used to achieve the present network scenario for exchange of information with more security and
compression.
2. DATA COMPRESSION
Compression is used just about everywhere. Compression algorithms reduce the redundancy in
data representation to decrease the storage required for that data. The task of compression
consists of two components, an encoding algorithm that takes a message and generates a
“compressed” representation and a decoding algorithm that reconstructs the original message or

some approximation of it from the compressed representation. We distinguish between lossless
algorithms, which can reconstruct the original message exactly from the compressed message,
and lossy algorithms, which can only reconstruct an approximation of the original message.
Lossless algorithms are typically used for text, and lossy for images and sound where a little bit of
loss in resolution is often undetectable, or at least acceptable [4].
2.1 Arithmetic Coding
In information theory an entropy encoding is a lossless data compression scheme that is
independent of the specific characteristics of the medium. One of the main types of entropy
coding assigns codes to symbols so as to match code lengths with the probabilities of the
symbols. Typically, these entropy encoders are used to compress data by replacing symbols
represented by equal-length codes with symbols represented by codes where the length of each
codeword is proportional to the negative logarithm of the probability. Therefore, the most
common symbols use the shortest codes. The most popular entropy encoding is Arithmetic
Encoding [5].
In arithmetic coding, a message is represented by an interval of real numbers between 0 and 1.
As the message becomes longer, the interval needed to represent it becomes smaller, and the
number of bits needed to specify that interval grows. Successive symbols of the message reduce
the size of the interval in accordance with the symbol probabilities generated by the model. The
more likely symbols reduce the range by less than the unlikely symbols and hence add fewer bits
to the message.[6] Before anything is transmitted, the range for the message is the entire interval
[0, l), denoting the half-open interval 0 <= x < 1 [12]. Thus, the algorithm successively deals with
smaller intervals, and the code string, viewed as a magnitude, lies in each of the nested intervals.
The data string is recovered by using magnitude comparisons on the code string to recreate how
the encoder must have successively partitioned and retained each nested subinterval. [13]
3. STEGANOGRAPHY
Steganography is the art and science of communicating in such a way that the presence of a
message cannot be detected [1]. Steganography involved a Greek fellow named Histiaeus. As a
prisoner of a rival king, he needed a way to get a secret message to his own army. He shaves the
head of a willing slave and tattoos his message on to the bald head. When hairs grew back, off
he went to deliver the hidden writing in person [3].
Steganography derives from the Greek word “steganos” means covered or secret and “graphy”
means writing. On the simplest level Steganography is hidden writing, whether it consists of
invisible ink on paper or copyright information hidden within an audio file. Today, Steganography,
“stego” for short, is most often associated with in other data in an electronic file. This is done by
replacement the least important or most redundant bits of data in the original file i.e. bits that the
human eye or ear hardly misses with hidden data bits [2].
3.1 Where it Comes From [3,8]
One of the earliest uses of Steganography was documented in histories. Herodotus tells how
around 400 B.C. Hisitieaus shaved the head of his most trusted slave and tattooed it with the
message which disappeared after the hair has regrown. The purpose of this message was to
investigate the revolt against the Persians. Another slave could used to send the reply.
During the American Revolution, invisible ink which would glow over a flame was used by both
the British and the American’s to communicate secretly. German hides text by using invisible ink
to print small dots above or below letters and by changing the heights of letter-strokes in cover
texts.
In world war 1prisoners of war would hide Morse code messages in letters home by using the
dots and dashing on I, j, t and f. censors intercepting the messages were often altered by the
phrasing and could change them in order to alter the message.

During world war 2nd the Germans would hide data in microdots. This involved photographing the
message to be hidden and reducing the size so that it can be used as a period within another
document.
3.2 Types of Steganography[3]
Steganography can be split into two types, these are Fragile and Robust. The following section
describes the definition of these two different types of Steganography.
3.2.1 Fragile
Fragile Steganography involves embedding information into a file which is destroyed if the file is
modified. This method is unsuitable for recording the copyright holder of the file since it can be so
easily removed, but is useful in situations where it is important to prove that the file has not been
tampered with, such as using a file as evidence in a court of law, since any tampering would have
removed the watermark. Fragile Steganography techniques tend to be easier to implement than
robust methods.
3.2.2 Robust
Robust marking aims to embed information into a file which cannot easily be destroyed. Although
no mark is truly indestructible, a system can be considered robust if the amount of changes
required to remove the mark would render the file useless. Therefore the mark should be hidden
in a part of the file where its removal would be easily perceived.
3.3 Key Features
There are several key features regarding Stegangraphy and its usage are as follows:
• The main goal of Steganography is to hide a message m in some audio or video (cover)
data d, to obtain new data d', practically indistinguishable from d, in such a way that an
eavesdropper cannot detect the presence of m in d'.
• The goal of Steganography is to hide the message in one-to-one communication
• We can hide as much data as possible.
• Ease of detection level should be Difficult.
• We can hide as much data as possible.
• Goal of detector is to detect the hidden data.
3.4 Applications
There are various areas where Steganography can be applied:
• Confidential communication and secret data storing.
• Protection of data alteration
• Access control system for digital content distribution.
• Media Database systems
• Corporate espionage, Cover Communication by Executives, Drug dealers, Terrorists.
4. PROPOSED TECHNIQUE
The proposed technique is based on the concept of arithmetic coding and Steganography in
which a word of text is converted into floating point number that lie in range between 0 and 1.
This floating point number is converted into hexadecimal number and after that one time pad and
a state table is used to hide the compressed hexadecimal data.
At Receiver end, data is extracted by using the Steganography method that will be explained
later; after that decompression is done to obtain the original word.
4.1 Compression and Hiding
Firstly input symbol is compressed using arithmetic coding after that one time pad and the state
table is used on the result of arithmetic coding.
ALGORITHM
To compress and encrypt the message Algorithm includes following steps:

Step 1: Using table encode the input symbol.
a) Initialize lower_ bound=0, upper_ bound=1
b) While there are still symbols to encode
Current _range = upper _bound - lower _bound
Upper_ bound = lower _bound + (current _range * upper _bound of new symbol)
Lower_ bound = lower_ bound + (current _range * upper_ bound of new symbol)
End while
Step 2: The string may be encoded by any value within the probability range and after that
convert the output decimal number into hexadecimal data.
Step 3: Choose 2nd MSB of the selected cover image. This is the one time pad.
Step 4: Now, the state table operation is applied on the hexadecimal equivalent and the one time
pad. The information about this state table is exchanged between sender and receiver earlier.
This state table will help in confusing the intruder because the intruder does not know anything
about the state table. Hence, the security level is increased further. The state table is given in
Table 1. [7]
Input Output
0 0 0 0
1 0 0 1
0 1 1 0
1 1 1 1
TABLE 1: State Table
Step 5: The output obtained from step 4 is used in LSB substitution method of Steganography.
Step 6: The final embedded cover image is send to the receiver side.
4.2 Decompression and Extraction
Algorithm
Step 1: Extract the LSB’s from the cover image.
Step 2: Choose 2nd MSB’s of the cover Image, this is the onetime pad.
Step 3: Apply the state table (Table 1) to the LSB’s and the 2nd MSB’s of the cover image.
Step 4: The output obtained from step 3 is the original hidden data in hexadecimal format.
Step 5: Convert the hexadecimal format into decimal equivalent.
Step 6: Apply arithmetic decoding procedure.
Encoded_ value=Encoded input
While string is not fully decoded
Identify the symbol containing encoded value within its range
current_ range = upper _bound of new symbol - lower _bound of new symbol
encoded value = (encoded _value - lower_ bound of new symbol) ÷ current_ range
End while

Output: The output is the original symbol.
4.3 Example
Suppose Input Data is: “ganga”
Step 1: Create Probability Table
For character g:
Occurrence of character ‘g’ in Input data is “2”.
Probability is 2/5=0.4
For character a:
Occurrence of character ‘a’ in Input data is “2”.
For character n:
Occurrence of character ‘n’ in Input data is “1”.
The probability table is prepared according to the occurrences of the letters. This is explained in
table 2.
Symbol Probability Range(lower_ bound, upper_
bound)
A 40% [0.00,0.40)
G 40% [0.40,0.8)
N 20% [0.8,0.1)
TABLE2: Symbols along with probability of occurrence
4.3.1 Compression and Hiding
Data to be encoded is “ganga”
Step1:
Encode 'g'
current_ range = 1 - 0 = 1
upper bound = 0 + (1 × 0.4) = 0.4
lower bound = 0 + (1 × 0.8) = 0.8
Encode 'a'
current range = 0.8 - 0.4 = 0.4
upper bound = 0.4 + (0.4 × 0.0) = 0.4
lower bound = 0.4 + (0.4 × 0.8) = 0.56
Encode 'n'
current range = 0.56-0.4 = 0.16
upper bound = 0.4 + (0.16 × 0.8) = 0.528
lower bound = 0.4 + (0.16 × 1) = 0.56
Encode 'g'
current_ range = 0.56-0.528 = 0.032
upper bound = 0.528 + (0.032 × 0.4) = 0.5408
lower bound = 0.528 + (0.032 × 0.8) = 0.5536

Encode 'a'
current range = 0.5536 - 0.5408 = 0.0128
upper bound = 0.5408 + (0.0128 × 0.0) = 0.5408
lower bound = 0.5408 + (0.0128 × 0.4) = 0.54592
Step2:
The string "ganga" may be encoded by any value within the range [0.5408, 0.54592).
Now output is 0.54260 and its hexadecimal equivalent= 01010100001001100000
Step3: Select an Image which is considered as a cover image.
11001010 10101010 11100010 10100001 11100011
11100010 10100001 10101101 10001001 10101010
10101101 10101010 10100001 11100011 10100001
11100011 11001010 10101010 11001010 11100010
10101010 10101101 10100001 10101010 11100010
10101010 11100011 11001010 11100010 10101010
10101010 11001010 10101010 10101010 11100011
10101010 11100010 11100011 10101010 11001010
TABLE 3: Cover image
Step4: Choose 2nd MSB’s of cover Image as a one time pad key.
Step5: Our One time pad is – 10101100000001011011
Data- 01010100001001100000 from step 2.
Apply operation on bits according to the given state table [1].
Step6: Final Output is: 0110011001110000000010000011100101000101
Step7: Now Apply LSB substitution method of stenography to hide data in cover image.
11001010 10101011 11100011 10100000 11100010
11100011 10100001 10101100 10001000 10101011
10101101 10101011 10100000 11100010 10100000
11100010 11001010 10101010 11001010 11100010
10101011 10101100 10100000 10101010 11100010
10101010 11100011 11001011 11100011 10101010
10101010 11001011 10101010 10101011 11100010
10101010 11100010 11100011 10101010 11001011
TABLE 4: Cover image with data hidden inside.
4.3.2 Decompression and Extraction
Step 1: Extract the LSB’s of cover image which gives us hidden data.
Hidden Data: 0110011001110000000010000011100101000101
Step2: Reverse the operation on bits by taking combination of 2 bits, which gives the combination
of one time pad key and actually compressed data i.e.
100110011011000000001000011011010001010
Step3: Separate the one time pad key and compress data i.e.

One time pad key: 10101100000001011011
Data- 01010100001001100000
Step4: Convert hexadecimal format into decimal format i.e. 0.54260
Step5: Using the probability ranges from table decodes the three character string encoded as
0.54260.
Decode first symbol
0.54260 is within [0.4, 0.8)
0.54260 encodes 'g'
Remove effects of 'g' from encode value
Current _range = 0.8 - 0.4 = 0.4
Encoded _value = (0.54260-0.4) ÷ 0.4 = 0.3565
Decode second symbol
0.3565 is within [0.0, 0.4)
0.3565 encodes 'a'
Remove effects of 'a' from encode value
current range = 0.0 - 0.4 = 0.4
encoded value = (0.3565 - 0.0) ÷ 0.4 = 0.89125
Decode third symbol
0.89125 is within [0.8, 1)
0.89125 encodes ’n’
Remove effects of 'n' from encode value
Current _range = 1 - 0.8 = 0.2
Encoded _value = (0.89125-0.8) ÷ 0.2 = 0.45625
Decode second symbol
0.45625 is within [0.4, 0.8)
0.45625 encodes 'g'
Remove effects of 'g' from encode value
Current range = 0.0 - 0.4 = 0.4
Encoded value = (0.45625 - 0.4) ÷ 0.4 = 0.14063
Decode third symbol
0.14063 is within [0.8, 1)
0.14063 encodes ’a’
Now we are with our secret data i.e. “ganga”
5. BENEFITS
• In proposed system generated cipher text takes very less bandwidth of secure channel.
• Highly Secure.
6. CONCLUSION AND FUTURE SCOPE
The Present network scenario demands exchange of information with reduction in both space
requirement for data storage and time for data transmission along with security. Our proposed
technique fulfils all such requirements as this technique uses the concept of data compression
and Steganography. Along with that the state table that increases the security further because the
intruder does not have any idea about this state table. By using this technique we can reduce the

size of data and after that compressed data can be hidden to provide the security. Hence this
technique increased the data transfer rate and security during data communication. There exists
some enhancement in the compression method used as future work. We can use any other
compression method that will provide better compression ratio than the existing one.
7. REFERENCES
[1]. Christian Cachin, “An Information-Theoretic Model for Steganography”, A preliminary
version of this work was presented at the 2
nd
Workshop on Information Hiding, Portland,
USA, 1998, and appears in the proceedings (D. Aucsmith, ed., Lecture Notes in
Computer Science, vol. 1525, Springer).Original work done at MIT Laboratory for
Computer Science, supported by the Swiss National Science Foundation (SNF).March 3,
2004, pp. 1-14.
[2]. Eric Cole, Ronald L. Krutz, James W. Conley, “Network security bible” Wiley Pub. 2005,
pp. 482-520
[3]. SecondLieutentJ.caldwell,“Steganography”,UnitedStatesAirForce,http://www.stsc.hill.af.mil
/crosstalk/2003/caldwell.pdf, June2003.
[4]. Guy E. Blelloch. Computer Science Department. Carnegie Mellon University blellochcs.
cmu.edu.http://www.cs.cmu.edu/afs/cs/project/pscicoguyb/realworld/www/compression.
pdf ,September 25, 2010.
[5]. V.Kavitha , K.S Easwarakumar. “Enhancing Privacy in Arithmetic Coding” ICGST-AIML
Journal, Volume 8, Issue I, pp. 23-28, June 2008.
[6]. IAN H. WIllEN, RADFORD M. NEAL, and JOHN G. CLEARY. “Arithmetic coding for data
compression.” Communications of the ACM , Volume 30 Number 6,pp.521-540, June
1987.
[7]. Ajit Singh, Nidhi Sharma. “Development of mechanism for enhancing the Data Security
using Quantum Cryptography.” Advanced Computing: An International Journal (ACIJ),
Vol.2, No.3, pp.22-25, May 2011.
[8]. Herodotus. The Histories. London, England: J. M. Dent & Sons Ltd, 1992.
[9]. Ajit Singh , Rimple Gilhotra. “Data security using private key encryption system based on
arithmetic coding.” International Journal of Network Security & Its Applications (IJNSA),
vol.3, no.3, May 2011.
[10]. Mehdi Kharrazi, Husrev T. Sencar Nasir Memon. “Performance study of common image
Steganography and steganalysis techniques.” Journal of Electronic Imaging 15(4),041104
(Oct–Dec 2006)
[11]. M. Kharrazi, H. T. Sencar, and N. Memon, Image Steganography Concepts and Practice,
Lecture Notes Series, Institute for Mathematical Sciences, National University of
Singapore, Singapore _2004
[12]. J.A Storer, (1988) “Data Compression: Methods and Theory” Computer Science Press.
[13]. Glen G. Langdon, (1984) “An introduction to arithmetic coding”, IBM Journal of Research
and Development Volume 28, No.2

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