LSB Steganography using Bits Complementationiieng.org/images/proceedings_pdf/8221E1114029.pdfImage LSB steganography is the technique of embedding secret information bits in the least

Abstract—Least Significant Bit Steganographic techniques are

the most simple and commonly used data hiding techniques using

images as cover files. In LSB Steganographic techniques secret

information bits are replaced with the least significant bits of images

pixels. A number of LSB Steganographic techniques are available. In

this work a new LSB technique is proposed using bit

complementation. 4 of the 8 bits representing pixels are taken as

target bits so as to replace with it the secret data bits or its

complement depending on its matching decision. Bits from 2 to 5 are

taken as the target bits. The first bit is reserved for decision whether

to replace the target bits with the data bits or with the complement of

the data bits. The proposed technique ensures ultimate security of the

secret information and highest data hiding capacity compared to any

of the technique available so far.

Keywords—Steganography, LSB, Security, Image, Complement,

Data hiding

I. INTRODUCTION

TEGANOGRAPHY is a communication technique that

hides the secret information in another information type

[1]. Greek developed the technique of writing secret messages

on wooden tablets and then covering the tables with wax.

Pirate’s legends introduced the technique of tattooing the

shaved heads of their men and when the hair would grow it

will conceal the secret messages. In World War II, Germans

developed a unique technique called microdots [4]. They

printed high quality images to the size of a small dot. In Cold

War the Russian and the Americans used to hide sensors in the

enemy's facilities. These sensors would send data without been

caught [4].

With the expansion of internet and electronic ways of

communication the need for secure communication and

Zakir Khan is with the Department of Information Technology, Hazara

University Mansehra, Pakistan (corresponding author’s phone: +92 323

9029100; e-mail: [email protected]).

Mohsin Shah, is with Optical Engineering, School of Optoelectronics,

Beijing Institute of Technology, China, on leave from the Department of

Information Technology, Hazara University Mansehra, Pakistan (e-mail:

[email protected]).

Muhammad Naeem is with is with the Department of Information

Technology, Hazara University Mansehra, Pakistan (e-mail:

[email protected]).

Danish Shahzad, is with Department Computer Engineering, Kadir Has

University Istanbul, Turkey (e-mail: [email protected]).

Toqeer Mahmood is with the Department of Computer Engineering,

University of Engineering and Technology Taxila, Pakistan (e-mail:

[email protected]).

protection of user’s data is felt. Various cryptographic

techniques are available to protect user data. These

techniques change the form of data so attackers are easily

attracted to it. On the other hand Steganographic techniques

hides user’s data in different file formats such as image,

audio, video, and text to which attackers are not attracted as

they cannot suspect that secret information is passing before

them [3].

A Steganographic system is composed of five

components: a) User secret data; b) Cover file (image,

audio, video, text); c) The resultant stego file; d) Hiding

algorithm and e) Decoding algorithm [2].

The least significant bit (LSB) Steganographic technique

is the simplest and commonly used among different

techniques available in the literature. In LSB technique,

secret information is embedded in the least significant bits

representing pixels or samples of the cover file. All the LSB

techniques available in the literature have their merits and

demerits in terms of payload and perceptibility of the secret

information [1], [5].

The proposed technique achieves highest SNR and

maximum payload compared to any of the available LSB

technique in the literature. We analyze the results by taking

into consideration various image. It is observed that the

proposed technique in terms of the image quality attributes

is well above all the LSB techniques available in literature

so far.

The rest of the paper is arranged as follows: Section II gives

a summary of related work, while Section III explains

proposed technique and section IV concludes the paper.

II. LITERATURE REVIEW

Image LSB steganography is the technique of embedding

secret information bits in the least significant bits of pixels of

an image. Image steganography is classified to the following

type- Transform technique, Distortion technique, LSB

technique, and Statistical technique. A comprehensive survey

of the most recent LSB Steganographic techniques is presented

by Priya Thomson in [5]. Secret data is embedded in the least

significant bits GIF images which is palette based format and

stores image colors in alpha channel or a lookup table [6]. The

data embedding capacity of GIF images is less and is

vulnerable to attacks. Difference expansion technique makes

use of redundancy and embeds 1 bit of secret information in 2

bits of cover image. Producing low visual quality stego images

is the main drawback of this technique [7]. Corners of images

LSB Steganography using Bits

Complementation

Zakir Khan, Mohsin Shah, Dr. Muhammad Naeem, Danish Shehzad, and Toqeer Mahmood

S

Int'l Conf. on Chemical Engineering & Advanced Computational Technologies (ICCEACT’2014) Nov. 24-25, 2014 Pretoria (South Africa)

http://dx.doi.org/10.15242/IIE.E1114029 84

are exploited for secret information bits embedding [8]. As not

enough data bits can be embedded in the corners of images the

capacity of this technique is less. In edge based LSB technique

edges of an image are identified where pixels from their

neighbors are different [9]. Data is embedded in the LSB bits

of pixels present in edges of an image. This technique suffers

from low information hiding capacity. Pixel value differencing

and modulus function is used to embed information bits into

the LSB of images [10]. This technique embeds less

information bits in images. Interpolation technique embeds

more secret information bits in complex areas of images and

less in smooth regions [11]. This technique makes use of

interpolation error to hide data in images. Adaptive edge based

technique uses a pseudo random number generator to select

embedding areas in images [12]. More information bits are

embedded in sharp region compared to smooth regions.

III. PROPOSED TECHNIQUE

In this paper a new Steganographic technique is proposed

for secure data transmission based on data and its

complement mechanism for secret data transfer. This

technique is based on 4 bits data and its complement

enabling to hide 4-bit data in a single pixel of image and

make it unpredictable for the attackers.

First separate least significant bit and make it decision bit of

data recovery. After separating least significant bit take four

bits from data. Now take complement of the data bits, and

compare both data bits and complement of data bits with the

image 4 LSB excluding the decision bit i.e compare with 2-5

LSB of image. Replace image 2-5 bits of a pixel with data or

complement of data which is nearest to the image bits, means

there is minimum difference between the image bits and

replacement bits. If image bits are replaced with data bits than

replace the decision bit with 0 else replace decision bit with 1

as shown in Figure 1.

The reverse process is based on decision bit. First we read

the decision bit if the value of decision bit is 0 then the next 4-

LSB’s i.e from bit # 2 to bit # 5 are the data bits, and if the

decision bit value is 1 it’s means the 4LSB’s i.e from bit # 2 to

bit # 5 are the complement of data bits. So take complement of

4LSB’s and write to the data file. All this procedure is shown

in the Figure 2.

Fig. 1 Data Encoding Process



Fig. 2 Decoding Process

A. Encoding Process

Step: 1 Read the Data

Step: 2 Read the Image

Step: 3 For Every pixel of Image

Step: 3.1 Read 4LSB’s i.e 2,3,4,5

Step: 3.2 Read a Nibble of Data

Step: 3.3 Get Complement of Data Nibble

Step: 3.4 Compare 4LSB’s with Data Nibble and (Data

Nibble)ʹ

Step: 3.5 Replace 4LSB’s with Nearest One i.e Data

Nibble or (Data Nibble)ʹ

Step: 3.6 if Replacement bit are Data bits

Replace 1LSB with 0

Step: 3.7 else

Replace 1LSB with 1

Step: 4 Write the Stego Image

B. Encoding Process

Step: 1 Read the Stego Image

Step: 2 For Every pixel of Image

Step: 2.1 if decision bit is 0

4LSB’s i.e 2,3,4,5 is data nibble

Step: 2.2 else

Complement of 4LSB’s i.e 2,3,4,5 is data

nibble

Step: 3 write the secret Data to file

C. Example:

The whole Encoding and Decoding process of the proposed

method is illustrated with the help of an example:

Encoding

Suppose we have a pixel 10011011 and data nibble 1001

1st separate the bits of pixel into three parts 100 1101 1

The 100 bits are the most significant bits of pixel

The 1101 are the targeted 4-LSB’s

The last 1 is the least significant bit used as decision bit for

recovery

Now take complement of data nibble i.e. 0110

By comparing the targeted 4-LSB’s with data nibble and the

complement of data nibble

As targeted 4-LSB are near to data nibble so replace the

targeted bits with data bits and replace the decision bit with

0.

So the new pixel will be 100 1001 0

Decoding

Suppose we have a pixel 10010010

1st separate the bits of pixel into three parts 100 1001 0

The 100 bits are the most significant bits of pixel

Read the decision bit

As decision bit is 0

The next 4LSB’s i.e 2, 3, 4, 5 are the data nibble



IV. CONCLUSION

In this paper a new LSB Steganographic technique is

presented. The presented technique is based on the

complementation and matching of the secret information

nibble. Secret information bits are grouped as nibbles and the

complement of each nibble is found. Each nibble or its

complement is replaced with the bits of each pixel called target

bits. Bit number 2 to 5 are the target bits with which secret

information nibble or its complement is replaced. Bit number

first is used for decision whether to replace information nibble

or its complement. The remaining 3 most significant bits are

lift unchanged. The proposed technique hides 4 bits of

information in every pixel of the cover image. This means that

the proposed technique has a high information hiding capacity.

As it cannot be identified that the bits at positions 2 to 5 are

the bits of information or its complement, hence the proposed

technique keeps high security of the secret information.

REFERENCES

[1] M. Hussain and M. Hussain, “A survey of image steganography

techniques”, International Journal of Advanced Science and

Technology, vol. 54, pp. 113-124, 2013.

[2] M. Nosrati, R. Karimi and M. Hariri, “An introduction to steganography

methods”, World Applied Programming, vol. 1, no. 3, pp. 191-195,

2011.

[3] H. Gupta, R. Kumar and S. Changlani, “Enhanced data hiding capacity

using LSB based image steganography method”, International Journal

of Emerging Technology and Advanced Engineering, vol. 3, no. 6, pp.

212-214, 2013.

[4] A brief history of Steganography. [ONLINE] Available at:

http://www.lia.deis.unibo.it/Courses/RetiDiCalcolatori/Progetti98/Fortin

i/history.html. [Accessed 14 October 14].

[5] P. Thomas, “Literature survey on modern image steganographic

techniques”, International Journal of Engineering Research and

Technology, vol. 2, pp. 107-111, 2013.

[6] W. Bender, D. Gruhl, N. Morimoto and A. Lu, “Techniques for data

hiding”, IBM Systems Journal, vol. 35, pp. 313-336, 1996

http://dx.doi.org/10.1147/sj.353.0313.

[7] J. Tian, “Reversible data embedding using a difference expansion”,

IEEE Trans. on Circuits and Systems for Video Technology, vol. 13, pp.

890-896, 2013.

http://dx.doi.org/10.1109/TCSVT.2003.815962

[8] K. Hempstalk, “Hiding behind corners: Using edges in images for better

steganography”, Proc. of the Computing Women’s Congress, Hamilton,

New Zealand, pp. 11-19, 2006.

[9] K. M. Singh, L. S. Singh, A. B. Singh and K. S. Devi, “Hiding secret

message in edges of the image”, Proc. of IEEE Int. Conf. on

Information and Communication Technology, pp. 238-241, 2007.

[10] C. M. Wang, N. I. Wu, C. S. Tsai and M. S. Hwang, “A high quality

steganographic method with pixel value differencing and modulus

function”, Journal of Systems and Software, vol. 81, pp. 150-158, 2008

http://dx.doi.org/10.1016/j.jss.2007.01.049.

[11] L. Luo, Z. Chen, M. Chen, X. Zeng and Z. Xiong, “Reversible image

water marking using interpolation technique”, IEEE Trans. on

Information Forensics and Security, vol. 5, pp. 187-193, 2010

http://dx.doi.org/10.1109/TIFS.2009.2035975

.

[12] W. Luo, F. Huang and J. Huang, “Edge adaptive image steganography

based on LSB matching revisited”, IEEE Trans, on Information

Forensics and Security, vol. 5, pp. 201-214, 2010.


Zakir Khan has done his Bachelor of Computer Science from Hazara

University Mansehra, Pakistan in 2012 and is currently enrolled for his

Master in Computer Science at the Department of Information Technology

Hazara University, Pakistan. He is a skilled programmer and his research

interests include Image processing, Optical fiber communication, and

computer networks.

Mohsin Shah (on leave) is serving as Lecturer at the Department of

Information Technology Hazara University Mansehra, Pakistan. He is

currently pursuing his Ph.D. in Optics from School of Optoelectronics,

Beijing Institute of Technology, China. He completed his B.Sc.

Telecommunication Engineering from University of Engineering and

Technology Peshawar, Pakistan in 2007 and M.Sc. Telecommunication

Engineering from University of Engineering and Technology Taxila, Pakistan

in 2012. Recently he is admitted for his PhD in Optical Engineering at

Beijing Institute of Technology China. He has 2 years of diversified

experience in the field of cellular mobile communication systems. His

research interests include Optics and Photonics, Network Security and Image

Processing.

Muhammad Naeem is serving as Lecturer at the Department of

Information Technology Hazara University Mansehra, Pakistan. He got his

Ph.D. from University of Leicester, UK in 2012. His research interests

include Software Product Lines, Requirements Engineering, Steganography,

and Computational Mathematics. He is also interested in formal framework

like Linear Logic and Proportional Logic.

Danish Shahzad is serving as Research Assistant at Department of

Computer Engineering, Kadir Has University Istanbul, Turkey. He has done

his MS in Computer Science in 2014 from Hazara University Mansehra,

Pakistan. He completed his BS in Telecommunication in 2010 from Comsats

Institue of Information Technology. His research interests include Mobile

Adhoc Networks, Network security, Parallel programming and Hybrid

programming.

Toqeer Mahmood is currently serving as Programmer at University of

Engineering and Technology Taxila, Pakistan. He completed his MS

Computer Engineering in 2010 from Center for Advanced Studies in

Engineering (CASE) Islamabad, Pakistan. He is currently pursuing his Ph.D.

in Image Processing from University of Engineering and Technology Taxila,

Pakistan. His research interests include Image Processing, Computer vision,

Computer Networks and Numerical Techniques.



http://www.lia.deis.unibo.it/Courses/RetiDiCalcolatori/Progetti98/Fortini/history.html

http://www.lia.deis.unibo.it/Courses/RetiDiCalcolatori/Progetti98/Fortini/history.html

http://dx.doi.org/10.1147/sj.353.0313

http://dx.doi.org/10.1147/sj.353.0313

http://dx.doi.org/10.1147/sj.353.0313





http://dx.doi.org/10.1016/j.jss.2007.01.049













LSB Steganography using Bits Complementationiieng.org/images/proceedings_pdf/8221E1114029.pdfImage LSB steganography is the technique of embedding secret information bits in the least

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