P a g e | 4 Vol. 10 Issue 1 (Ver 1.0), April 2010
Global Journal of Computer Science and Technology
GJCST Computing Classification
F.2.1 & G.2.m
An Analysis of LSB & DCT based Steganography
Dr. Ekta Walia
a
, Payal Jain
b
,
Navdeep
c
Abstract- This paper presents analysis of Least Significant Bit
(LSB) based Steganography and Discrete Cosine Transform
(DCT) based Steganography. LSB based Steganography embed
the text message in least significant bits of digital picture. Least
significant bit (LSB) insertion is a common, simple approach to
embedding information in a cover file. Unfortunately, it is
vulnerable to even a small image manipulation. Converting an
image from a format like GIF or BMP, which reconstructs the
original message exactly (lossless compression) to a JPEG,
which does not (lossy compression), and then back could
destroy the information hidden in the LSBs. DCT based
Steganography embed the text message in least significant bits
of the Discrete Cosine (DC) coefficient of digital picture. When
information is hidden inside video, the program hiding the
information usually performs the DCT. DCT works by slightly
changing each of the images in the video, only to the extent that
is not noticeable by the human eye. An implementation of both
these methods and their performance analysis has been done in
this paper.
Keywords- Least Significant Bit (LSB), Discrete Cosine
Transform (DCT), Steganography
I INTRODUCTION
teganography comes from the Greek words Steganós
(Covered) and Graptos (Writing). Steganography in
these days refers to information or a file that has been
concealed inside a digital picture, video or audio file. If a
person or persons view the object that the information is
hidden inside, he or she will have no idea that there is any
hidden information; therefore the person will not attempt to
decrypt the information.
a
Professor, Department of Information and Technology,
Maharishi Markandeshwar College of Engineering
Maharishi Markandeshwar University, Mullana,
Ambala(Haryana)
E-mail: wekta@yahoo.com, Tel No: 91-9416551292
a
b
Lecturer, Department of Information and Technology,
Maharishi Markandeshwar College of Engineering
Maharishi Markandeshwar University, Mullana, Ambala
(Haryana)
payaljain2006@gmail.com
b
, Tel No: 91-9466742552
b
c
Student, Department of Information and Technology,
Maharishi Markandeshwar College of Engineering
Maharishi Markandeshwar University, Mullana,
Ambala(Haryana)
A. Steganographic Techniques
i. Physical Steganography
Physical Steganography has been widely used. In ancient
time people wrote message on wood and then covered it
with wax. Message was written on the back of postage
stamps. Message was written on paper by secret inks.
ii. Digital Steganography
Digital Steganography is the art of invisibly hiding data
within data. It conceals the fact that message exists by
hiding the actual message. In this, secret data can be hidden
inside the image, text, sound clip which can be represented
in binary.
iii. Printed Steganography
Digital Steganography output can be in the form of printed
documents. The letter size, spacing and other characteristics
of a cover text can be manipulated to carry the hidden
message. A recipient who knows the technique used can
recover the message and then decrypt it.
II METHODS OF CONCEALING DATA
IN DIGITAL IMAGE
A. Least Significant Bit (Lsb)
LSB is the lowest bit in a series of numbers in binary. e.g. in
the binary number: 10110001, the least significant bit is far
right 1.
The LSB based Steganography is one of the steganographic
methods, used to embed the secret data in to the least
significant bits of the pixel values in a cover image. e.g. 240
can be hidden in the first eight bytes of three pixels in a 24
bit image.
PIXELS: (00100111 11101001 11001000)
(00100111 11001000 11101001)
(11001000 00100111 11101001)
240 : 011110000
RESULT: (00100110 11101001 11001001)
(00100111 11001001 11101000)
(11001000 00100110 11101000)
Here number 240 is embedded into first eight bytes of the
grid and only 6 bits are changed.
S
Global Journal of Computer Science and Technology
Vol. 10 Issue 1 (Ver 1.0), April 2010 P a g e | 5
B. Discrete Cosine Transform (Dct)
DCT coefficients are used for JPEG compression. It
separates the image into parts of differing importance. It
transforms a signal or image from the spatial domain to the
frequency domain. It can separate the image into high,
middle and low frequency components.
Fig. I Discrete Cosine Transform of An Image
The general equation for a 1D (N data items) DCT is
defined by the following equation:
1
0
2
)12(
cos)(
N
x
N
ux
xfα(u)C(u)
(1)
for u = 0, 1, 2, . . . , N-1.
The general equation for a 2D (N by M image) DCT is
defined by the following equation:
N
vy
N
ux
yxfvuvuC
N
x
N
y
2
)12(
cos
2
)12(
cos),()()(),(
1
0
1
0
(2)
for u,v = 0, 1, 2, . . . , N-1
Here, the input image is of size N X M. c(i, j) is the intensity
of the pixel in row i and column j; C(u,v) is the DCT
coefficient in row u and column v of the DCT matrix.
Signal energy lies at low frequency in image; it appears in
the upper left corner of the DCT. Compression can be
achieved since the lower right values represent higher
frequencies, and generally small enough to be neglected
with little visible distortion.
DCT is used in steganography as-
Image is broken into 8×8 blocks of pixels.
Working from left to right, top to bottom, the DCT
is applied to each block.
Each block is compressed through quantization
table to scale the DCT coefficients and message is
embedded in DCT coefficients.
III LITERATURE SURVEY
A lot of Research has been carried out on Steganography
because it is important to know how much data can be
concealed without image distortion. Their description is as
follows:
Ken Cabeen and Peter Gent [1] have discussed the
mathematical equations of Discrete Cosine Transform
(DCT) and its uses in image compression. Andrew B.
Watson [2] has discussed Discrete Cosine Transform (DCT)
technique for converting a signal into elementary frequency
component. He developed simple function to compute DCT
and show how it is used for image compression. Jessica
Fridrich et. al [3] have discussed a reliable and accurate
method for detecting least significant bit (LSB) non
sequential embedding in digital images. The secret message
length is derived by inspecting the lossless capacity in the
LSB and shifted LSB plane. Mohesen Ashourian, R.C. Jain
and Yo-Sung Ho [4] have proposed a data hiding scheme to
embed a signature image in the host image. They selected a
gray scale host image of 512×512 pixels and signature
image of 256×256 pixels. They developed image data hiding
scheme on dithered quantization and a modified baseline
JPEG coding scheme. A test of system performance has
been done by JPEG compression, addition of Gaussian
noise, and Gaussian and Median filtering of host image.
J.R.Krenn [5] has proposed a method to embed message in
LSB of DC coefficients of cover image. He proposed a
simple pseudo-code algorithm to hide a message inside a
JPEG image. Ren-Junn Hwang et. al[6] have proposed data
hiding based on JPEG technique. They proposed a method
of compressing the stego image by lossy compression
method to reduce the image size. The receiver then extracts
complete data correctly from lossy compressed image.
H. W. Tseng and C. C. Chang [7] have proposed a novel
high capacity data hiding method based on JPEG. They
proposed a method that employs a capacity table to estimate
the number of bits that can be hidden in each DCT
component so that significant distortions in the Stego-image
can be avoided. Youngran Park et. al [8] have proposed a
new image steganography method to verify whether the
secret information had been deleted, forged or changed by
attackers. They proposed a method that hides the secret
information into special domain of digital image. Neeta
Deshpande et. al [9] have embedded data in least significant
bits of cover image. They explained the LSB embedding
technique and presented the evaluation results. Aneesh Jain
and Indranil Sengupta [10] have proposed a scheme, which
hides data in bitmap images, in a way that there is almost no
perceptible difference between the original image and new
image, and this is also resistant to JPEG compression. M.
Chaumont and W. Puech [11] have proposed a method to
hide the color information in a compressed grey-level
image, allow free access to the compressed gray level
image, and give color image access only if you own a secret
key. KokSheik Wong, Xiaojun Qi, and Kiyoshi Tanaka [12]
have proposed Mod4 steganography method in discrete
cosine transform (DCT) domain. Mod4 is capable of
embedding information into both uncompressed and JPEG
compressed image. Takayuki Ishida et. al [13] have
discussed a modified QIM-JPEG2000 steganography which
improve the previous JPEG2000 steganography using
quantization index modulation (QIM).
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Global Journal of Computer Science and Technology
IV ALGORITHMS OF STEGANOGRAPHY
A. Lsb Based Steganography
Algorithm to embed text message:-
Step 1: Read the cover image and text message which is to
be hidden in the cover image.
Step 2: Convert text message in binary.
Step 3: Calculate LSB of each pixels of cover image.
Step 4: Replace LSB of cover image with each bit of secret
message one by one.
Step 5: Write stego image
Algorithm to retrieve text message:-
Step 1: Read the stego image.
Step 2: Calculate LSB of each pixels of stego image.
Step 3: Retrieve bits and convert each 8 bit into character.
B. DCT Based Steganography
Algorithm to embed text message:-
Step 1: Read cover image.
Step 2: Read secret message and convert it in binary.
Step 3: The cover image is broken into 8×8 block of pixels.
Step 4: Working from left to right, top to bottom subtract
128 in each block of pixels.
Step 5: DCT is applied to each block.
Step 6: Each block is compressed through quantization
table.
Step 7: Calculate LSB of each DC coefficient and replace
with each bit of secret message.
Step 8: Write stego image.
Algorithm to retrieve text message:-
Step 1: Read stego image.
Step 2: Stego image is broken into 8×8 block of pixels.
Step 3: Working from left to right, top to bottom subtract
128 in each block of pixels.
Step 4: DCT is applied to each block.
Step 5: Each block is compressed through quantization
table.
Step 6: Calculate LSB of each DC coefficient.
Step 7: Retrieve and convert each 8 bit into character.
V PERFORMANCE & RESULTS
Comparative analysis of LSB based and DCT based
steganography has been done on basis of parameters like
PSNR. Both grayscale and colored images have been used
for experiments. Peak signal to noise ratio is used to
compute how well the methods perform.
PSNR computes the peak signal to noise ratio, in decibels,
between two images. This ratio is used as a quality
measurement between two images. If PSNR ratio is high
then images are best of quality.
2
2
10
))max(),(max(max(log10
),(
yx
yx
yxPSNR
A. LSB Based Steganography
Fig. II Original Cameraman.bmp Fig III Stego cameraman.bmp
PSNR between Fig II and Fig III = 51.0870 dB
Fig. IV Original cell.bmp Fig. V Stego cell.bmp
PSNR between Fig. IV and Fig. V = 49.7214 dB
Fig. VI Original circuit.bmp Fig . VII Stego circuit.bmp
PSNR between Fig. VI and Fig.VII = 48.3476 dB
i. Using Color Images
Fig .VIII Original army.bmp Fig. IX Stego army.bmp
PSNR between Fig VIII and Fig IX =51.0872 dB
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Fig .X Original lasercolor.bmp Fig .XI Stego lasercolor.bmp
PSNR between Fig X and Fig XI = 51.0881 dB
Fig. XII Original kufte.bmp Fig. XIII Stego kufte.bmp
PSNR between Fig XII and Fig XIII = 51.0451 dB
B. DCT Based Steganography
i. Using Grayscale Images
Fig. XIV Original cameraman.bmp Fig. XV Stego cameraman.bmp
PSNR between Fig XIII and Fig. XIV = 55.3865 dB
Fig. XVI Original coins.bmp Fig. XVII Stego coins.bmp
PSNR between Fig. XVI and Fig. XVII = 55.3049 dB
ii. Using Color Images
Fig. XVIII Original army.bmp Fig. XIX Stego army.bmp
PSNR between Fig. XVIII and Fig. XIX = 57.2172 dB
Fig. XX Original ilexvert.bmp Fig. XXI Stego ilexvert.bmp
PSNR between Fig. XX and Fig. XXI = 57.0530 dB
VI CONCLUSION
LSB based steganography embed the text message in LSB
of cover image. DCT based steganography embed the text
message in LSB of DC coefficients. This paper implements
LSB based steganography, DCT based steganography and
computes PSNR ratio. PSNR is the peak signal to noise
ratio, in decibels, between two images. This ratio is used as
a quality measurement between two images. If PSNR ratio
is high then images are better of quality. Comparison of
LSB based and DCT based stego images using PSNR ratio
shows that PSNR ratio of DCT based steganography scheme
is high as compared to LSB based steganography scheme for
all types of images- (Grayscale as well as Color). DCT
based steganography scheme works perfectly with minimal
distortion of the image quality as compared to LSB based
steganography scheme. Even though the amount of secret
data that can be hidden using this technique is very small as
compared to LSB based steganography scheme still, DCT
based steganography scheme is recommended because of
the minimum distortion of image quality.
VII REFERENCES
1) Ken Cabeen and Peter Gent, ―Image Compression
and Discrete Cosine Transform‖, College of
Redwoods.
http://online.redwoods.cc.ca.us/instruct/darnold/L
APROJ/Fall98/PKen/dct.pdf
P a g e | 8 Vol. 10 Issue 1 (Ver 1.0), April 2010
Global Journal of Computer Science and Technology
2) Andrew B. Watson, ―Image Compression Using
the Discrete Cosine Transform‖, NASA Ames
Research Center , Mathematica Journal, 4(1), p.81-
88,1994
3) Jessica Fridrich, Miroslav Goljan, and Rui Du,
―Detecting LSB Steganography in Color and Gray-
Scale Images‖, Magazine of IEEE Multimedia,
Special Issue on Multimedia and Security, pp.22-
28, October-December 2001.
4) Mohesen Ashourian, R.C. Jain and Yo-Sung Ho,
“Dithered Quantization for Image Data Hiding in
the DCT domain”, in proceeding of IST2003,
pp.171-175, 16-18 August, 2003 Isfahan Iran.
5) J.R.Krenn, ―Steganography and Steganalysis‖,
January 2004.
6) Ren-Junn Hwang, Timothy K. Shih, Chuan-Ho
Kao, “A Lossy Compression Tolerant Data Hiding
Method Based on JPEG and VQ.” Journal of
Internet Technology Volume 5(2004).
7) Hsien – Wen Tseng and Chin – Chen Chang, ‖
High Capacity Data Hiding in JPEG Compressed
Images‖, Informatica, Volume 15 , Issue 1
(January 2004) 127-142, 2004,0868-4952
8) Youngran Park, Hyunho Kang, Kazuhiko
Yamaguchi and Kingo Kobayashi, ―Integrity
Verification of Secret Information in Image
Steganography‖, Symposium on Information
Theory and its Applications, Hakodate, Hokkaido,
Japan, 2006.
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―Implementation of LSB Steganography and Its
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on. 06/01/2007;
DOI: 10.1109/ICDIM.2007.369349
10) Aneesh Jain, Indranil Sen Gupta, ―A JPEG
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Raster Graphics Images‖, TENCON 2007 - 2007
IEEE Region 10 Conference, vol.2
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