International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 9- Sep 2013
Implementation of Digital Image Steganography
Using ADSP BF532 Processor
P.D.Gadekar1, S.K.Waghmare2
1
2
G.H.R.C.O.E.M Chas, Ahmednagar, India
Department of Electronics Engineering, GHRCEM, Wagholi, Pune, India
Abstract— Hide the information means inserts secret data into a
cover file, so that the existence of data is not visible. Today
information hiding techniques used are watermarking and
Steganography. In watermarking we have to protect the
ownership of a digital content. In steganography secret messages
embed into digital content so the secret messages are not
detectable. In Steganography some techniques are irreversible,
means original image cannot be recovered from stego image. An
original image can be completely recovered from the stegoimage after extraction of secret data is known as reversible
Steganography. This technique has been focused on spatial
uncompressed domain which include Least Significant Bit
algorithm (LSB). In this, we propose a lossless, compressed
domain Steganography technique for compressed images based
on the Discrete Wavelet Transform (DWT). The stego-image
preserves the same image quality as the original compressed
images. The experimental results show that the proposed method
is not only easy to implement but also gives high payload
(capacity) in the cover image with very little error.
cover image with the secret message bits.LSB is the most
preferred technique used for data hiding because it is simple to
implement offers high hiding capacity, and provides a very
easy way to control stego-image quality [2]
The other type of hiding method is the transform domain
techniques which appeared to overcome the robustness and
imperceptibility problems found in the LSB substitution
techniques. There are many transforms that can be used in
data hiding, the most widely used transforms are; the discrete
cosine transform (DCT) the discrete wavelet transform (DWT)
and the discrete Fourier transform (DFT). Most recent
researches are directed to the use of DWT since it is used in
the new image compression format JPEG2000 and MPEG4,
In [9] the secret message is embedded into the high frequency
coefficients of the wavelet transform while leaving the low
frequency coefficients sub band unaltered.
Keywords— Steganography, discrete wavelet transform, spatial
domain, Least Significant Bit (LSB) algorithm.
II. DISCRETE WAVELATE TRANSFORM(DWT)
It stores the information in an image and removes noise
efficiently. By using multi-resolution technique different
frequencies are analysed with different resolutions. Wavelets
are localized waves which have their energy concentrated in
time or space for analysis of transient signals. A fast
computation of wavelet transforms which is easy to
implement and reduces the computation time and resources
required is DWT which based on sub-band coding. [6]
A. Wavelet Family
There are different basic functions we can use as the mother
wavelet for wavelet transformation. Through translation and
scaling the mother wavelet produces all wavelet functions
used in the transformation, so it determines the characteristics
of the resulting wavelet transform. Therefore, based on the
particular application the appropriate mother wavelet should
be chosen. Among different types of wavelet families such as
Haar, Daubechies4, Coiflet1, Symlet2, Meyer, Morlet,
Mexican Hat etc we use Haar Wavelet transform
B. Haar Wavelet Transform
In proposed system we are using Haar Wavelet type
because it is the simple among all. In this the low frequency
wavelet coefficients are generated by averaging the two pixel
values. High frequency coefficients are generated by taking
I. INTRODUCTION
Steganography is defined as the art and science of hiding
secret information (data) in any digital file (plain sight) within
a cover data so that the information can be securely
transmitted over a network. Steganography word is originated
from two Greek words steganos and graphia means "covered
writing". From ancient times romans and ancient Egyptians
used Steganography. We can use any digital file such as
image, video, audio, text or IP packets to hide secret message.
Generally the file used to hide data is referred to as cover
object, and the term stego-object is used for the file containing
secret message.
Image files are the most popular cover objects due to the
redundancy of digital information, representation of an image
data they are easy to find and have higher degree of distortion
tolerance with high hiding capacity. According to the format
of the cover image or the method of hiding, we have two
popular types of hiding methods; spatial domain embedding
and transform domain embedding.
The Least Significant Bit (LSB) substitution is an example
of spatial domain techniques. The basic idea in LSB is the
direct replacement of LSBs of noisy or unused bits of the
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 9- Sep 2013
half of the difference of the same two pixels. The four bands
obtained are LL, LH, HL, and HH which is shown in Fig 1.
The LL band is known as approximation band, consists of low
frequency wavelet coefficients, which consist of significant
part of the spatial domain image. The other bands are called as
detail bands which consist of high frequency coefficients and
contain the edge details of the spatial domain image.
Step1: Column wise processing to get H and L
H = (Co¯ Ce)
L = (Ce-[H /2])
Where Co: odd column , Ce: even column pixel values.
Step 2: Row wise processing to get LL, LH, HL and HH,
Separate odd and even rows of H and L,
Namely, Hodd– odd row of H
Lodd– odd row of L
Heven – even row of H
Leven – even row of L
LH = Lodd – Leven
LL = Leven– LH/2
HL = Hodd– Heven
HH = Heven -HL/2
IV. PROPOSED SYSTEM
Figure 2 shows the hardware interface of our system. We
are using BF532 processor. There are two parts of the process
first part is embedding and other is extraction.
(1)
(2)
(3)
(4)
(5)
(6)
III. LEAST SIGNIFICANT BIT (LSB) ALGORITHM
The LSB is an example of spatial domain techniques. In
LSB we directly replace LSBs of noisy or unused bits of the
cover image with the secret message bits. It is the most
preferred technique used to hide the data because it is simple
to implement and have high hiding capacity. Also easily
control stego-image quality. [2] But it has low robustness to
modifications made to the stego-image such as low pass
filtering and compression [3] and also low imperceptibility.
Algorithms using LSB in grayscale images can be found in [4,
5, 6].
To overcome the robustness and imperceptibility found in
the LSB substitution techniques the transform domain
technique is used. Different types of transforms that can be
used in data hiding, among which mostly used transforms are;
the discrete cosine transform (DCT), the discrete wavelet
transform (DWT) and the discrete Fourier transform (DFT).
The DWT is mostly preferred because it is used in the new
image compression format JPEG2000 and MPEG4. In [9] the
secret message is embedded into the high frequency
coefficients of the wavelet transform while leaving the low
frequency coefficients sub band unaltered.
In this paper, an image file with “.jpg” extension has been
selected as host file. It is assumed that the least Significant
bits of that file should be modified without degrading the
image quality.
There are 3 types of this algorithm:
ISSN: 2231-5381
1) 2 pixel per character
2) 4 pixel per character
3) 8 pixel per character
As move on from first type to last type the data hiding
capacity decreases but quality of image increases.
Figure 2. The Hardware interface diagram of proposed
system
A. Embedding
From personal computer we are getting input image as
cover image then giving that to processor It process that
image. In that firstly it compress the image with the help
of Discrete Wavelet transform (DWT).Then in that
compressed image embed the secret data which we have to
passed to get stego image. This embedding process is done
with the help of Least Significant Bit (LSB) algorithm.
Then apply Inverse Discrete Wavelet Transform (IDWT)
to get Stego image.
B. Extraction
After getting stego image at transmitter, at receiver exactly
reverse process is done. On stego image apply DWT then
extract secret data. Finally we get our original image.
V. FLOW DIAGRAM
Embedding:
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 9- Sep 2013
(8)
Where R is the maximum fluctuation in the input image data
type.
As we know ideally MSE value should be small and
PSNR value should be very high.
Extraction:
Figure 3. The flow diagram of proposed system
Step 1. First we take input image as cover image in .jpg
format.
Step 2. Then apply discrete wavelet transform mechanism to
compress the image.
Step 3. With the help of LSB algorithm, embed the secret
data and compress image. After embedding apply IDWT to
get stego image at transmitter side.
Step 4.Exactly reverse process is done at the receiver side.
On stego image apply DWT.
Step 5.Then we are extracting secret data from stego image.
Step 6.In validations we are calculate the MSE and PSNR
values.
VI. VALIDATIONS
In validation we are taking some snapshots of our
project demo also calculating Mean Square Error (MSE) and
Peak Signal to Noise Ratio (PSNR) values.
In stego image a performance is measured by means of
two parameters namely, Mean Square Error (MSE) and Peak
Signal to Noise Ratio (PSNR)
The MSE is calculated by using the equation,
Figure 4. Snapshot of Lena Image
Figure 5.Snapshot of Flower Image
(7)
Where M : number of rows and N : number of columns in the
input image.
The PSNR calculated using the following equation:
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 9- Sep 2013
ACKNOWLEDGEMENT
We take this opportunity to gratefully acknowledge the
inspiration, encouragement, guidance, help and valuable
suggestions received from all our well-wishers. We would like
to thank our Project guide Prof. S. K. WAGHMARE who
have helped us and made available much useful information to
complete this project report.
We also thankful to Prof. S. P. BHUMKAR who has
given valuable support. Without their complete support and
willing co-operation, this would not have been possible. We
are forever obliged to our parents and friends for their
encouragement to us and faith in our ability to succeed.
REFERENCES
Figure 6.Snapshot of Rose
Table shows MSE and PSNR values for different images.
Table1.Some Experimental Results
Sr.No.
Image
MSE
1
Lena
0.0938
2
Flower
0.0859
3
Rose
0.0830
PSNR
58.411
58.7890
58.9396
VII.
CONCLUSION
This technique has two main objectives firstly that
Steganography should provide the maximum hiding
capacity and the embedded data must be imperceptible to
the observer. It was found that both objectives are fulfilled
by proposed method which gives high capacity in the
cover image with very little error.
ISSN: 2231-5381
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