Ijcatr04021016

International Journal of Computer Applications Technology and Research
Volume 4– Issue 2, 170 - 174, 2015, ISSN:- 2319–8656
www.ijcat.com 170
Adaptive Steganography Based on Logistic Map
Fariba Ghorbany Beram
Sama Technical and Vocational Training College
Islamic Azad University,
Masjedsoleyman Branch, Masjedsoleyman,
Iran
Sajjad Ghorbany Beram
Sama Technical and Vocational Training College
Islamic Azad University,
Masjedsoleyman Branch, Masjedsoleyman,
Iran
Abstract:
umerous novel algorithms have been proposed in the fields of steganography with the goals of increase security,capacity and
imperceptibility. In this paper, we introduced a new blind adaptive algorithm in image steganography technique to Improving that
goals. The existing methods hide the information using constant bit length in integer wavelet coefficients. This paper uses variable bit
length based on float wavelet coefficients to hide the data in a particular positions using secret key. The proposed method try to obtain
an optimal mapping function to reduce the difference error between original coefficients values and modified values. we provided with
the double security by using a secret key only known to both sender and receiver, therefore improving goals compared to the existing
algorithm.
Keywords: Steganography; Security; Logistic Map; capacity; image
1. INTRODUCTION
over the last decade, one of the most significant current
discussions in computer science is the field of information
security. In general, information security is the techniques,
policies and strategies used to protect and secure computer
systems, in maintaining the operations of an organization. One
of the concerns in information security is the concept of
information hiding. It is the process of embedding information
into digital content without causing perceptual degradation.
Steganography of current information hiding has shown that
steganography is one of the recent important subdisciplines.
This is because most of the proposed information hiding
system is designed based on steganography. Today,
steganography is most often associated with the high-tech
application where data are hidden with other information in an
electronic file[1]. Generally speaking, a good steganographic
technique should have good visual imperceptibility and a
sufficient capacity of hidden secret data[2]. Steganographic
methods can be classified into spatial domain embedding and
frequency domain embedding[3,4]. Almost all digital file
formats can be used for steganography, but the image and
audio files are more suitable because of their high degree of
redundancy [5].
2. RELATED WORKS
In this section we introduce some of the methods described
Steganography . For this purpose, two groups of methods in
the spatial domain and transform domain techniques will be
examined.In the spatial domain techniques, secret messages
are placed in a carrier media, without prior information
hiding, on a carrier medium, the conversion will be done.
Confidential data is actually placed directly on the carrier
media. One of the first Steganography techniques, using the
least significant bits of the carrier media. The use of this
technique for placement of confidential information on a
carrier media, not a tangible change in the media.
Steganography the images presented in lots of different
techniques, the goal of all of them is the availability of high
capacity, security, and resistance. These three criteria are in
conflict with each other and simultaneously achieve all three
simultaneously is very difficult and perhaps impossible. Three
objectives are stated at the three vertices of a triangle. The
matter requires attention to each other and not all of these
parameters simultaneously met in the best way.Wavelet
transform gives the best result for image transformation[6].
the frequency domain transform we applied in this research is
Haar-DWT, the simplest DWT. A 2-dimensional Haar-DWT
consists of two operations: Discrete Wavelet Transformation
has its own excellent space frequency localization property.
Applying DWT in 2D images corresponds to 2D filter image
processing in each dimension. The input image is divided into
4 non-overlapping multi-resolution sub-bands by the filters,
namely LL1 (Approximation coefficients), LH1 (vertical
details), HL1 (horizontal details) and HH1 (diagonal details).
The sub-band (LL1) is processed further to obtain the next
coarser scale of wavelet coefficients, until some final scale
“N” is reached. When “N” is reached, we’ll have 3N+1 sub-
bands consisting of the multi-resolution sub-bands (LLN) and
(LHX), (HLX) and (HHX) where “X” ranges from 1 until
“N”. Generally most of the Image energy is stored in these
sub-bands[7,8,9,10]. The least significant bit (LSB) insertion
method is the most common and easiest method for
embedding messages in an image in spatial domain but it has
some limitations such as it is easier to understand using
steganalysis[11,12]. Variable Embedding Ratio and LSB is
used in [13]. paper[14] proposes a method for image
steganography. The chosen Variable Embedding Ratio [VER]
is 4:2 that is 4 bits are embedded in edge pixels and 2 bits in
other pixels.In[15] uses variable bit length based on integer
wavelet coefficients to hide the data in a particular positions
using secret key by LSB substitution method. In smooth areas
they embed three bits of secret information. In the
complicated areas, variable rate bits are embedded[16].

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3. SECRET KEY
we use chaos theory to produce secret key. The name "chaos
theory" comes from the fact that the systems that the
theory describes are apparently disordered, but chaos
theory is really about finding the underlying order in
apparently random data. The nonlinear dynamics
researchers have observed an interesting relationship
between chaotic behavior and Random number generator
systems as many properties of the chaotic systems such
as their sensitivity to initial conditions can be considered
to the confusion in generation of secret keys.
Deterministic pseudorandom numbers are used for the
generation of secret key in cryptography system. The
logistic map is a very simple mathematical model often
used to describe the growth of biological populations. The
simple mathematical form of the logistic map is given as
[17]:
(1)
Xn+1=r.xn(1-xn)
xn is the state variable being in the interval [0, 1] and r
is system parameter which might have any value between 1
and 4. In this paper we have used the logistic function to
generate the secret key. If this function is quite Chaotic
behavior, you would have x0=0.3 amnd 3.57<r<4.
4. PROPOSED METHOD
wavelet transform is applied to the cover image to get the
wavelet coefficients. the wavelet coefficients is splitted into
RGB planes .The obtained wavelet coefficients from the RGB
planes, select one or two or three planes according to the
secret key and Each selected plane is decomposed into m*m
blocks according to the secret key.Range, the number of bits
that can be replaced, between 1 to logarithm biggest
coefficient value. According to the value of coefficients, the
number of bits replaced The secret message is determined. It
makes Optimal Use of the Wavelet Coefficients. While fewer
number of coefficients are modified, More bits can be
replaced. After replacement, inverse wavelet transform
applied to restore the image (Fig 1).
4.1 Embedding procedure:
cover image is splitted into R, G, B planes .
Each RGB is converted into frequency domain by using Haar
wavelet transform.
Select RGB plans based on secret key .
selected RGB plane is decomposed into blocks based on
secret key .
Value of wavelet coefficient are classified
{D=2n-1
–2n
-1,n =log (coefficient)}D is range value of
wavelet coefficient, n-1 is number of secret data bits to be
embedded and coefficient is value of wavelet coefficient
,dec(n) is the decimal value of secret data bits.
If 2n-1
< coefficient <2n
-1 then
(2n
–dec(n bit of secret data))/2
1) Determine the inverse wavelet
transform(idwt) on each RGB planes to restore the
image.
4.2 Extraction procedure:
1) stego image is splitted into RGB planes .
2) Select RGB plans based on secret key .
3) Each RGB is converted into frequency
domain by using Haar wavelet transform.
4) Each RGB plane selected is decomposed
into blocks.
5) Select blocks based on secret key.
6) Value of wavelet coefficient are
classified :
If 2n-1
< coefficient <2n
-1 then
x=( 2n
– coefficient)*2
message[]=dec2bin(x)
coefficient is value of wavelet coefficient stego image and
message is data extraction from stego image .
If coefficient=18 and secret message is 111011 then
16<23<32 ,n=5, number of bits is 4.select 4 bits of
secret message(1110) .dec(1110) is 14.
32-14/2=25
We put 25 instead of 23 in the picture .
For extract,if stego coefficient is 25 then
16<25<32
Secret mesage=dec2bin((32-25)*2)= dec2bin(14)=1110
So we had to replace bits that just were extracted.

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Fig 1. Proposed Block Diagram Data Embedding
5. RESULTS AND DISCUSSION
My proposed algorithm was implemented in MATLAB and
are tested with many colored images. In this paper We
selected 512x512 “Lena” jpeg image to perform our testing.
Fig 2 has been shown cover image, Fig 3 has been shown
stego image. The performance of various steganographic
methods can be rated by the three parameters:
security,capacity and imperceptibility. The steganographic
methods proposed in this paper are very secure as variable
number of bits are hidden in different coefficient of wavelet.
This method embeds secret information in a random order
using a secret key only known to both sender and receiver So
it is very difficult to find out the hidden data from the stego
image. The same stego image can also bear different secret
image for different receiver depending on their secret key.
Capacity means the amount of message that can be embedded.
Table I, show Average PSNR values and Embedding Rate
achieved using standard images.
Table I. Average PSNR values achieved using standard
images
Embedding
Rate
0.01 0.02 0.05 0.20 0.25 0.35
PSNR 64 61 55 49 48 46
Fig 2.Cover Image Stego image
Fig3 indicative of the blue before placing a secret message in
an image and then paste the information is confidential,
tangible change in the color chart, as we will be created.
Fig 3. blue before and after placement
In the proposed algorithm [18] The image edges are detected
by using the cany operator. In this way, four bits per pixel that
are on the edge, and two bits in the others pixels are hidden
hidden. Since a variable number of bits of the secret message
in different parts of the image data, the method is safe, Because
if that person is suspected of carrying forward the media until
the algorithm is not available, the least significant bits of the
secret message can not be retrieved. The disadvantage of the
method is to extract the secret message from the carrier at
destination media, the media must also be present. We have the
advantage of variable bit rate method [18] in the proposed
algorithm, we use the other hand to eliminate the disadvantage
of my method.
Compare the signal to noise of the proposed method and
algorithm [18] shown in Fig 4. As we observe the same
replacement rate, the proposed algorithm provides better
results.
Fig4. compares the psnr in the replacement rate of 262,144
If Steganography image H1 and original image H ,HWIDTH,
HLEN number of rows and columns of the image are
calculated PSNR and MSE of the Equation (2) , Equation (3) is
calculated.
(2)
MSE=

www.ijcat.com 173
(3)
PSNR=10*
The mean square error (MSE) is reduced, the image
quality was high. Low value, the maximum amount of signal to
noise ratio (PSNR) of the carrier image is of poor quality
6. CONCLUSION
In this paper we proposed a data hiding scheme that hides data
into the float wavelet coefficients of an image. The system
combines a float wavelet transform and the variable rate of
embedding to maximize performance of steganographic
method proposed. Because of the the chaos system is used, the
proposed method is secure.
entirely By using this method the data hiding capacity is
improved and secrecy of the embedded data bits can be
provided. It is also seen that the stego image formed is of
good quality. Future work may be carried out to increase the
capacity and enhance the visual quality of the stego image by
improving the PSNR value . The methods proposed in this
paper are:
 very secure
 capacity is good.
 PSNR obtained is approximately maximum
compared to the existing algorithm which confirm
imperceptibility of the host and the stego image.
 The proposed system also reduces the difference
between original coefficients values and modified
values by using the adaptive float coefficient
adjustment.
 Blind steganogeraphy method
7. REFERENCES
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www.ijcat.com 174
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