Research on Image Steganography for getting more efficient results on Stego image using Adaptive Algorithm & LSB Technique.

1. INTERNATIONAL GLOBAL JOURNAL FOR ENGINEERING RESEARCH, VOL. 10, NO. 2, Nov 2014
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Image Steganography with Encryption
Using Adaptive Algorithm
1
Vaishali Kataria, 2
Arihant Khichha, 3
Vinod Todwal
1
M.Tech Scholar,RCEW, Jaipur, India
2, 3
Assistant professor, RCEW, Jaipur, India
1
friend.vishu29@gmail.com
Abstract— The basic structure of Steganography is made up of three components: cover image, message, and the key. The
carrier can be a digital image; it is the object that will carry the hidden message. A key is used to decode/decipher/discover
the hidden message. Steganography, meaning hidden writing, is the technique of concealing secret information into
another cover-media, such as audio, video, image and text, in such a manner that the very existence of the information is
camouflaged. Unlike the goal of cryptography, which prevents the data from illicit access and modification by transforming
the secret data into messy and meaningless bit-streams, the aim of steganography is to conceal their very existence such
that the embedded data is in audible or invisible to the grabbers. Using cryptographic techniques, the transformed secret
bit-streams may draw the eavesdroppers’ attention, while using steganographic techniques, the interceptors may not
notice the existence of the hidden data.
Keywords— Steganography, cryptographic, LSB substitution, canny edge detection, Blowfish encryption.
I. INTRODUCTION
In the proposed rectified steganographic technique, the side
information of neighboring pixels is used to estimate the
number of bits which can be carried in the pixel of the cover-
image. The pixel value is then replaced by the new value
which includes the secret data. More secret bits are embedded
in the edge area than those in the smooth area as the change in
smooth area is more easily discovered by human eyes. Besides,
the embedded secret data can be extracted completely from
the resulting stego-image without any errors.
LSB substitution is proposed to reduce the difference value
so that the distortions occurred in the smooth areas can be
lessened. In the first proposed technique, two neighboring
pixels are used to determine the number of bits to be
embedded into the pixel. The pixels in the edge areas can
embed more bits than those in the smooth areas. Using the
simple LSB substitution method with an Optimal Pixel
Adjustment Process (OPAP), the proposed technique provides
a relatively high hiding capacity and relatively good quality
stego-image [1]. The secret data, embedded into the cover-
image using the above rectified steganographic technique or
data hiding technique, can be in any type of format. However,
when the embedded data is a digital image, a data hiding
technique is proposed to enhance the image quality of the
resulting stego-image. In the second proposed technique, the
secret-image is embedded into the cover-image by exploring
the correlation between the cover-image block and the secret-
image block. The best match image block of the secret-image
block is selected based on the block difference from both of
the cover-image and the secret-image. Using this data hiding
technique, the image quality of the resulting stego-image can
be greatly improved when embedding a smooth secret-image
into a noisy cover-image.
There are three fundamental requirements for
steganography: robustness, imperceptibility and embedding
capacity. The robustness is the immune degree of the

2. Vaishali Kataria et al.: Image Steganography with Encryption Using Adaptive Algorithm
28
embedded secret-data to the cover-object manipulations, such
as filtering, re-sampling, cropping and lossy compression, etc.;
the imperceptibility means that the presence of the secret-data
is not easily noticeable by the observers; and the embedding
capacity is the amount of secret-data can be embedded into the
cover object. Nevertheless, it is impossible to obtain the
highest degree of robustness and the maximum embedding
capacity with the acceptable level of imperceptibility at the
same time. Therefore, a compromise must be made between
robustness, imperceptibility and embedding capacity. For
different applications, the acceptable balance between these
three constraints is different. For example, robustness is the
main concern for the digital watermarking techniques while
high embedding capacity is not necessary [2]. Conversely, the
primary requirements for the data hiding techniques are
imperceptibility and embedding capacity while high level of
robustness is not required.
This paper work is focusing on the data hiding techniques,
where the digital images are used as the cover-object. Hence,
imperceptibility and embedding capacity will be emphasized
and used to evaluate the performance of the proposed data
hiding techniques.
II. EARLIER WORK
Weiqi Luo et al., [3] proposed a method which embeds the
secret message into sharper edge regions of cover image
adaptively according to size of the message and the gradients
of the content edges of cover image. Gyankamal and Shinde
[4] developed a method of embedding the encrypted secret
message into the black and white cover picture images. This
method aims to utilize the cover image as much as possible.
The encrypted secret message bits are compared with the
blocks of cover image and the maximum matching block is
selected for embedding the secret information. Cheng-hsing
yang et al., [5] proposed a technique to embed the secret
information by Pixel Value Differing (PVD) method. The
number of secret bits embedded depends on the difference
between two consecutive pixels. Vijay kumar and Dines!)
kumar [6] has presented a performance evaluation of image
steganography using DWT applied on cover image and
payload to derive four sub bands such as Approximation
Coefficients (CA) Vertical Detail Coefficients (CV)
Horizontal Detail Coefficients (CH) Diagonal Detail
Coefficients (CD). The error blocks are calculated by
subtracting the approximation coefficients of cover image
from approximation coefficients of secret image. These blocks
are replaced with the best matched CH blocks. They made use
of CV and CD blocks also to embed the secret images. Aos. et
al., [7] implemented a new means of hiding the secret
information in the Executable (.EXE) file, such that it is
unrevealed to any anti-virus software, since anti-virus
software secretly read the furtive data embedded inside the
cover file. Nan-I Wu and Min-Shiang Hwang [8] developed
steganographic techniques for gray scale images and
introduced schemes such as high hiding capacity schemes and
high stego-image degradation imperceptibility schemes. These
schemes provide high imperceptibility and data hiding
capabilities. Bo-Luen Lai and Long-Wen Chang [9] proposed
a transform domain based adaptive data hiding method using
haar discrete wavelet transform. The image was divided into
sub-bands (LLI, HL1, LH I and HH I) and most of the data is
hidden in the edge region as it is insensitive to the human eye.
If these sub-bands were complex, then further division of the
bands were performed, so that more number of data bits could
be embedded.
III.PROPOSED METHODOLOGY
Text For confidentiality of private communication we
proposed a method with combination of encryption with
steganography. For ensuring the security, the plain text is
converted into cipher text and the process is called encryption.
Encrypted data is more difficult to differentiate from naturally
occurring phenomena than plain text is in the carrier image.
There are several algorithms by which we can encrypt data
before hiding it in the chosen medium. The combination of
encryption and steganography methods can be used to produce
better protection of the message. In case, when the
steganography fails and the message can be detected, it is still
of no use as it is encrypted using some cryptography method.

3. 29 INTERNATIONAL GLOBAL JOURNAL FOR ENGINEERING RESEARCH, VOL. 10, NO. 2, NOV 2014
There are two major techniques implemented in this
project. The first technique is the encryption algorithm that is
being used for converting the plain text (secret message) into
the cipher text. The second technique is the steganographic
algorithm that is being used for embedding the text into the
image. So we have two algorithms, namely, Blowfish
Algorithm for encryption and adaptive LSB technique for
image steganography.
Fig. 1: Steganography message embedding process
In this proposed work, a combination of cryptography
and steganography has been used to enhance embedding
capacity of a steganographic channel by pre-processing the
secret data and applying encryption technique over it to make
it more robust against Steganalysis. As for encrypting the
message a strongest and fastest technique Blowfish is used. In
figure 1 shows message embedding process after encryption
of the plain text (Text Message) into cipher text. In figure 2
shows a message extraction process in which adaptive LSB is
used for cipher text extraction and then the decryption of
cipher text using blowfish algorithm.
Fig. 2: Steganography message extraction process
After conversion of the plain text into cipher text using
Blowfish algorithm as the architecture of the embedding
process shown in the figure1, each blocks of the cipher text
embedded into different cover image. According to the length
of the one dimensional binary value of cipher text number of
cover image should be considered.
The advantages of LSB are its simplicity to embed the bits
of the message directly into the LSB plane of Cover-image
and many techniques use these methods. The simplest
approach to hiding data within an image is called least
significant bit (LSB) insertion. For 24-bit true color image, the
amount of changes will be minimal and indiscernible to the
human eye. As an example, suppose that we have three
adjacent pixels (nine bytes) with the following RGB encoding:
10010101 00001101 11001001
10010110 00001111 11001010
10011111 00010000 11001011
Now suppose if we want to hide the following 9 bits of
data101101101. If we overlay these 9 bits over the LSB of the
9 bytes above, we get the following pixels.
10010101 00001100 11001001
10010111 00001110 11001011
10011111 00010000 11001011
Cover image + hidden information = stego image
In this perspective, the cover image is the main image in
which the hidden information will be embedded. The resultant
image is the stego image which contains both the cover image
and the message.
The Adaptive LSB technique involves the following steps:
 Convert the text into its binary equivalent.
 Preprocess the cover image by separating the smooth
and edge part using canny edge detection.
 Get the each pixel value of cover images one by one.
 Replace each bit of the cipher text with the last bit of
each pixel in the image.
Each character of the plain text is first converted into its
corresponding ASCII equivalent. Then, this ASCII value is
converted into its corresponding binary equivalent. As for
example the conversion of text “HELLO” is explained below.

4. Vaishali Kataria et al.: Image Steganography with Encryption Using Adaptive Algorithm
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Table 1
ASCII value and corresponding binary value
H E L L O
72 69 76 76 79
01001000 01000101 01001100 01001100 01001111
So every contents of the text file are converted into its
corresponding binary equivalent using the mentioned
technique. Also the text file maybe masked before replacing it
in the pixels of the image. The Masking Technique is shown in
the below example. In this technique the each byte of the text
file’s binary equivalent is binary ANDed with the binary
equivalent of 254. Then the bits are exchanged with the image
pixels. This masking process will provide additional security.
For “H” binary value: 01001000
Mask (255) : 11111111
01001000
The system also checks that the length of the secret
message does not exceed the maximum embedding potential
of the first image. If the secret message exceeds the
embedding length it requires another cover image to embed
and it will reconstruct at the receiver side.
In non-compressed images steganography typically occurs
in the spatial domain. Data is typically hidden in images by
modifying the least significant bit (LSB) of a pixel value. This
introduces a very small change in the color of the pixel that is
not noticeable to the human eye. By ripping the LSB values in
a bitmap we can embed a binary message that can be retrieved
at a later point. In the compressed JPEG image we cannot
modify pixel values in the spatial domain because the JPEG
compression algorithm is lossy. This means that should we try
embedding data on the LSB of pixel values we may not get
the same pixel value after decompression. Because of this
problem steganography needs to take place in a different
manner than in non-compressed bitmap images. By adding an
extra feature in our project to separate the edge part of the
image we are extracting the smooth portions only to embed
the message bits. For this we use canny edge detection method
to remove the edge part from cover image.
After this each pixel value is processed of the cover image
and converts the intensity value into their equivalent binary
value. First the image separated into smooth and edge part
using canny edge detection and then only smooth portion of
the image are converted into the binary value.
Each bit of the cipher text is replaced in the LSB position
of the pixels in the image. Here LSB refers to the Least
Significant Bit i.e. the last bit of the pixel value. Since only
the LSB is changed, the difference between the original image
and the encrypted image will be very small, that the difference
cannot be detected by naked human eyes. Only software that
are particularly determine the patterns in the images can detect
the irregularities in the patterns. The Cellular Automata finds
a wide application in Image Processing. Using cellular
automata the design patterns in regular images like a shell, or
a stone or any object that has a regular pattern of colors, can
be determined. For this purpose rules are framed according to
these patterns. So applying Cellular Automata in
Steganography, one can detect the availability of secret
messages in the images if there is an irregularity in the pattern
of the images.
The Adaptive LSB technique can also be briefly explained
with the help of bits. The LSB technique is explained with the
help of binary values. The last bits of the pixels are replaced
with the bits of the cipher text. So the final image will
resemble the original image.
Modulating the LSB does not result in a human-perceptible
difference because the amplitude of the change is small.
Therefore, to the human eye, the resulting stego-image will
look identical to the cover-image. This allows high perceptual
transparency of LSB. But the quality of the stego image
produced by simple LSB substitution may not be acceptable.
It means that the method degrades the image quality and
probably attracts unauthorized attention. Once he/she notices
the stego image, secret message can be easily extracted by
simple LSB analysis.

5. 31 INTERNATIONAL GLOBAL JOURNAL FOR ENGINEERING RESEARCH, VOL. 10, NO. 2, NOV 2014
The proposed scheme solves this Simple LSB problem
using Adaptive LSB. Adaptive LSB increase the complexity
of hidden data as well as preserves the quality of stego image.
In the very first step of Adaptive LSB method detects edges of
the cover image using canny edge detection algorithm. Cover
image now separated into two parts as edges and remaining
smooth part. We apply the LSB substitution to the only
smooth parts of the image.
In our implementation we first encrypt the message using
blowfish algorithm and convert it into cipher text. On the
basis of this cipher text we calculate length and divide it into
appropriate number of modules. These individual cipher
modules then hide into the individual cover image using
Adaptive LSB method. For Adaptive LSB method each cover
image is passed over canny edge detection for smooth and
edge portions separation.
For the edge detection from gray scale cover image, canny
edge detection algorithm was used which was designed to be
an optimal edge detector. It takes as input a gray scale image,
and produces as output an image showing the positions
of tracked intensity discontinuities. The canny operator works
in a multi-stage process. First of all the image is smoothed
by Gaussian convolution. Then a simple 2-D first derivative
operator is applied to the smoothed image to highlight regions
of the image with high first spatial derivatives. Edges give rise
to ridges in the gradient magnitude image. The algorithm then
tracks along the top of these ridges and sets to zero all pixels
that are not actually on the ridge top so as to give a thin line in
the output, a process known as non-maximal suppression. The
tracking process exhibits hysteresis controlled by two
thresholds: T1 and T2, with T1 > T2. Tracking can only begin
at a point on a ridge higher than T1. Tracking then continues
in both directions out from that point until the height of the
ridge falls below T2. This hysteresis helps to ensure that noisy
edges are not broken up into multiple edge fragments.
The message embedding process is depicted step by step
as plain text is first converted into cipher text using blowfish
algorithm. This cipher text is divided into number of blocks
according to the length of the original message. Individual
cipher text block using Adaptive LSB technique embedded
into different cover images. Finally the stego image will send
to the network and extracted on the receiver side using
extraction process.
IV.EXPERIMENTAL RESULTS
The proposed algorithm provides improvement of stuffing
capacity. In PVDM method, the pixel pairs are classified as
smooth area pixels, where the pixel value difference is small
and edge area pixels, where the pixel value difference is large.
It can be realized that the number of pixel pairs in smooth
areas is considerable in amount. In order to improve the
capacity of the technique, these smooth area pixels can be
used for embedding secret data using LSB replacement
method, which accommodates more number of bits.
Table 2
Hiding Capacity of cover images
Images Resolution
Capacity in Bits
Simple
LSB
Proposed
Method
Cover
Image 1
500*500 361645 556578
Cover
Image 2
500*500 360753 526365
Cover
Image 3
500*500 360237 460432
Cover
Image 4
500*500 410643 720675
Cover
Image 5
500*500 432054 427456
The original Pixel values of cover image are considered are
(246,100).The stego pixel values of the cover image after
stuffing 7bits of secret bits are (247,101). Hence without
much difference in pixel values, 7 secret data bits can be
embedded which shows an improvement in data stuffing
capacity.Capacity is key important evaluating parameter in
steganography technique. Capacity is defined as number of
pixels used in the cover image to embed the secret message of
any length. Proposed technique uses very minimum number of

6. Vaishali Kataria et al.: Image Steganography with Encryption Using Adaptive Algorithm
32
pixels compared to the existing technique and this has been
proved by experimental results.
From Table 2, it can be observed that the hiding capacity of
data bits is increased in the range of 55% to 72% for various
images, by comparing the capacity of Pixel Value
Differencing with Modulus function method.
V. CONCLUSIONS
In this paper, an Adaptive pattern based image
steganography technique has been introduced. This technique
addresses key important issues like Adaptiveity in data
embedding and blowfish algorithm for cryptography and both
of these in combination give appropriate results. Proposed
technique results in very high capacity with low visual
distortions and all this have been proved by experimental
results. This technique has also been compared with important
features of other steganography algorithms.
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