International Journal of Engineering Trends and Technology (IJETT) – Volume 29 Number 1 - November 2015
A Review on current Image Steganography trends with focus
on Spatial Domain Techniques
Saleema.A1, Dr.T.Amarunnishad*2
#
II year M.Tech, Computer Science and Engineering, TKM College of Engineering, Kollam, Kerala, India
Abstract — Steganography is a communication
method which involves sending secret information in
appropriate carriers. Since it has an interesting fact of
concealing the message as well as the existence of the
message, steganography is on its evolutionary path to
unearth new platforms. As the field of steganalysis is
growing exponentially, the need for developing strong
steganographic algorithms is also growing. Since the
use of steganography is spreading across various
fields, the goal of increasing the embedding capacity,
security and image quality is being the major
concerns. Here we analyze the current trends and
recent advancements in image steganography mainly
giving focus on the spatial domain techniques. This
paper reviews some recent state of the art techniques
from the literature. Also we mention the various
classifications and some of the quality assessment
measures and attacks.
Keywords — Digital Image Steganography, Adaptive
Steganography,
Reversible
and
Irreversible
Steganography, Embedding capacity, Imperceptibility,
Stego image, Stegnanalysis.
I. INTRODUCTION
Steganography deals with the art of hiding information
with an interesting property of hiding the mere
existence of the secret information. What makes
steganography more preferable than cryptography is
its extra layer of security provided by the
undetectability of the presence of secret information.
Cryptography is the practice of scrambling a message
to an obscured form to prevent others from
understanding it while steganography is the study of
obscuring the message so that it cannot be seen.
Steganography requires two files – the
cover/carrier file and the secret file. Various
multimedia carriers like audio, video, text, image etc
can act as a cover media to carry the secret
information. Also the secret can be of any type, which
in most cases is converted into a bitstream. The
resultant file after embedding can also be called as a
stego-file. In image steganography the cover file will
be an image and the secret may be plain text, cipher
text (or another image).In this review, we focus on
image steganography.
computational complexity of insertion and extraction
processes.
Payload capacity refers to the amount of
information that can be embedded in the cover without
affecting the quality of the cover medium. For image
steganography, it is usually expressed in bpp (bits per
pixel) which indicates the number of bits that can be
concealed in a particular pixel.
Security/Robustness refers to the capability of a
stego-image to resist the statistical and visual
steganalytic attacks as well as some image
manipulations like rotation, scaling, cropping etc.
Another important thing to consider is the
imperceptibility which means the changes introduced
by the steganographic algorithm should not be
noticeable by the human eye.
Also the time required to embed as well as extract
the secret data should be considered while designing a
steganographic algorithm.
III. IMAGE STEGANOGRAPHY MODEL
The major components of image steganographic
model are cover image, secret image and stego-image.
Cover image is the place where we conceal our secret.
The secret image to be hidden is converted into a bit
stream and using the stego-key, the insertion
algorithm is performed. After insertion a stego-image
is formed which looks exactly similar to that of the
cover image, but having the secret data in it. This
stego image should be resistant to steganalytic attacks
as they are being send through the insecure
communication channels.
Upon receiving the stego-image, using the stegokey the receiver can extract the bit stream by
performing the extraction process (Reverse of
insertion process). Later the bit stream is converted to
image. The stego-key is the one which should be
predetermined and shared by both the sender and the
receiver. Figure 1 shows an image steganography
model.
II. REQUIREMENTS OF STEGANOGRAPHY
The important issues to be considered in
steganography
are
the
payload
capacity,
security/robustness,
imperceptibility
and
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International Journal of Engineering Trends and Technology (IJETT) – Volume 29 Number 1 - November 2015
((2
SSIM ( Ic, Is)
2
Ic
(
Ic Is
2
Is
(224 1) 0.01)2 )((2 Ic
((224 1) 0.01)2 )( Ic2
Is
2
Is
(224 1) 0.03)2 )
((224 1) 0.03)2 )
(3)
where
Ic
,
Is
are the mean of cover and stego
images respectively,
Ic
,
Is
is the covariance of
2
Ic
Figure 1: Image Steganography Model
IV. PERFORMANCE MEASURES
Performance of a steganographic algorithm depends
on the following factors.
* Security
* Imperceptibility
* Embedding capacity
* Computational complexity
Like any other image processing techniques,
there are statistical as well as visual measures for
performance evaluation in image steganography.
Since the field of steganalysis is fast growing, the
effort required in designing robust steganographic
algorithms is also increasing.
Some basic measures of testing the image quality
include PSNR, MSE, SSIM etc.
PSNR is defined as the ratio between the
maximum possible power of a signal and the power of
corrupting noise that affects the fidelity of its
representation. The signal in this case is the original
image, and the noise is the error introduced by
steganography. It is preferable if the stego-image
generated have a PSNR of more than 40 dB. PSNR
can be calculated using equation (1).
PSNR 10 log10
max 2
MSE
2
,
Is are the variance
cover and stego images and
of cover and stego images respectively.
Correlation measure can be used to find out the
correlation between cover and stego images. A
correlation value closer to 1 indicates that there is less
distortion and the stego image is more similar to that
of the cover. Otherwise the stego image is distorted. It
is calculated using the equation (4).
M
N
pij
p qij
q
i 1 j 1
r
M
N
pij
i 1 j 1
p
2
M
N
qij
q
2
i 1 j 1
(4)
Where p and q indicate the average pixel value of
cover image and stego image respectively. MxN is the
image size.
Some other image quality measures include
Normalized cross correlation, Average Difference,
Maximized difference, Image fidelity [1] etc.
There are some metrics like Minkowski summation
[2], Watson metric [3], Universal quality index [4],
Spearman’s rank correlation coefficient [5] which can
be used for image quality assessment.
In order to assure security/robustness of
steganography algorithms, it should be proven that
they are resilient to some known steganalytic attacks.
2
Some classical attacks include Chi square (
) attack,
Regular Singular (RS) attack, Pixel Value
Differencing, LSB flipping etc.
Where max is the maximum pixel intensity and MSE
Chi square attack was proposed by Westfield and
(Mean Squared Error) for a colour image can be
Pfitzmann
(2000). It is based on statistical analysis of
calculated by equation (2)
a Pair of Values (PoVs) that are exchanged during
MSER MSEG MSEB
sequential concealing. The idea of the statistical attack
MSE
3
(2) is to compare the theoretically expected frequency
distribution in steganograms with some sample
Where MSER , MSEG , MSEB corresponds to the
distribution observed in the possibly changed carrier
MSE of Red, Green and Blue components respectively. medium [6]. We can find out the probability that some
embedding has taken place by the degree similarity of
SSIM is the Structural Similarity Metric the observed sample distribution and the theoretically
expected frequency distribution.
proposed by Wang et.al (2004) to measure the
Regular Singular Steganalysis proposed by
Fridrich
et.al [7] is commonly used in random LSB
similarity between two identical images. This metric
steganography. It is based on some alternations made
can be calculated by equation (3).
in the Least Significant Bit plane of an image and the
classification of pixels into three groups. The
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International Journal of Engineering Trends and Technology (IJETT) – Volume 29 Number 1 - November 2015
embedding length depends on the counts of the three
groups.
Figure 2: General Classification of Image
Steganography
V. IMAGE STEGANOGRAPHY METHODS
A. General Classification and current trends
Image steganography methods can be broadly
classified into three namely spatial domain based,
frequency domain based and compressed domain
based (Figure 2). Each method has advantages as
well as disadvantages. As we analyse, the spatial
domain techniques is found to achieve a better trade
off with the basic requirements of steganographic i.e.
Spatial domain techniques can achieve high
embedding capacities with better security and
robustness and considerable computation time.
In the recent years researchers of image
steganography have introduced some efficient
methods by combining the traditional LSB and PVD
like approaches with new algorithms. Some
researchers concentrates on making use of some
features of cover images to find out better embedding
regions and then concealing more secret data on those
regions According to the human vision sensitivity
complex regions are well suited to hide data than
smooth regions. Also in some approaches the cover
image is subjected to some sort of preprocessing to
increase the payload capacity.
To achieve low cost, optimal and adaptive data
concealing some soft computing based approaches are
being used with steganographic algorithms. It is
found that introduction of soft computing techniques
like fuzzy Logic, Adaptive Neural Network, Particle
Swarm Optimization, Ant Colony optimization etc can
give excellent results in image steganography.
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Figure 3: Classification of Spatial Domain
Techniques.
B. Recent Advancements in Spatial Domain
Techniques
The Spatial Domain Techniques for image
steganography can be classified as in figure 3. The
most popular spatial domain techniques used in image
steganography are LSB based approaches. These
include LSB replacement, LSB matching, LSB
Matching Revisited etc. The major advantages of LSB
based techniques include less degradation or
imperceptibility and reasonable embedding capacity.
The disadvantage is that such methods are less robust
and sometimes the hidden data can be easily destroyed
by some known steganalytic attacks.
In [8], El Emam proposed a steganographic
algorithm, hiding a large amount of data (up to 75% of
the cover image size). The pixels for hiding are
selected randomly and he designed a new concept
defined by main cases and their sub cases for each
byte in one pixel of a color image, to find the number
of bits to be replaced with the secret data. The
algorithm consists of three layers, one for adaptive
segmentation of the cover image, one for encryption
of secret message and the third for information hiding.
Later he improved the method by adding an additional
layer (forth layer) in [9]. This layer which is based on
a neural network adds additional security against
statistical as well as visual attacks. Further the method
is modified by introducing the concept of Genetic
Algorithm [10]. Also a new method for information
hiding based on byte characteristics and variance
distribution is proposed in [10]. Thus it turned out to
be an intelligent method to conceal a large amount of
secret message with hybrid adaptive neural network
and modified adaptive genetic algorithm. This can be
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International Journal of Engineering Trends and Technology (IJETT) – Volume 29 Number 1 - November 2015
viewed as a significant advancement in the research
area of image steganography.
In [11], a high capacity image steganography
using multi layer embedding which results in good
quality stego image and low computational
complexity is proposed. The method is found to be
better than the NMI [12] and INP [13] hiding methods.
Neighbor mean interpolation (NMI) and Interpolating
Neighboring
Pixels
(INP)
integrate
image
interpolation with data hiding in spatial domain.
A low distortion data embedding method is
proposed in [14]. This method explores pixel value
differencing and base decomposition. This method is
based on a range table for characterizing the host
image as in some other methods like PVD [I5] and
HPVD [16]. But the advantage of this method over
PVD and HPVD are the degree simplification and less
pixel variation. It is found out that while embedding
the same or slightly larger quantities of messages, this
scheme produces the highest embedded image quality
compared with the other two.
Replacing the LSB of cover images with secret
data and authentication code will result in distortion of
stego image. So in [17], a novel method of image
sharing is introduced by applying the Optimal Pixel
Adjustment Process (OPAP) process to enhance the
image quality under different payload capacity and
various authentication bits conditions. The use of an
image identification number makes this method
distinct and more secure than other schemes.
The authors proposed a new steganography
algorithm using hybrid fuzzy neural networks. Here
data is embedded using LSB approach and a post
processing of the stego image is carried out using the
fuzzy neural networks. This method could produce
stego images of high visual quality and payload
capacity.
C. Adaptive Techniques
Another classification of image steganography is
Adaptive Steganography. This category may include
spatial as well as transform domain techniques. Such
techniques embed data adaptively by considering the
concepts of human vision and results in high payload
capacity as well as low distortion. Most of the PVD
based techniques comes under this category as they
are edge adaptive.
In [18] and [19], they propose an edge adaptive
scheme which can select the embedding regions
according to the size of secret message and the
difference between two consecutive pixels in the cover
image. Here they tried to embed as much data as
possible in the sharper edge regions than the smoother
regions by adjusting a few parameters. Instead of
selecting the pixel/pixel pair for embedding using a
pseudorandom generator, they made use of the
relationship between the image content and the size of
the secret message.
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A method called as HBC is proposed in [20], which
also have the same property of edge adaptive scheme.
Since they just modifies the LSBs while keeping the
most significant bits unchanged (edge adaptive case of
LSB replacement), there is a problem of asymmetry in
their stegos.
A similar adaptive method which concentrates on
edge areas for hiding with spatial LSB domain
systems which outperforms the PVD and LSB
replacement methods is proposed in [21] .They
combine the method of LSB replacement with another
skillful method for readjusting the pixel values in
order to increase the quality of stego images.
In [22] and [23] Adaptive pixel pair matching is
used. They combined with image steganography
certain characteristics of human visual system and
found to be more resistant toward the visual and
statistical attacks. In effect these methods hide more
data on the edges identified on the host image.
A new scheme for grayscale images that can
efficiently assign dynamic embedding capacity and
produce high stego image quality is proposed in [24]
in which they exploit the Human Visual Sensitivity
and local complexity of images. They have taken
consideration of local texture characteristics of cover
image and thereby adjust the number of secret bits that
can be embedded in each pixel inside a block. This
method assures strong linear dependency between
histograms of cover and stego images.
D. Reversible and Irreversible Steganography
Image Steganographic methods can be of two types
namely irreversible as well as reversible. Most of the
above methods so far discussed are irreversible, which
means the original cover image cannot be recovered
after the hidden messages have been extracted.
Reversible/Lossless Steganographic method aims at
extracting the embedded information as well as
recovering the original cover media. Although certain
distortions are introduced during embedding, they
should be able to remove such distortions.
Reversible data hiding schemes can be of two types.
Difference expansion based and Histogram shifting
based. The DE based methods can provide better
embedding capacity but as it accumulates image
degradation between pixel pairs, it is not well suited
for multi layer embedding. In histogram shifting based
methods, although there is limited embedding capacity,
image distortion is found to be less.
In [25] , a reversible data hiding scheme is used
which is based on histogram shifting imitated
reversible data hiding to embed data by using the pixel
shifting strategy. The results show that this scheme is
suitable for single as well as multi layer embedding
with low distortion. The superiority of this method
with others resides in its high visual quality.
A reversible hiding method for BTC compressed
color images is proposed in [26]. They applied
Genetic Algorithm for the optimization of bitmaps to
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improve the compression rate and secret data is
embedded in the optimal common bitmap.
Reversibility is achieved by the order of quantization
levels. This method can embed more than three bits
per BTC encoded block and also good in image
quality.
In [27], the reversible data hiding scheme is
classified into 4 types. Type-I are the initial data
hiding algorithms based on lossless compression,
which are not so satisfactory since the introduced
distortion is usually large. Type-ll are those based on
Difference Expansion technique, in which the use of a
location map results in consuming the embedding
capacity. Type-III algorithms are based on predictionerror expansion technique and Type-IV are based on
Histogram shifting. Also they mention another type of
algorithms named integer transform based reversible
data hiding. They proposed PDE based scheme based
on a PDE predictor which iteratively updates the
prediction to yield more accurate one.
Tians method in 2003 was a significant
breakthrough in reversible hiding based on Difference
Expansion with high embedding capacity and visual
quality. Later this method was improved by Kieu ang
Chang in [28] by proposing a method which
horizontally and vertically embeds one secret bit into
one cover pixel pair. This method can give a capacity
of more than 2 bpp with five layer embedding,
assuring higher PSNR.
Lossless data hiding by preserving the file size is
nowadays being a new research area since these have
wide applications today. An improved VLC based
lossless data hiding scheme which directly embeds
data into the bit stream of JPEG images is proposed in
[29] which can output a stego image with exactly the
same content as the original one preserving the same
file size. With enlargement of file size, this method
assures significant improvement in embedding
capacity.
An adaptive reversible data hiding scheme based
on integer
transform is proposed in [30] which
concentrates
on
achieving
high capacity. Here tuning of integer transform is
performed and data is embedded adaptively according
to the block type determined by the pre estimated
distortion.
There are some lossless hiding algorithms with
'Vacating room for embedding after encryption’
(VRAE) and 'Reserving room for embedding before
encryption' (RRBE). Reserving room before
encryption is found to be more effective and easier.
Such a technique is proposed in [31] which empty out
room by embedding LSBs of some pixels into other
pixels and then encrypt the image, so the positions of
these LSBs in the encrypted image can be used to
embed data.
E. Cover image pre-processing
Some of the methods for steganogaphy introduce a
preprocessing stage before applying the data
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embedding algorithm. This is because the cover image
selected has a significant impact in achieving the basic
steganographic requirements like payload capacity,
imperceptibility, transparency etc. Some images may
not have enough contents to hide data securely. If we
appropriately do some preprocessing on the cover
image by making use of the lucrative effects of image
processing, we can amplify the data embedding rate
for the particular cover image. In some other methods
particular regions of the cover image is selected in
some manner so that data embedding in those selected
regions seems to be more imperceptible. These
methods make use of the properties like local
complexity, human vision sensitivity etc.
Sajedi and Jamzad [32] proposed a boosted
steganography scheme with cover image processing
with the goal of increasing the undetectability of stego
images. Here the main idea of the preprocessing stage
is to impose more variation in the pixel intensities of
cover images compared to the original ones. The
improvement they obtained is due to the properties of
contrast enhancement methods and Successive Mean
Quantization Transform (SMQT) enhancement. Their
steganographic scheme consists of two stages one to
preprocess and second for embedding. They reveal
that Sharpening and Histogram Equalization provide
more embedding capacity than SMQT transform. The
SMQT enhancement results in more normal images
and the enhancement is not apparent visually. But
Sharpening and HE enhancement may be detectable
with some simple processing while SMQT that has a
local enhancement is not detectable easily. The image
whose quality is enhanced by SMQT transform is
usually perceptually indistinguishable from the
original image.
In an adaptive method proposed in [24], DerChyuan Lou classified pixels into three levels based
on the variance of the local complexity of the cover
image and they determined the level of complexity a
pixel belonged to by taking human vision sensitivity
into consideration. In their scheme the standard
deviation is adopted to analyze the local complexity so
as to estimate number of bits that can be concealed in
a block of image. A higher standard deviation means
the texture of the block is rougher and therefore the
block can carry more secret data.
In [22] and [33]
a candidate region in the stego image is found out for
data hiding by scanning the cover image using a
spatial frequency and luminance masking. Thereafter
an adaptive pixel pair matching is used to hide data in
the candidate region. They plotted some
characteristics of the human visual system which can
be combined with image steganography to avoid
visual and statistical detection of the embedded data.
They highlighted the facts that the human eye is less
sensitive to noise in the high resolution bands having
orientation of 45. Also the eye is less sensitive to noise
in those areas of the image where brightness is high or
low and the highly textured areas.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 29 Number 1 - November 2015
In [34] Rashidy and Bahram proposed an approach
to find out the best place for embedding modified
secret data in the host image to achieve high level of
security. Here genetic algorithm is used to find out the
best starting point, scanning order and other options
such that the PSNR of the stego image is maximized.
In [35], the authors used a preprocessing operation
in order to increase the security of the system as well
as the resistance against steganlytic attack. This
operation checks whether an image could be used or
not for embedding. They first divide the cover image
into blocks and the pixel in th center of the blocks is
checked by calculating the differences between this
pixel and all the surrounding pixels. According to the
values they categorized the blocks into 5 categories.
The ratio of each category to the total number of
pixels in the images is calculated and according to
them they determine whether that image is suitable or
not.
V. CONCLUSION
This paper presented a discussion about some major
and efficient image steganographic methods evolved
in the past decade. As we analyze emerging
technologies like soft computing can be incorporated
with the field of image steganography to achieve
better and acceptable results. We focused on spatial
domain techniques since they are offering a higher
payload while satisfiably meeting the other
requirements. Also we discussed some advantages as
well as disadvantages comparing various methods.
Although it is impossible to say that a particular
method is the best of all, we can find out the best
method for a particular usage from this review.
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