Proceedings of 2014 RAECS UIET Panjab University Chandigarh, 06 – 08 March, 2014
Improved Detection of Least Significant Bit
SteganographyAlgorithms in Color and Gray Scale
Images
Manu Devi
Department of Computer Engineering
The Technological Institute of Textile & Sciences
Bhiwani, Haryana, India
manughanghas26@gmail.com
Abstract— This paper proposes an improved LSB (least
Significant bit) based Steganography technique for images
imparting better information security for hiding secret
information in images. There is a large variety of steganography
techniques some are more complex than others and all of them
have respective strong and weak points. It ensures that the
eavesdroppers will not have any suspicion that message bits are
hidden in the image and standard steganography detection
methods can not estimate the length of the secret message
correctly. In this paper we present improved steganalysis
methods, based on the most reliable detectors of thinly-spread
LSB steganography presently known, focusing on the case when
grayscale Bitmaps are used as cover images.
Keywords—LSB; Steganography; Steganalysis; Gray Images;
RGB.
I.
INTRODUCTION
An important aspect of the way of life is communication. A
lot of devices present today have the ability to transmit various
information between them using different ways of
communication, like in secure public networks, different types
of wireless networks and the most used is the Internet. In some
cases it is needed to keep the information travelling through
discrete kinds of channels secret. Steganography is the art of
thrashing information in ways that prevent the detection of
hidden messages. Steganography is a technique that involves
hiding a message in an suitable carrier e.g., an image, an audio
or video file. The carrier can then be sent to a receiver without
anyone else knowing that it contains a hidden message [1].
Literally meaning “covered writing”, it includes a broad
collection of secret communication methods like undetectable
inks, microdots, character organization, digital signatures,
covert networks, spread spectrum etc. that conceal the very
existence of message. An image steganographic scheme is one
kind of steganographic systems, where the confidential
message is hidden in a digital image with some hiding method.
Someone can then use a suitable embedding procedure to
recover the hidden message from the image. The unique image
is called a cover image in steganography, and the messageimplanted image is called a stego image [2]. The basic
structure of Steganography is made up of three constituent: the
carrier, the message, and the key. Steganos means covered or
secret, and graphy, means writing or drawing. In this paper we
present improved steganalysis methods, based on the most
reliable detectors of thinly-spread LSB steganography
978-1-4799-2291-8/14/$31.00 ©2014 IEEE
Nidhi Sharma
Department of Computer Engineering
The Technological Institute of Textile & Sciences
Bhiwani, Haryana, India
Nidhisharma1725@gmail.com
presently known, focusing on the case when grayscale
Bitmaps are used as cover images.
In an efficient
steganographic system, a normal cover medium should not be
noticeable from a stego-object. The aims of this paper are a) to
suggest improved steganalysis statistics for LSB
steganography, b) to use huge image libraries to give
experimental evidence of the improvement, and c) to observe
closely the upper limits on bit rate which keep LSB
steganography undetectable.
II.
IMAGE BASED STEGANOGRAPHY
Embedding a message into an image needs two files. The first
is the innocent-looking image that will grip the hidden
information, called the cover picture . The second file is the
message in which the information to be hidden. A
memorandum may be plain-text, cipher-text, other images, or
something that can be embedded in a bit stream. When shared,
the cover image and the embedded message make a stegoimage. A stego-key (a type of password) may also be used to
hide then later decode the message. Most steganography
software recommends the use of lossless 24-bit images such as
BMP. The next- best complementary to 24-bit images is 256color gray-scale images. The most frequent of these are BMP
files.
Embedding data using Modified LSB (Least Significant bit)
Insertion:
A. Embed data in Least Significant Byte of each pixel across
Edge areas:
LSB insertion is a common, simple approach in embedding
information in a cover [13] file. But in this improved LSB
technique we will insert the data only in last significant byte
i.e. blue component of a pixel as that having lowest
contribution to the color image according to Human Visual
System investigation. To hide a message in a 24-bit image, the
B component of each pixel of RGB color image is modified.
For example, the letter A can be hidden in a pixel with original
data as:
(00100111 11101001 11001000)
The binary value for A is 01000001. Inserting the binary
value for A in the given pixel would result in
(00100110 11101001 01000001)
These bits are the only actually changed in the bytes used. On
average, LSB requires that only half the bits in an image be
changed. To hide more data, the cover image should have
enough edge pixels to hide the data.
B. Embedding data using 1-3-4 LSB Insertion across Smooth
areas.
To embed the data in smooth areas 1-3-4 LSBs Insertion
technique has been utilized which hides data in 1-bit in 1 least
significant bit of Red component (Most significant byte),3-bits
in 3 least significant bits of Green component and 4 bits in 4
least significant bits of Blue component(Least significant byte)
of each selected pixel .This ratio 1:3:4 has been taken
depending on their relevant contribution of each red, green
and blue component to the colors of RGB image.
C. Selecting the Edge Pixels Randomly
To select the edge pixel randomly, a pseudorandom number
generator (PRNG) will be used. Pseudorandom number
generator is an algorithm that generates a sequence of
numbers, the elements of which are around free of each other.
The production of pseudorandom number generators is not
truly random - they only approximate some of the properties
of arbitrary numbers. To make use of a PRNG, it first
demands a seed. Seeding is the technical term for giving it an
initial value, from which it can shoot out a sequence. If a
PRNG is given the similar beginning, then it will give the
same set of numbers every time and the elements of which are
approximately independent of each other. The outputs of
pseudorandom number generators are not truly random - they
only approximate some of the properties of random numbers.
To use a PRNG, it first need a seed. Seeding is the technical
term for giving it an initial value, from which it can shoot out
a sequence. If a PRNG is given the similar seed, then it will
give the same set of numbers every time.
III.
LSB STEGANOGRAPHY
LSB insertion is a simple approach for embedding information
in a protect file. It is vulnerable to even a slight image
manipulation. Image translation from a format like GIF or
BMP which reconstructs the original message exactly (i.e.,
lossless compression in which bits are compressed without
losing any bits during compression and exactly recovered
during decompression) to a JPEG which does not(i.e., lossy
compression in which some bits are lost during compression
resulting in some loss in fidelity) and then back could destroy
the information hidden in the LSBs. LSB placing can be
performed in 24-bit, 8-bit or gray-scale images. Here we
consider simple Least Significant Bit (LSB) steganography,
long-known to steganographers, in which the hidden message
is transformed to a stream of bits which replace the LSBs of
pixel values in the cover image. When the hidden message
contains fewer bits than the cover image has pixels, we
assume that the modifications are spread randomly around the
cover image according to a secret key shared with the intended
recipient of the stego image. This sort of steganography is
only suitable for images stored in bitmap form or losslessly
compressed. One should clearly distinguish this method
(perhaps best called LSB replacement) from an alternative,
where the cover pixel values are randomly incremented or
decremented so that the least significant bits match the hidden
message (this should perhaps be called LSB matching). In the
latter case the message is still conveyed using the LSBs of the
pixel values of the image, but the simple variation to the
embedding algorithm makes it much unbreakable to detect.
None of the methods discussed here will detect this alternative
form of steganography, and definitely it is a much more
difficult task to do so: a detector for LSB matching in full
colour bitmaps but it is ineffective for grayscale covers;
another detector which works for full colour images but it is
only reliable for very large embedded messages and barely
effective for grayscale covers. Least significant bit (LSB)
insertion is a simple approach to embedding information in
image file. The straightforward steganography techniques
embed the bits of the message directly into least significant bit
plane of the cover-image in a deterministic sequence. The
advantage of LSB-based method is easy to implement and
high message pay-load. Although LSB secretes the message in
such way that the humans do not identify it, it is still possible
for the opponent to recover the message due to the simplicity
of the technique Therefore; a system named Secure
Information Hiding System (SIHS) is proposed to improve the
LSB scheme. It rises above the sequence-mapping problem by
embedding the massage into a set of arbitrary pixels, which
are scattered on the cover-image.
IV. PROPOSED WORK
Least Significant Bit Insertion Method
One of the most common techniques used in steganography
today is called least significant bit (LSB) insertion. This
method is just what it sounds like; the least significant bits of
the cover-image are altered so that they form the insert
information. The following example explains how the letter A
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)
A: 01000001
Result: (00100110 11101001 11001000)
(00100110 11001000 11101000)
(11001000 00100111 11101001)
A. Algorithm to insert text message using Grayscale Image
Step 1: Study the cover image & text message, which is to be
secreted in the cover image.
Step 2: Change text message into binary.
Step 3: Compute LSB of each pixel of cover image.
Step 4: Put back LSB of cover image with each bit of secret
message one by one.
Step 5: Write stego image.
B.Algorithm to regain text message using Grayscale Image
Step 1: Read the stego image.
Step 2: Determine LSB of each pixel of stego image.
Step 3: Recover bits & convert each 8 bit into character.
Step 4: Determine MSE & PSNR.
For example, using a cover byte 11001000 to hide 3 bit of
information (111), with a simple LSB results in 11001111, this
has a difference of 7 values with respect to the original.
Applying the method proposed here to the above example (in
th
this case, decreasing the 4 least Significant Bit, which have
been used 3 bits LSB to hide information) results in 11000111,
with a distance of 1 from the original byte but with the same
hidden information.
V.RESULTS
The experimental results presented in this section describe the
performance of our proposed technique. For steganography we
use LSB based well known embedding methods. The LSB
insertion was used to embed the message in to the cover
image. The selection of pixel to embed was crucial, since the
LSB insertion modifies the pixels. Modified pixels in areas of
the image where there are pixels that are most like their
neighbors were much more noticeable to the normal eye. To
solve this problem edge pixel were randomly selected to
embed the message. The benefit of LSB is its ease to embed
the bits of the message directly into the LSB plane of cover
image. Also its perceptual transparency where the changes
made to the cover-image cannot be draw by human being eye.
On the opposing, the LSB is very perceptive to any kind of
filtering or manipulation of the stego-image.
Fig. 1. Flow diagram of proposed work
In theory the three least significant bits of the pixel have
changed, introducing a small alteration, but the difference
between the old and new color represents a leap of 65793
colors in the scale of colors.
One method that would introduce more efficiency and less
distortion would store the 3 bits of information to hide in the
same color. Using the same example, the 3 bits of information
will be introduced in the 3 LSB bits of green color (1010100010101111-10101000).
LSB match adaption
Following the method LSB Match (designed to work with a
single LSB bit) has been adapted to allow an LSB Match with
any number of LSB bits. This method calculates the distance
between the original color and the steganographic color.
Should the distance is greater than a certain threshold
(determined by the number of bits to hide) the color is
decremented to get a final color closest to the original,
Simplying a further reduction in the alteration caused by the
hidden information.
Fig. 2. Image Set For Experiments
standard RS statistic of [4]. The experiment has been repeated
with the cover images first resembled down to a number of
different sizes, and it is instructive to see what a difference
this makes to the reliability of the RS statistic.
Fig. 3: Stego Image Result Of LSB Embedding Technique
Fig. 5: ROC curves for a set of 1200 uncompressed images, originally
1024×768 but resembled down to a variety of smaller sizes. In each case 10%
steganography successful used, and tested against the regular RS statistic.
Accuracy Calculation
Generally, stego-image quality is considered from two aspects.
First, we use the Peak Signal-to-Noise Ratio (PSNR)
measurement to evaluate the difference between the stego and
cover images. Second, we judge against the quality of the
stego image with the cover image as seen by the Human
Visual System (HVS). Mean Square Error (MSE) is between
the cover and stego images. For a cover image thickness and
height are m and n, where I denote the cover-image and K
denotes the stego-image MSE is defined as:
The general PSNR formula is defined as:
Fig. 4: Stego Images Result of Proposed Method
Figure shows some of the results from the database. The chart
displayed shows the ROC curves for a small set of 1200
uncompressed images, when 10% LSB steganography (i.e. 0.1
bits per pixel) is used and the images are tested with the
The maximum value of a pixel in grayscale image is 255. A
higher PSNR indicates that the quality of the stego image is
batter and more similar to the cover image. This approach
hides the text in selected dark places but the data is not put
directly in those pixels and put in low bits of each eight bit
pixel. It uses the 3 helpful approaches, which are:
1. Least significant bit insertion 2. Grey level approach with
edge exposure 3. Randomization .The LSB insertion was used
to embed the message in to the cover image. The selection of
pixel to embed was crucial, since the LSB insertion modifies
the pixels. Modified pixels in areas of the image where there
are pixels that are most like their neighbors were much more
noticeable to the normal eye. The advantage of LSB is its
simplicity to embed the bits of the message directly into the
LSB plane of cover image. Also its perceptual transparency
where the changes made to the cover-image cannot be traced
by human eye. On the contrary, the LSB is very perceptive to
any kind of filtering or manipulation of the stego-image.
CONCLUSION
In conclusion, we have suggested a number of improved
methods for deciding whether a grayscale bitmap contains
LSB steganography or not. The paper proposed a new
technique for information security. It presents an improved
steganography method for embedding secret message bit in
least significant byte of non adjacent and random pixel
locations in edges of images and 1-3-4 LSBs of red, green and
blue components of randomly selected pixels across soft areas.
No inventive cover image is required for the extraction of the
secret message. The research was aimed towards the
evaluation and development of a new and enhanced data
hiding technique based on LSB. The chief objective of this
paper is to propose a solution that is robust, effective and to
make it very unbreakable for human eye to predict and detect
the existence of any secret data inside the host image. In most
cases, however, we have not undertaken to give a theoretical
explanation of why the improvement occurs – our new
methods are heuristic and there is no claim of optimality. The
security of the hidden message is very high by using the
password when compared to the additional methods. The
image feature is also superior The Entropy, MSE and
capabilities are improved with acceptable PSNR compared to
the existing algorithm. In future the algorithm be able to be
tested with the transform methods. The proposed solution has
not only achieved what was required but has also increased the
data hiding capacity of the host image by utilizing all the
pixels. We hope that the results presented here will stimulate
research to this end.
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