17-09-2016, 11:57 AM
1455100699-BasePaper.pdf (Size: 217.24 KB / Downloads: 5)
ABSTRACT— Need of hiding information from intruders
has been around since ancient times. To maintain the
secrecy of information, different methods of hiding data
have evolved. One of them is steganography which
means hiding information under some other information
without noticeable change in the cover information.
Steganography is an important area of research in recent
years involving a number of applications. It is the science
of embedding information into the cover image, text,
video etc without causing statistically significant
modification to the cover. The modern secure image and
video steganography presents a challenging task of
transferring the embedded information to destination
without being detected. In this work, we present an
image and video based steganography that uses least
significant bit (LSB) and discrete cosine transform
(DCT) techniques to enhance the security of payload.
Video is simply a sequence of images and hence much
space is available in between for hiding information.
Each frame of video will be broken down into individual
components and then converted into 8-bit entities.
Initially direct pixel replacement, DCT, 4 bit and 1 bit
LSB algorithm is used to embed the payload bits into
respective cover such as image and video to derive the
stego image or the stego video. By several experimental
analysis, we observe that secure images with low MSE
and high PSNR are obtained
INTRODUCTION
Internet which is ever more accessible to interference by
unauthorized people over the world, it is important to
bring down a chance of information being sensed while
transmitting is the major issue these days. Although the
internet now puts communication, education, commerce
and socialization at our finger tips, its rapid growth has
raised some weighty security concerns with respect to
multimedia content. Data transmission in public
communication system is not secure because of
interception and improper manipulation by eavesdropper.
Communication through computer network requires
more security. Attacks may affect the quality of the data.
The owners of this data content face enormous
challenges in safeguarding their intellectual property while still exploiting the internet as an important
resource for commerce. One lesser-known but rapidly
growing method is steganography, the art and science of
hiding information so that it does not even appear to
exist.
II. RELATED WORK
Nitin Jain et.al.,[1] they have shown how the
edges of the images can be used to hide text message in
steganography. It gives the depth view of image
steganography and edge detection filter techniques. The
method calculates binary value of each character of text
message and then tried to find dark places of gray image
(black) by converting the original image to binary image.
Then these images have been converted to RGB image in
order to find dark places. In this way each sequence of
gray colour turns into RGB colour and dark level of grey
image is found by this way. In the final stage each 8
pixels of dark places has been considered as a byte and
binary value of each character has been put in low bit of
each byte that was created manually by dark places
pixels for increasing security of the main way of LSB bit
steganography. Steganalysis then used to evaluate the
hiding process to ensure the data can be hidden in best
possible way. 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.
Potdar et.al.,[2] used a spatial domain
technique in producing a fingerprinted secret sharing
steganography for robustness against image cropping
attacks. Their paper addressed the issue of image
cropping effects rather than proposing an embedding
technique. The logic behind their proposed work is to
divide the cover image into sub-images and compress
and encrypt the secret data. The resulting data is then
sub-divided in turn and embedded into those image
portions. To recover the data, a Lagrange Interpolating
Polynomial was applied along with an encryption
algorithm. The computational load was high, but their
algorithm parameters, namely the number of sub-images
(n) and the threshold value (k) were not set to optimal
values leaving the reader to guess the values. Bear in
mind also that if n is set to 32, for example, that means
32 public keys are needed along with 32 persons and 32
sub-images, which turns out to be unpractical. Moreover, data redundancy that they intended to eliminate does
occur in their stego-image.
M.B.Ould MEDENI et.al.,[3] article, the
authors propose a novel method for hiding information
within the spatial domain of the gray scale image. The
pixel value differencing (PVD) method segments the
cover image into non-overlapping blocks containing two
connecting pixels and modifies the pixel difference in
each block (pair) for data embedding. While embedding
secret data, each pixel is split into two equal parts. The
number of 1’s in the most significant part is counted and
the secret message is embedded in the least part
according to the number of corresponding bits. The
proposed method is based on four-pixel differencing and
LSB substitution. Hemalatha.S et.al.,[9] paper, the
authors propose a method that uses two gray scale
images of size 128 x 128 that are used as secret images
and embedding is done in RGB and YCbCr domains. The
quality of stego images is good in RGB domain by
comparing the PSNR values. The authors have used
Integer Wavelet Transform (IWT) to hide secret images
in the colour cover image. The authors have compared
the PSNR values and image quality when embedding is
done in the RGB and YCbCr domains.
Abdelwahab and Hassan [10] propose a data
hiding technique in the DWT domain. Both secret and
cover images are decomposed using DWT (1st level).
Each of which is divided into disjoint 4x4 blocks. Blocks
of the secret image fit into the cover blocks to determine
the best match. Afterwards, error blocks are generated
and embedded into coefficients of the best matched
blocks in the HL of the cover image. Two keys must be
communicated; one holds the indices to the matched
blocks in the CLL (cover approximation) and another for
the matched blocks in the CHL of the cover. Note that
the extracted payload is not totally identical to the
embedded version as the only embedded and extracted
bits belong to the secret image approximation while
setting all the data in other sub images to zeros during
the reconstruction process.
In Keith. Haynes’s[11] article the author
studies the use of image steganography to breach an
organization’s physical and cyber defences. The
proposed method utilizes computer vision and machine
learning techniques to produce messages that are
undetectable and if intercepted cannot be decrypted
without key compromise. To avoid detection DWT
(discrete wavelet transform) is used. The goal of a
computer vision system is to allow machines to analyze
an image and make a decision as to the content of that
image. The computer vision can be categorized as
Model-Based & Appearance Based which uses example
images and machine learning techniques to identify
significant areas or aspects of images that are important
for discrimination of objects contained within the image.
Machine learning is different from human knowledge/
learning. A computer has to make decision of the
presence of a face based on the numbers contained in a
2D matrix. The feature is identified by using Haar feature
selection. The goal is to identify the set of features that
best distinguishes between images in the different
classes. In the proposed method the cover image does not
contain a secret message, rather the classification of the
image yields the hidden message. Since the proposed algorithm utilizes ordinary unmodified images, there are
no inherent indicators of covert communication taking
place.
III. PROPOSED STEGANOGRAPHY METHOD
In the proposed work, we present image and video based
steganography techniques that use least significant bit
(LSB) and discrete cosine transform (DCT) to enhance
the security of payload. Video is simply a sequence of
images and hence much space is available in between for
hiding information. Each frame of video will be broken
down into individual components and then converted
into 8-bit entities. Initially Direct Pixel Replacement,
DCT, 4 bit and 1 bit LSB algorithm is used to embed the
payload bits into respective cover such as image and
video to derive the stego image or the stego video. By
several experimental analyses we try to determine the
best among the implemented techniques that gives low
MSE and high PSNR value.