14-02-2013, 09:39 AM
Edge Adaptive Image Steganography Based on LSB Matching Revisited
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Abstract
The least-significant-bit (LSB)-based approach is a
popular type of steganographic algorithms in the spatial domain.
However, we find that in most existing approaches, the choice
of embedding positions within a cover image mainly depends
on a pseudorandom number generator without considering the
relationship between the image content itself and the size of the
secret message. Thus the smooth/flat regions in the cover images
will inevitably be contaminated after data hiding even at a low
embedding rate, and this will lead to poor visual quality and
low security based on our analysis and extensive experiments,
especially for those images with many smooth regions. In this
paper, we expand the LSB matching revisited image steganography
and 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.
For lower embedding rates, only sharper edge regions are used
while keeping the other smoother regions as they are. When the
embedding rate increases, more edge regions can be released
adaptively for data hiding by adjusting just a few parameters.
INTRODUCTION
STEGANOGRAPHY is a technique for information hiding.
It aims to embed secret data into a digital cover media, such
as digital audio, image, video, etc., without being suspicious.
On the other side, steganalysis aims to expose the presence of
hidden secret messages in those stego media. If there exists a
steganalytic algorithm which can guess whether a given media
is a cover or not with a higher probability than random guessing,
the steganographic system is considered broken.
ANALYSIS OF LIMITATIONS OF RELEVANT APPROACHES
AND STRATEGIES
In this section, we first give a brief overview of the typical
LSB-based approaches including LSB replacement, LSBM,
and LSBMR, and some adaptive schemes including the original
PVD scheme [17], the improved version of PVD (IPVD) [18],
adaptive edges with LSB (AE-LSB) [19], and hiding behind
corners (HBC) [14], and then show some image examples to
expose the limitations of these existing schemes. Finally we
propose some strategies to overcome these limitations.
In the LSB replacement and LSBM approaches, the embedding
process is very similar. Given a secret bit stream to be
embedded, a traveling order in the cover image is first generated
by a PRNG, and then each pixel along the traveling order
is dealt with separately. For LSB replacement, the secret bit
simply overwrites the LSB of the pixel, i.e., the first bit plane,
while the higher bit planes are preserved. For the LSBM
scheme, if the secret bit is not equal to the LSB of the given
pixel, then 1 is added randomly to the pixel while keeping the
altered pixel in the range of . In such a way, the LSB of
pixels along the traveling order will match the secret bit stream
after data hiding both for LSB replacement and LSBM. Therefore,
the extracting process is exactly the same for the two approaches.
It first generates the same traveling order according
to a shared key, and then the hidden message can be extracted
correctly by checking the parity bit of pixel values.
CONCLUDING REMARKS
In this paper, an edge adaptive image steganographic scheme
in the spatial LSB domain is studied. As pointed out in
Section II, there usually exists some smooth regions in natural
images, which would cause the LSB of cover images not to be
completely random or even to contain some texture information
just like those in higher bit planes. If embedding a message in
these regions, the LSB of stego images becomes more random,
and according to our analysis and extensive experiments, it
is easier to detect. In most previous steganographic schemes,
however, the pixel/pixel-pair selection is mainly determined by
a PRNG without considering the relationship between the characteristics
of content regions and the size of the secret message
to be embedded, which means that those smooth/flat regions
will be also contaminated by such a random selection scheme
even if there are many available edge regions with good hiding
characteristics.