19-02-2013, 04:55 PM
Edge Adaptive Image Steganography Based on LSB
Matching Revisited
Edge Adaptive Image Steganography.pdf (Size: 6.25 MB / Downloads: 327)
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.
The experimental results evaluated on 6000 natural images with
three specific and four universal steganalytic algorithms show that
the new scheme can enhance the security significantly compared
with typical LSB-based approaches as well as their edge adaptive
ones, such as pixel-value-differencing-based approaches, while
preserving higher visual quality of stego images at the same time.
Index Terms—Content-based steganography, least-significant-
bit (LSB)-based steganography, pixel-value differencing
(PVD), security, steganalysis.
I. 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. In practice,
Manuscript received October 16, 2009; accepted December 13, 2009. Date of
publication February 17, 2010; date of current version May 14, 2010. This work
was supported by the NSFC (60633030), by the 973 Program (2006CB303104),
by the China Postdoctoral Science Foundation (20080440795), and by the
Guangzhou Science and Technology Program (2009J1-C541-2). The associate
editor coordinating the review of this manuscript and approving it for publication
two properties, undetectability and embedding capacity, should
be carefully considered when designing a steganographic algorithm.
Usually, the larger payload embedded in a cover, the more
detectable artifacts would be introduced into the stego. In many
applications, the most important requirement for steganography
is undetectability, which means that the stegos should be visually
and statistically similar to the covers while keeping the embedding
rate as high as possible. In this paper, we consider digital
images as covers and investigate an adaptive and secure data
hiding scheme in the spatial least-significant-bit (LSB) domain.
LSB replacement is a well-known steganographic method. In
this embedding scheme, only the LSB plane of the cover image
is overwritten with the secret bit stream according to a pseudorandom
number generator (PRNG). As a result, some structural
asymmetry (never decreasing even pixels and increasing
odd pixels when hiding the data) is introduced, and thus it is very
easy to detect the existence of hidden message even at a low embedding
rate using some reported steganalytic algorithms, such
as the Chi-squared attack [2], regular/singular groups (RS) analysis
[3], sample pair analysis [4], and the general framework for
structural steganalysis [5], [6].
LSB matching (LSBM) employs a minor modification to LSB
replacement. If the secret bit does not match the LSB of the
cover image, then or is randomly added to the corresponding
pixel value. Statistically, the probability of increasing
or decreasing for each modified pixel value is the same and so
the obvious asymmetry artifacts introduced by LSB replacement
can be easily avoided. Therefore, the common approaches used
to detect LSB replacement are totally ineffective at detecting
the LSBM. Up to now, several steganalytic algorithms (e.g.,
[7]–[10]) have been proposed to analyze the LSBM scheme.
In [7], Harmsen and Pearlman showed that LSBM works as a
low-pass filter on the histogram of the image, which means that
the histogram of the stego image contains fewer high-frequency
components compared with the histogram of its cover. Based on
this property, the authors introduced a detector using the center
of mass (COM) of the histogram characteristic function (HCF).
In [8], Ker pointed out that the original HCF COM method in
[7] does not work well on grayscale images and introduced two
ways of applying the HCF COM method, namely utilizing the
down-sampled image and the adjacency histogram instead of
the traditional histogram, which are effective for grayscale images
that have been JPEG compressed with a low quality factor,
say, 58. In a recent work [10], Li et al. proposed to calculate
calibration-based detectors, such as Calibrated HCF COM, on
the difference image. The experimental results showed that the
new detector outperforms Ker’s approaches in [8] and achieved
acceptable accuracy at an embedding rate of 50%. In [9], Huang
et al. investigated the statistical features of those small overlapping
blocks in the subimage which consists of the first two bit
planes of the image and proposed another kind of steganalytic
feature based on the alteration rate of the number of neighborhood
pixel values. The experimental results demonstrated that
the method was more effective on uncompressed grayscale images.
Besides those specific detectors, some universal steganalytic
algorithms such as [11], [12], and [13] can also be used for
exposing the stego images using LSBM and/or other steganographic
methods with a relatively high detection accuracy.
Unlike LSB replacement and LSBM, which deal with the
pixel values independently, LSB matching revisited (LSBMR)
[1] uses a pair of pixels as an embedding unit, in which the LSB
of the first pixel carries one bit of secret message, and the relationship
(odd–even combination) of the two pixel values carries
another bit of secret message. In such a way, the modification
rate of pixels can decrease from 0.5 to 0.375 bits/pixel
(bpp) in the case of a maximum embedding rate, meaning fewer
changes to the cover image at the same payload compared to
LSB replacement and LSBM. It is also shown that such a new
scheme can avoid the LSB replacement style asymmetry, and
thus it should make the detection slightly more difficult than the
LSBM approach based on our experiments.
The typical LSB-based approaches, including LSB replacement,
LSBM, and LSBMR, deal with each given pixel/pixelpair
without considering the difference between the pixel and
its neighbors. Until now, several edge adaptive schemes such as
[14]–[19] have been investigated. In [14], Hempstalk proposed
a hiding scheme by replacing the LSB of a cover according
to the difference values between a pixel and its four touching
neighbors. Although this method can embed most secret data
along sharper edges and can achieve more visually imperceptible
stegos (please refer to Fig. 1(g) and Table I), the security
performance is poor. Since the method just modifies the LSB
of image pixels when hiding data, it can be easily detected by
existing steganalytic algorithms, such as the RS analysis (please
refer to Section IV-C1). In [15], Singh et al. proposed an embedding
method which first employs a Laplacian detector on every
3 3 nonoverlapping block within the cover to detect edges,
and then performs data hiding on center pixels whose blocks
are located at the sharper edges according to a threshold . As
mentioned in [15], the maximum embedding capacity of such
a method is relatively low . Furthermore, the
threshold is predetermined and thus it cannot change adaptively
according to the image contents and the message to be
embedded. The pixel-value differencing (PVD)-based scheme
(e.g., [17]–[19]) is another kind of edge adaptive scheme, in
which the number of embedded bits is determined by the difference
between a pixel and its neighbor. The larger the difference,
the larger the number of secret bits that can be embedded.
Usually, PVD-based approaches can provide a larger
embedding capacity (on average, larger than 1 bpp). Based on
our extensive experiments, however, we find that the existing
PVD-based approaches cannot make full use of edge information
for data hiding, and they are also poor at resisting some
statistical analyses.
One of the common characteristics of most the steganographic
methods mentioned above is that the pixel/pixel-pair
selection is mainly determined by a PRNG while neglecting
the relationship between the image content and the size of the
secret message. By doing this, these methods can spread the
secret data over the whole stego image randomly even at low
embedding rate. However, based on our analysis and extensive
experiments, we find that such embedding schemes do not
perform well in terms of the security or visual quality of the
stego images. Assuming that a cover image is made up of
many nonoverlapping small subimages (regions) based on a
predetermined rule, then different regions usually have different
capacities for hiding the message. Similar to the problem of
cover image selection [20], we should preferentially use those
subimages with good hiding characteristics while leaving the
others unchanged. Therefore, deciding how to select the regions
is the key issue of our proposed scheme. Generally, the regions
located at the sharper edges present more complicated statistical
features and are highly dependent on the image contents.
Moreover, it is more difficult to observe changes at the sharper
edges than those in smooth regions.