This paper proposes a novel blind image watermarking scheme exploiting Block Truncation Coding (BTC). Most of existing BTCbased watermarking or data hiding methods embed information in BTC compressed images by modifying the BTC encoding stage or BTCcompressed data, resulting in watermarked images with bad quality. Other than existing BTCbased watermarking schemes, our scheme does not really perform the BTC compression on images during the embedding process but uses the parity of BTC quantization data to guide the watermark embedding and extraction processes. In our scheme, we use a binary image as the original watermark. During the embedding process, the original cover image is first partitioned into nonoverlapping 4×4 blocks. Then, BTC is performed on each block to obtain its BTC quantized high mean and low mean. According to the parity of high mean and the parity of low mean, two watermark bits are embedded in each block by modifying the pixel values in the block to make sure that the parity of high mean and the parity of low mean in the modified block are equal to the two watermark bits. During the extraction process, BTC is first performed on each block to obtain its high mean and low mean. By checking the parity of high mean and the parity of low mean, we can extract the two watermark bits in each block. The experimental results show that the proposed watermarking method is fragile to most image processing operations and various kinds of attacks while preserving the invisibility very well, thus the proposed scheme can be used for image authentication.
1. Introduction
O
ver the last two decades, information hiding has become an emerging technology that embeds secret information in image
[1

3]
, video
[4

7]
or audio
[8
,
9]
. In the digital information network age, the possible applications of information hiding technologies become broader and broader, and its important branches is the digital watermark. The first application coming to mind is copyright protection of digital media. In the digital world, it is possible for almost anyone to duplicate or manipulate digital work without degrading the quality. The embedded watermark permits identification of the owner of the work. The second application is maybe in the field of data security, where watermarks can be used for certification, authentication, and conditional access. For example, certification is an important issue for official documents, and we can hide the identity number that is written in clear text on the card as a digital watermark in the identity photo; therefore switching or manipulating the identity photo will be detected. The third application is the authentication of image content. The goal of this application is to detect any alterations and modifications in an image. The paper focuses on this application.
According to the robustness together with the application of the watermarks, digital watermarking methods can be categorized into three classes: robust, fragile and multipurpose approaches. Robust watermarking approaches
[1
,
2
,
5

7
,
10

12]
are mainly designed for copyright protection where the watermark is still detectable after accidental or malicious attacks, while fragile watermarking schemes
[3
,
8
,
13
,
14]
are basically designed for integrity verification and content authentication where the slightest alteration of the image is detectable in the extracted watermark. Recently, several multipurpose watermarking methods
[4
,
15
,
16]
have been proposed to simultaneously achieve multiple purposes such as copyright protection, content authentication, image retrieval and data hiding. And the semifragile watermarking is robust to the operation on image which keeps the image content, such as image compression and image enhancement, etc. while it is fragile for the malicious modification. So it is a multipurpose approach.
In the past two decades, since more and more images have been stored in compressed formats such as JPEG and JPEG2000 or transmitted based on vector quantization (VQ) and block truncation coding (BTC), more and more scholars have been engaged in the compresseddomain watermarking schemes. Among them, VQbased and BTCbased methods are attractive for they are two famous blockbased image compression techniques with easy implementation and high efficiency. In the past ten years, several VQ based watermarking approaches
[10
,
11
,
13
,
15]
have been proposed as a special branch in the digital watermarking area, where the watermark information is embedded in codeword indices. They can be divided into three categories: robust, semifragile and multipurpose schemes. The algorithms proposed in
[10
,
11]
are robust, the method presented in
[13]
is semifragile, and the scheme provided in
[15]
is a multipurpose scheme for both copyright protection and content authentication.
In addition to VQ, BTC
[17]
is another famous blockbased lossy image compression technique. It uses a onebit quantizer to reduce the number of gray levels in each block while preserving the same mean and standard deviation. BTC is a quick, effective and simple blockbased lossy image compression technique. It has some characters such as high performance and high channel fault tolerance. And it has a great application value on realtime image transmission. Among several BTC variations, the absolute moment BTC (AMBTC)
[18]
preserves the first absolute moment instead of the standard deviation along with the mean. AMBTC is computationally simpler than the original BTC. In the past decade, several watermarking and data hiding schemes for BTC compressed graylevel images have been proposed. The first work was proposed by Lu et al.
[12]
, where the robust watermark is embedded by controlling the VQBTC encoding process according to the watermark bits. Later, Lin and Chang
[19]
put forward a data hiding approach for BTC compressed images by implementing LSB substitution operations on BTC high and low means as well as performing the minimum distortion algorithm on BTC bitplanes. Chuang and Chang
[20]
presented a hiding algorithm to embed data in the BTC bitplanes of smooth blocks. In addition, Hong et al.
[21]
, Chen et al.
[22]
, and Zhang et al.
[23]
have proposed several lossless data hiding schemes for BTCcompressed images. Recently, Yang and Lu
[14]
have proposed a fragile image watermarking scheme whose main idea is to exploit VQ or BTC to encode each block according to the corresponding watermark bit.
The previous BTCbased watermarking or data hiding approaches modify either the BTC encoding process or the BTCcompressed data according to the secret bits, and thus the quality of the watermarked image is the same as or worse than that of the BTCcompressed image. Especially, there are few BTCbased fragile image watermarking schemes that can identify the modified areas and recover the original content. To improve the watermarked image quality and not be detected by the malicious attackers, this paper proposes a new approach that does not really perform the BTC compression on the image but uses the AMBTC quantized data to guide the embedding and extraction processes, and thus lots of data can be embedded as well as a high image quality can be obtained. In addition, the proposed scheme can locate the maliciously modified locations. The rest of this paper is organized as follows. First, Section 2 briefly introduces the AMBTC technique followed by five related works to be compared in the experiments. Section 3 provides a detailed description of the proposed algorithm. Experimental results and a comparison with existing seven works are given in Section 4. Finally, Section 5 summarizes the contributions of our work.
2. Related Work
 2.1. Absolute Moment Block Truncation Coding
The absolute moment BTC (AMBTC)
[18]
is a blockbased spatial domain image compression technique. The main idea is to quantize every pixel in an image block into two levels while preserving the mean and the first absolute central moment of a block. The AMBTC method first segments the 256graylevel input image
X
into nonoverlapping small blocks, each being a
k
dimensional vector (typically
k
=4×4), namely, X={
x
_{1}
,
x
_{2}
, …,
x_{N}
}, where
N
is the number of blocks. Then, for each block
x_{i}
=(
x
_{i1}
,
x
_{i2}
, …,
x_{ik}
)
^{T}
, the AMBTC scheme separately quantizes each element in
x_{i}
into two levels such that the mean value and the first absolute central moment can be preserved in the reconstruction stage. The mean value
m_{i}
of each block
x_{i}
is taken as the onebit quantizer’s threshold, namely,
Let
q_{i}
stand for the number of pixels having value not less than
m_{i}
. Then the two output quantization level values
a_{i}
and
b_{i}
can be calculated as follows
where
a_{i}
and
b_{i}
are defined as the low mean and the high mean of block
x_{i}
, respectively. If
q_{i}
=
k
, we define
a_{i}
=
b_{i}
=
m_{i}
. After the binary quantization is performed for all the pixels in
x_{i}
, we can obtain a bitplane
p_{i}
in which ‘0’ corresponds to the pixels with values less than
m_{i}
while ‘1’ corresponds to the rest of the pixels. Thus we can use the triple (
a_{i}
,
b_{i}
,
p_{i}
) to describe the compressed version of
x_{i}
. During the decoding stage, the image block
x_{i}
can be easily reconstructed from the bitplane
p_{i}
by replacing each ‘0’ with
a_{i}
and each ‘1’ with
b_{i}
respectively.
Fig. 1
shows an example of encoding and decoding the image block
x
based on AMBTC. Obviously, for a 256graylevel 4×4sized image block, its low mean and high mean are coded separately with 8 bits each, and its bitplane needs 16 bits, so the coding bit rate is (8+8+16)/16=2bpp. Although the coding bit rate is much higher than JPEG and VQ, AMBTC can be performed very fast.
An example of encoding a block x by the triple (a, b, p).
 2.2. Chuang and Chang’s Scheme
For a smooth block
x_{i}
whose absolute distance between
a_{i}
and
b_{i}
is small, its bitplane
p_{i}
will be less significant in the AMBTC reconstruction process. Under these circumstances, some suitable locations in
p_{i}
may be replaced with the s2.3. Hong et al.’s Schemeecret bit. Based on this idea, Chuang and Chang’s scheme
[20]
embeds data in the smooth blocks’ bitplanes that are determined by the difference between
b_{i}
and
a_{i}
. If
b_{i}

a_{i}
is not greater than the preset threshold TH, then the block
x_{i}
is classified as a smooth block; otherwise, the block is a complex block. Then, a suitable location in the smooth block’s bitplane
p_{i}
is selected to be replaced with the secret bit. Obviously, the higher the threshold TH is, the more data may be hidden, but the more distortion will be introduced. The extraction process is very simple. From each compressed BTC blocks, the difference
b_{i}

a_{i}
is first calculated, and then whether the difference is less than TH or not is determined. Once confirmed, the secret bit in the bitplane
p_{i}
is extracted. One obvious drawback of Chuang and Chang’s scheme is that the capacity is determined by the number of smooth blocks.
 2.3. Hong et al.’s Scheme
As we know, if we want to exchange
a_{i}
and
b_{i}
in the compressed triple of block
x_{i}
, we only need to flip the bitplane
p_{i}
into
in order to obtain the same reconstructed block. Based on this idea, Hong et al.’s embedding scheme
[21]
can be illustrated as follows: First, all embeddable blocks with
a_{i}
<
b_{i}
are found, that is to say, the block with
a_{i}
=
b_{i}
is nonembeddable. Then, for each embeddable block, if the bit to be embedded is ‘1’, then the compressed code is changed from (
a_{i}
,
b_{i}
,
p_{i}
) into (
b_{i}
,
a_{i}
,
); otherwise, do nothing. In other words, the secret bit ‘0’ corresponds to the code (
a_{i}
,
b_{i}
,
p_{i}
) and the secret bit ‘1’ corresponds to the code (
b_{i}
,
a_{i}
,
). The extraction process is very simple. Assume we receive the code (
a_{i}
,
b_{i}
,
p_{i}
), we only need to judge the relationship between
a_{i}
and
b_{i}
. If
a_{i}
>
b_{i}
, then the secret bit is 1; if
a_{i}
<
b_{i}
, the secret bit is 0; otherwise, no secret bit is embedded. Although Hong et al.’s scheme is reversible, it does not consider hiding data in the blocks with
a_{i}
=
b_{i}
.
 2.4. Chen et al.’s Scheme
To embed secret data in the blocks with
a_{i}
=
b_{i}
, Chen et al.
[22]
proposed an improved reversible data hiding algorithm by introducing Chuang and Chang’s bitplane replacement idea to deal with the case
a_{i}
=
b_{i}
in Hong et al.’s bitplane flipping scheme. The scheme aims to get a payload higher than Hong et al.’s bitmap flipping scheme as well as a stegoimage quality improvement over Chuang and Chang’s scheme.
 2.5. Yang and Lu’s Method
The above three methods fall into the data hiding category that is a bit different from digital watermarking. Recently, Yang and Lu
[14]
have proposed a very simple BTC/VQdomain watermarking scheme. Their embedding procedure can be detailed as follows: the original image
X
is first divided into nonoverlapping blocks
x_{i}
,
i
=1, 2, …,
N
. For each image block
x_{i}
, if the watermark bit to be embedded
w_{i}
=1, then VQ is used to encode the block
x_{i}
by searching for its bestmatched codeword
c_{j}
in the codebook
C
. If the watermark bit to be embedded
w_{i}
=0, then the AMBTC is used to encode the block
x_{i}
by replacing the pixels that are not less than the mean value
m_{i}
with the high mean
b_{i}
and replacing the pixels that are smaller than the mean value
m_{i}
with the low mean
a_{i}
. Finally, all encoded image blocks are pieced together to obtain the final watermarked image
X
^{w}
. The extraction process is just the reverse process of the embedding process as follows: the suspicious image
Y
is first divided into nonoverlapping blocks
y_{i}
,
i
=1, 2, …,
N
. For each image block
y_{i}
, it is encoded with VQ and BTC to obtain their corresponding mean squared error MSE
_{VQ}
and MSE
_{BTC}
respectively. If MSE
_{VQ}
< MSE
_{BTC}
, then the extracted watermark
w_{i}
=1. If MSE
_{VQ}
> MSE
_{BTC}
, then the extracted watermark bit
w_{i}
=0. If MSE
_{VQ}
= MSE
_{BTC}
, then
w_{i}
is randomly set to 0 or 1. All obtained watermark bits are pieced together to obtain the final extracted watermark
W
^{e}
.
 2.6. Zhang et al.’s Scheme
Zhang et al.
[23]
have proposed an oblivious fragile watermarking scheme for images utilizing edge transitions in BTC bitmaps. In the embedding process of the scheme, the original image was partitioned into nonoverlapping 4×4 blocks, then the bitmap of each block was been obtained with BTC. The embedding watermark bit was been used to guide to modify the pixel values until the parity of the number of horizontal edge transitions in the bitmap of the modified block is equal to the embedding watermark bit. Similarly, in the extraction process, only to check the parity of the number of horizontal edge transitions in the BTC bitmap of each nonoverlapping 4×4 blocks. Then one watermark bit would be extracted from each block.
3. Proposed Watermarking Scheme
 3.1. Preprocessing Stage
From the above discussion, we can see that the existing BTCbased information hiding schemes actually embed the information in the BTCcompressed image. Thus, the watermarked image is usually with a poor quality. In order to improve the image quality, this paper proposes a new train of thought. We still exploit the BTC, but we do not perform the watermark embedding on the BTC compressed image but use the BTC quantization data to guide the watermark embedding and extraction process. The proposed algorithm consists of three processes, namely, the preprocessing stage, the watermark embedding stage and the watermark extraction stage, as shown in
Fig. 2
.
The block diagram of the proposed watermarking scheme.
From
Fig. 2
, we can see that the preprocessing stage is the common stage for both watermark embedding and extraction processes. The purpose of this stage is to achieve the BTC quantization data as the control parameter. Assume the input image is
X
or
Y
consisting of
N
blocks and the watermark is a binary image
W
with 2
N
bits. Thus, the preprocessing stage is just a BTC coding process as follows:
Step 1: The input image
X
(or
Y
) is divided into nonoverlapping
k
dimensional blocks
x_{i}
(or
y_{i}
),
i
= 1,2,…
N
.
Step 2: Each image block
x_{i}
(or
y_{i}
) is encoded by AMBTC, obtaining its mean value
m_{i}
and its two quantization levels
a_{i}
and
b_{i}
.
Step 3: All means are composed of the mean sequence
M
= {
m_{i}

i
= 1,2,…,
N
}, all low means are composed of the low mean sequence
A
= {
a_{i}

i
= 1,2,…,
N
} high means consist of the high mean sequence
B
= {
b_{i}

i
= 1,2,…,
N
}
 3.2. Watermark Embedding Stage
With the means
M
= {
m_{i}

i
= 1,2,…,
N
} the low means
A
= {
a_{i}

i
= 1,2,…,
N
} and high means
B
= {
b_{i}

i
= 1,2,…,
N
} in hand as the guider, now we can describe our watermark embedding process. Before embedding, we perform the raster scanning on the original watermark
W
, obtaining the watermark bit sequence
W
= {
w
_{1}
,
w
_{2}
,…,
W
_{2N}
}. The purpose of our embedding process is to ensure the blind extraction such that the watermark can be extracted from the watermarked image only based on its two quantization values
a_{i}
and
b_{i}
.
Here, a function
Parity
(
x
) is defined as follows:
The central idea is to force the parity of
a_{i}
and the parity of
b_{i}
to be equal to the two watermark bits. That is, two watermark bits will be embedded in each block. The embedding process can be illustrated detailedly as follows:
Step 1: The original image
X
is divided into nonoverlapping
k
dimensional blocks
x_{i}
,
i
= 1,2,…,
N
.
Step 2: The embedding process is performed block by block. For each block
x_{i}
, the two watermark bits to be embedded are
w
_{2i1}
and
w
_{2i}
, the embedding procedure is controlled by the quantization data
a_{i}
and
b_{i}
as following substeps.

Substep 2.1: We count the number of pixels that are not less thanmiand denote it asqi, thus the number of pixels that are less thanmiisk−qi. Obviously, we have 1≤qi≤k.

Substep 2.2: We find all the pixels inxithat are not less thanmi, and then arrange them in the descending order, obtaining a ‘high’ setH. Similarly, we find all the pixels inxithat are less thanmi, and then arrange them in the descending order, obtaining a ‘low’ setL.

Substep 2.3: If bothParity(ai) =w2i1and Parity(bi) =w2iare satisfied, we do not change any pixel in the blockxi, and go to Substep 3. Otherwise, we go to Substep 2.4.

Substep 2.4: We change some pixels in thexiin order to force the two equations Parity(âi)=w2i1and Parity()=w2iare satisfied for the modified block. There are two cases:

Case 1:ai=bi=mi(equivalently,qi=k). In fact, this case corresponds to a block with uniform pixel values. There are three subcases as follows.

Subcase 1.1: Parity(ai)≠w2i1but Parity(bi)=w2i. Ifai>0, we subtract 1 from each of the first half pixels in the block. Otherwise, we add 1 to each of the first half pixels in the block and add 2 to each of the remaining pixels.

Subcase 1.2: Parity(ai)=w2i1but Parity(bi) ≠w2i. Ifai<255, we add 1 to each of the first half pixels in the block. Otherwise, we subtract 1 from each of the first half pixels in the block and subtract 2 from each of the remaining pixels.

Subcase 1.3: Parity(ai)≠w2i1but Parity(bi)≠w2i. If 0

Case 2:ai

Subcase 2.1: Parity(ai)≠w2i1but Parity(bi)=w2i. In this case, we perform the following modification rule:

If≥kqi, we perform> =xij 1 for all {xijxij>0,xij∈L} repeatedly untilis satisfied. Thus, we haveâi=ai1 and thereforeParity(ai) is changed whileParity(bi) is still preserved.

Else if

Else ifai=0 andbi>1, we set= 1 for allxij∈L. Thus, we haveâi=1=ai+1, and thereforeParity(ai) is changed whileParity(bi) is still preserved.

Else ifai=0 andbi=1, we set= 1 for allxij∈Lset=xij+ 2 for allxij∈H. Thus, we haveâi=1=ai+1, and=bi+2 and thereforeParity(ai) is changed whileParity(bi) is still preserved.

Subcase 2.2: Parity(ai)=w2i1but Parity(bi) ≠w2i. In this case, we perform the following modification rule:

Ifwe perform=xij+1 for all {xijxij<255,xij∈H} repeatedly untilThus, we have=bi+1 and thereforeParity(bi) is changed whileParity(ai) is still preserved.

Else ifandbi=254, we set= 255 for allxij∈H. Thus, we have=255=bi+1 and thereforeParity(bi) is changed whileParity(ai) is still preserved.

Else ifbi=255 andai<254, we set=254 for allxij∈H. Thus, we have=254=bi1 and thereforeParity(bi) is changed while Parity(ai) is still preserved.

Else ifbi=255 andai=254, we set= 254 for allxij∈Hand set=xij 2 for allxij∈L. Thus, we have=254=bi1 andâi=ai2 and thereforeParity(bi) is changed whileParity(ai) is still preserved.

Subcase 2.3:Parity(ai) ≠w2i1andParity(bi) ≠w2i. In this case, the above two rules of Subcase 2.1 and Subcase 2.2 will be performed.
Step 3: If every block has been embedded with 2 watermark bits, then the algorithm is terminated with the watermarked image
X
^{w}
. Otherwise, go to Substep 2.1 for next block.
In order to understand the proposed embedding process more clearly, two concrete examples are described in
Fig. 3
. For the uniform block
x
_{1}
, assume the two watermark bits to be embedded are
w
_{1}
=1 and
w
_{2}
=0, since
a
_{1}
=
b
_{1}
=2, thus Parity(
a
_{1}
)≠
w
_{1}
and Parity(
b
_{1}
)=
w
_{2}
, and it falls into Subcase 1.1. For block
x
_{2}
, assume the two watermark bits to be embedded are
w
_{3}
=1 and
w
_{4}
=1, since
a
_{2}
=4 and
b
_{2}
=11, thus Parity(
a
_{2}
)≠
w
_{3}
and Parity(
b
_{2}
) =
w
_{4}
, and it falls into Subcase 2.1.
Concrete embedding examples.
 3.3. Watermark Extraction Stage
According to
Fig. 2
(b) , we can see that our watermark extraction process is a blind process such that we do not require the original image during the extraction process. It is very simple because we can get the two watermark bits from each block only based on checking the parities of
a_{i}
and
b_{i}
. Before extraction, the same preprocessing step is performed on the suspicious image
Y
to obtain its low mean sequence
A
={
a_{i}

i
=1,2,…,
N
} and high mean sequence
B
={
b_{i}

i
=1,2,…,
N
}. For each block
y_{i}
, if
a_{i}
is odd, then we can extract the watermark bit
w
_{2i1}
=1. Otherwise, we can extract the watermark bit
w
_{2i1}
=0. Similarly, if
b_{i}
is odd, then we can extract the watermark bit
w
_{2i}
=1. Otherwise, we can extract the watermark bit
w
_{2i}
=0. After all blocks are performed, we piece these bits together to obtain the final extracted watermark
W
^{e}
={
w
_{e1}
,
w
_{e2}
,…,
w
_{e2N}
}.
4. Experimental Results and Analysis
 4.1. Performance Testing
To evaluate the performance of the proposed watermarking scheme, the 256 grayscale 512×512 sized Lena image is used for watermarking. The Lena image is divided into 16384 blocks of size 4×4. A binary image of size 256×128 serves as the watermark W for embedding.
Fig. 4
(a) shows the original Lena image and
Fig. 4
(b) shows the watermarked Lena image with PSNR=51.11dB. To check the fragility of our algorithm, we perform several attacks on the watermarked image, including brightness enhancement by 10%, contrast enhancement by 10%, JPEG compression with QF=100%, JPEG compression with QF=80%, image cropping in the middle part of the image, blurring with a 3×3 window and a threshold 25, median filtering with a 3×3 window, and rotation by 1˚. The attacked images are shown in
Fig. 5
(a)(h). The corresponding extracted watermarks are shown in
Fig. 6
(c)(j), while the original watermark is shown in
Fig. 6
(a) and the extracted watermark under no attacks is shown in
Fig. 6
(b). The performance of extracted watermarks are evaluated by normalized crosscorrelation (NC). From these results we can see that the proposed algorithm is fragile to most attacks except for the change in brightness which is caused by the AMBTC’s special ability in preserving the mean value and the first absolute central moment. In that the texture of the image belongs to the part of the ownership and the operation on brightness enhancement can not change the texture of the image, so our proposed scheme can be used for texture authentication. From
Fig. 5
(e) and
Fig. 6
(g), we can see that our scheme can locate the maliciously modified locations. In addition, the proposed algorithm is blind because the original image is not required during the extraction process.
The cover and watermarked images.
The attacked watermarked images under 8 attacks, namely, brightness enhancement, contrast enhancement, JPEG (QF=100), JPEG (QF=80), cropping, blurring, median filtering, and rotation, respectively.
The original watermark, the extracted watermark from unattacked watermarked image and extracted watermarks under various attacks, namely, brightness enhancement, contrast enhancement, JPEG (QF=100), JPEG (QF=80), cropping, blurring, median filtering, and rotation, respectively.
In addition, we have two methods to improve the security of our proposed scheme. One method is to keep the size of the nonoverlapping kdimensional blocks as a key. The other method is to select the different position from the image as the first block’s lefttop pixel, and the different position is a key.
 4.2. Performance Comparison
To show the superiority of our proposed algorithm, we use six test images, Lena, Peppers, Mandrill, Boat, Goldhill and Jet_F16, of the same size 512×512 with 256 grayscales, as shown in
Fig. 7
. Comparisons among our algorithm, Chuang and Chang’s algorithm, Hong et al.’s algorithm, Chen et al.’s scheme, Yang and Lu’s Method and Zhang et al.’s scheme which are described in Section 2 are performed under the same block size 4×4. Two aspects of performance are considered, namely, the peak signal to noise ratio (PSNR) representing the quality of the watermarked image, and the capacity representing the maximum number of secret bits that can be hidden. Here, although the capacity is not a very important index for a watermarking scheme, but we would like to demonstrate that our scheme can hide large number of bits while preserving high image quality compared to existing BTCbased schemes.
Six test images.
As shown in
Table 1
, the PSNRs of watermarked images based on our scheme are much higher than those of the existing schemes. With respect to the embedding capacity, compared with existing schemes, our scheme can embed 2bits per block, which is at least double capacity of the existing schemes. Taking the above two attributes into comprehensive consideration, the proposed scheme is a better method for its high capacity and high PSNR.
Comparisons between the proposed and existing BTCbased hiding schemes (k=4×4)
Comparisons between the proposed and existing BTCbased hiding schemes (k=4×4)
We also compare our scheme with two classical LSBbased schemes, one is proposed by Mielikainen
[24]
, the other is proposed by Zhang and Wang
[25]
. In Mielikainen’s scheme, he proposed a modified LSB matchingbased steganographic method for embedding message bits into a still image. The embedding operation is performed on every two pixels. The LSB of the first pixel carries one bit of information, and another bit of information is presented by a function of the two pixel values. Their method made fewer changes to the cover images comparing to LSB matching and improved the image quality. However, there was a problem to handle with the saturated pixels, i.e., the pixels with value‘0’ or ‘255’ in the 256grayscale image. That is, the number of nonsaturated pixels is the capacity of this scheme. In Zhang and Wang’s scheme
[25]
, a secret digital in (2
n
+1)ary notational system is embedded into an
n
pixels group, in which only one pixel is increased or decreased by 1. In our experiment, we set
n
=54, then we have the fixed capacity 32856(=ln109/ln2*512*512/54). The comparison results are shown in
Table 2
. From this table, we can see that the two LSBbased schemes have better performance than our scheme in terms of PSNR and capacity, for they are pixelbased method and only modify the LSB of the pixel to be embedded, while our scheme is blockbased and we modify more than one pixel in each block. However, our scheme is a semifragile scheme that is robust to brightness or contrast changes, thus our scheme has some special characteristics that the LSBbased schemes do not have. In addition, we provide a new watermarking mechanism of pixeldomain embedding followed by compresseddomain analysis and extraction.
Comparisons between the proposed and recent two LSBbased watermarking schemes
Comparisons between the proposed and recent two LSBbased watermarking schemes
5. Conclusion
Considering the existing BTCbased watermarking or data hiding schemes obtain lowquality images or have less hiding capacity, in this paper, we propose a new blind fragile image watermarking approach by exploiting the AMBTC technique to guide the watermark embedding and extraction processes. It is a simple but efficient method suitable for texture authentication and data hiding. The central idea is to force the parity of the low mean and high mean of each block to be equal to the two watermark bits to be embedded respectively. Another contribution of this paper is that we have developed a semifragile watermarking scheme with the mechanism of pixeldomainbased embedding and compresseddomainbased extraction, which can make full use of the statistical characteristics of the compresseddomain. Experimental results show that it is fragile to most kinds of attacks and it is better than some existing BTCbased image watermarking schemes. The future work will be concentrated on how to further improve the capacity while maintaining the high image quality and how to recover the maliciously modified blocks.
BIO
Dongning Zhao received her B.S. and M.S. in communication engineering from Nanjing Institute of Communication Engineering, Nanjing, China, in 2001 and 2004, respectively. She is currently working toward the Ph.D. degree with ATR National Defense Technology Key Laboratory, College of Information Engineering, Shenzhen University. Her research interests include multimedia security, information hiding and digital watermark.
Weixin Xie received his B.S. from Xi’an Military Telecommunication Engineering Institute, Xi’an, China, in 1965. From 1981 to 1983, he was a visiting scholar with University of Pennsylvania, Philadelphia, USA. And from 1989 to 1990, he was a visiting professor with University of Pennsylvania, Philadelphia, USA. He is currently a professor with ATR National Defense Technology Key Laboratory, College of Information Engineering, Shenzhen University. He has published more than 100 papers in the journals. His research interests include intelligent information processing and sensor network.
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