Robust Imperceptible Video Watermarking
for MPEG Compression and DA-AD Conversion
Using Visual Masking
Sang-Keun Ji, Wook-Hyung Kim, Han-Ul Jang, Seung-Min Mun,
and Heung-Kyu Lee(B)
School of Computing, Korea Advanced Institute of Science and Technology,
Daejeon, Republic of Korea
{skji,whkim,hanulj,smmun,hklee}@mmc.kaist.ac.kr
Abstract. In this paper, we propose a robust and invisible video watermarking scheme. To ensure robustness against various non-hostile disturbances which can occur during the distribution of digital content,
the proposed system selects certain blocks using a robust and imperceptible block selection scheme and watermarks are embedded into these
blocks using spread-spectrum watermarking in DCT domain. In addition, visual masking is applied to the watermarking embedding process
for high invisibility. Our system is designed to extract 16 bits data in any
15-second interval. Experimental results show that the proposed system
offers high invisibility and that it is robust against MPEG-4 compression
and DA-AD conversion.
Keywords: Video watermarking
conversion · Visual masking
1
·
Video compression
·
DA-AD
Introduction
Digital watermarking is a technique to embed an imperceptible message in the
digital cover works such as audio, or video, etc. This watermarking scheme is
mainly used for copyright protection [1]. The main properties of digital watermarking are robustness, imperceptibility, and payload.
There are various non-hostile disturbances that threaten to degrade the
robustness of digital video. These include such as noise addition, compression,
and digital/analogue - analogue/digital (DA-AD) conversion [2]. The latter, in
particularly, can have such strong effect that it makes other copyright protection
techniques, such as digital rights management (DRM), ineffective. Moreover, digital video watermarking should be robust to DA-AD conversion because if it is
not, copy-protected videos could easily be duplicated using analogue means [3].
There have been proposals for robust watermarking schemes against DA-AD
conversion attack in several papers. Lubin et al. created a watermark pattern at
very low frequency and embedded the pattern in both space and time domains
c Springer International Publishing Switzerland 2016
Y.-Q. Shi et al. (Eds.): IWDW 2015, LNCS 9569, pp. 285–298, 2016.
DOI: 10.1007/978-3-319-31960-5 24
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satisfying fidelity, robustness, and security [4]. Lee et al. designed a robust watermarking method where the watermark pattern was low-pass filtered because low
frequency is less influenced by common signal processing, especially by DA-AD
conversion [5].
Imperceptibility is one of the main goals of digital watermarking and it
means that the embedded watermark creates insignificant changes to the cover
work which cannot be perceived by human visual system (HVS). Generally, high
imperceptibility is required to provide high quality watermarked content.
Niu et al. proposed visual-saliency-based watermarking to provide high
imperceptibility [8]. Visual saliency indicates the degree and location to which
human visual attention is most attracted [6]. Kim et al. designed a digital video
watermarking scheme using an HVS masking function for high robustness and
high invisibility [9]. Kutter et al. presented a watermarking method where the
blue channel was the watermark embedding domain [10]. They employed the
characteristic that human eyes are least sensitive to the blue channel.
In this paper, we propose a video watermarking system with high imperceptibility and robustness against MPEG-4 compression and DA-AD conversion.
In order to satisfy these requirements, our system selects significantly imperceptible blocks using a robust and imperceptible block selection method, and a
watermark is embedded into these blocks using spread-spectrum watermarking
in DCT domain. For robustness against MPEG-4 compression and DA-AD conversion, the watermark should be strongly embedded, though this can decrease
the degree of invisibility. Therefore, visual masking is applied to watermarked
blocks for high invisibility and a watermark is only embedded in the blue channel of RGB channel because human eyes are less sensitive to changes in the
blue channel [10]. In the proposed system, the changes caused by the watermark are not noticeable by human eyes because the robust and imperceptible
block selection scheme along with visual masking were applied to ensure good
invisibility.
The rest of the paper is organized as follows. In Sect. 2, The proposed watermarking system is described. In Sect. 3, the experimental setup and results are
shown and Sect. 4 concludes.
2
Proposed Method
The overall process of the proposed method consists of watermarking embedding
and extraction process as shown in Figs. 3 and 5. Watermarking embedding
process consists of visual masking, the robust and imperceptible block selection,
data encoding and watermark embedding.
2.1
Visual Masking
Generally, there is a trade-off between the robustness and the imperceptibility of
watermarking. In the embedding process, the stronger the watermark, the less
imperceptible the watermarked content, and vice versa. However, the strength
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Fig. 1. Examples of Visual Masking
of the watermark can be maintained and imperceptibility can be increased if
HVS is used. Watermarks are normally not embedded at low frequencies because
changes at a low frequency are significantly more visible than those done at a high
frequency. Thus, visual masking is utilized to embed high-strength watermarks
in less perceptible areas, while low-strength watermarks are embedded in more
perceptible areas. We describe the visual masking schemes used to improve the
watermarking performance of the proposed method in this section.
Visual Saliency Model. There are various approaches which can be used to
model human visual characteristics, as human visual characteristics are highly
complex. Particularly, the visual saliency model (VSM) is a scheme employing the area which are highly attractive to human visual attention. The visual
saliency model creates a saliency map using features such as color, intensity, and
orientation. In this paper, we create a visual saliency map using Graph-Based
Visual Saliency (GBVS), as proposed by Harel et al. [7]. GBVS is a simple and
biologically plausible bottom-up visual saliency model which uses a graph-based
random walk to reflect human visual characteristics.
The process of applying GBVS is as follows. First, the saliency map of a
frame is calculated using GBVS. The saliency map tends to be concentrated in
a particular range, as shown in Fig. 1(b). For the human visual system in the
saliency map, segmentation depending on the distribution of the values on the
map is important as opposed to the values themselves. For the segmentation of
the saliency map, the cumulative distribution function (CDF) of the saliency
map is calculated, and the saliency strength map is then computed considering
the CDF, as shown below:
⎧
⎨ α1 , F (px,y ) < 0.5
vsm
= α2 , 0.5 ≤ F (px,y ) < 0.8
(1)
αx,y
⎩
α3 , 0.8 ≤ F (px,y )
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where px,y is the value at (x, y) of the saliency map, and F is the CDF of the
vsm
saliency map. α1 , α2 , and α3 are the watermark strength constants, and αx,y
is the value at (x, y) of the saliency strength map. The saliency strength map
represents the weight map in the watermark embedding process based on the
visual saliency model.
Noise Visibility Function. Voloshynovskiy et al. proposed the HVS function
based on the computation of a noise visibility function (NVF) that characterizes
the properties of the local image by identifying textured and edge regions [11].
Watermarks are strongly embedded at less attractive regions, while they are
lightly embedded at more attractive regions using NVF. However, this method
has the disadvantage in that its complexity is very high. To overcome the complexity problem, our system computes a noise map using a simplified NVF with
a 2-D linear separable filter with low complexity as proposed by Kim et al. [9].
In addition to this, human eyes are less attracted to moving objects such
as driving cars. Thus, a motion map is computed using the differences between
video frames based on the time domain, as shown in Fig. 1(e). Then, a binary
motion map is calculated considering a predefined threshold. For example, the
binary motion value of (x, y) is set to 1 when the motion value of (x, y) is higher
than the threshold; otherwise, the value is set to 0. As shown in Fig. 1(f), a
noise map and a binary motion map are finally combined to increase the both
watermark embedding regions and strength. A combined noise map is used as the
weight map in the same manner as a saliency strength map. To use these maps
as the weight map, the values of the saliency strength map and the combined
noise map are normalized so that they are between 0 and 1.
2.2
Robust and Imperceptible Block Selection
In the proposed method, the watermark is embedded by means of visual masking, as described above. However, visual masking has a limitation in that the
watermark extraction accuracy is seriously low when the masking value is too
low. For example, the masking value is very low at an area of high attention
and a flat area, and this causes the watermark signal to be weak. Therefore, a
robust and imperceptible block selection scheme is proposed to overcome this
limitation of visual masking. The scheme employs the blocks with the highest
masking values to improve the degree of imperceptibility and minimize the data
loss caused by compression.
The selection process of a robust and imperceptible block is as follows. First,
an image is divided into blocks and a NVF map is calculated at every block.
Then, m blocks with the highest intensity of a noise map are selected. A saliency
strength map is calculated at m blocks and k blocks with the highest strength
are selected, where k is the number of necessary blocks to embed watermarks,
with k being smaller than m. These selected blocks are more imperceptible than
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other blocks because visual masking is applied. Moreover, these blocks are more
robust against the data loss as compared to other blocks because the watermark
strength of the selected blocks is higher than that of the other blocks.
An example of the correlation of the blocks in uncompressed and compressed
videos is in Fig. 2. In this example, the frame was divided into 16 blocks, and
the watermarked video explained in Sect. 3.1 is compressed by MPEG-4 part10
(H.264) with 1,200 Kbps. Red dots represent 5 blocks with the highest strength
using a robust and imperceptible block selection method, and blue dots represent
5 blocks with the lowest strength. As shown in Fig. 2, blocks with the highest
strength tend to have a higher correlation compared to other blocks.
Fig. 2. An example of the correlation of the blocks (Colour figure online)
The watermark accuracy and robustness increase when k increases; however, the imperceptibility decreases. In contrast, the watermark accuracy and
robustness decrease when k is low. Therefore, we experimentally determined the
number of necessary blocks and obtained high robustness and high imperceptibility.
2.3
Data Encoding
In the proposed system, watermark patterns are designed to represent 16 bits
data. To this end, two sets, X and Y , of pseudo-random sequences are used as
the watermark. Each set is composed of 256(= 28 ) pseudo-random sequences to
represent 8 bits data, as expressed by the following equation.
X = {X0 , X1 , X2 , ... , X255 },
Y = { Y0 , Y1 , Y2 , ... , Y255 }.
(2)
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To represent 16 bits data, the sequences Xi and Yj are selected from X and
Y . Here, Xi represents the upper 8 bits data and Yj represents the lower 8
bits data. By attaching them, two pseudo-random sequences can present 16 bits
data. For example, if the data is 0x00F F , then Xi corresponds to a sequence
representing 0x00 from X and Yj corresponds to a sequence representing 0xF F
from Y . Each pseudo-random sequence of Xi and Yj is an N -length pseudorandom sequence, as determined by the equation below.
Xi = {xi1 , xi2 , ..., xiN },
Yj = {yj1 , yj2 , ..., yjN }.
2.4
(3)
Watermark Embedding
In the proposed watermarking embedding scheme, there are two main steps.
First, blocks are selected using the robust and imperceptible block selection
method. Then, the watermarking embedding process is then used for each
selected block. The overall process of watermarking embedding is shown in Fig. 3.
Fig. 3. Watermark embedding process
First, the blocks which are used for watermark embedding are selected by
means of robust and imperceptible block selection for invisibility. Then, two
pseudo-random sequences Xi and Yj are embedded into each half of the selected
blocks. In the proposed method, the selected blocks are sorted according to visual
masking values, and the sequence Xi is embedded into odd-ordered blocks, with
Yj embedded into even-ordered block.
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For each of the selected blocks, the watermark embedding precess is performed as follows. First, spread-spectrum watermarking in the DCT domain
[12] is used for robustness. In each block, the discrete cosine transform is calculated and the vector V having coefficients of length N is extracted from a fixed
point. A pseudo-random sequence is then embedded into the vector V via the
following equation:
(4)
vi = vi + α|vi |wi , (0 ≤ i ≤ N )
where Vi = {v1 , v2 , ..., vn } is the vector of the original coefficients and Vi =
{v1 , v2 , ..., vn } is the vector of the watermarked coefficients. α is the strength of
the watermark signal. W = {w1 , w2 , ..., wn } is a pseudo-random sequence from
X or Y in the selected block. In the DCT domain, coefficients of length N are
selected from the mid-frequency coefficients for robustness of compression and
for good visual quality. After embedding a sequence, inverse DCT is performed
and the watermarked block B is obtained. An example of a watermarked frame
is in Fig. 4. In Fig. 4, the number of blocks is 16 and the number of selected blocks
k is 6, a sequence Xi is embedded into blocks with red line, and a sequence Yj is
embedded into blocks with blue line. White regions of left-top corner indicates
watermark embedding regions for each block.
Fig. 4. An example of watermark embedding in selected blocks (Colour figure online)
Finally, visual masking is applied to the watermarked blocks to enhance the
invisibility. The visual masking method is
Bs = Is ∗ B + (1 − Is ) ∗ B,
Bn = In ∗ B + (1 − In ) ∗ B,
Bw = (Bs + Bn )/2
(5)
where B denotes the original block of the selected block and B is the watermarked block of the selected block. In addition Is and In represent the value of
the saliency map and the combined noise map, respectively. The bigger these
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values are, the stronger the watermarks are embedded. Finally, the masked
watermarked block Bw is the average of Bs and Bn . Masking using a combined noise map can achieve high transparency, but it incurs a large amount of
information loss, which can decrease the robustness. In contrast, masking using a
saliency strength map inserts the watermark more strongly compared to the use
of a combined noise map. Therefore, masking is better when these two characteristics are balanced by averaging. This embedding process is repeated in each
selected block. A frame is then reconstructed by combining the selected blocks
Bw and the unselected blocks, and this process is repeated for each frame.
The proposed method selects the blocks in which to embed watermarks,
and it provides higher imperceptibility than a method which uses all of the
blocks. The watermarks are embedded strongly to prevent any decreases in the
robustness; however, the traces are imperceptible due to visual masking. Also,
watermark losses are minimized because blocks with low masking values are
excluded. Therefore, the proposed method achieves high robustness and high
imperceptibility.
2.5
Watermark Extraction
The watermark extraction process is shown in Fig. 5. First, a frame is divided
into blocks and the DCT coefficients of each block are calculated. Then, an N length vector V ∗ is extracted from the DCT coefficients at a fixed point. During
the watermark extraction process, a correlation is used to determine whether
a watermark is embedded or not. The correlation is computed between vector
V ∗ and all sequences from the two sequence sets X and Y via the following
equation:
N
1 W ·V∗
=
z=
wi · vi∗ .
(6)
N
N i=1
where z is the correlation value and W is a pseudo-random sequence from X
and Y .
By comparing the correlation z and the threshold, the presence or absence
of the watermark W is determined. The threshold is calculated as
T =
N
1 ∗
|v |.
15 · N i=1 i
(7)
The threshold is determined by experimental results to minimize a falsepositive rate.
After correlation computation of each block, the existence of a watermark is
determined by the following watermark extraction rule. The result of watermark
extraction of a frame is calculated using the extraction results of all blocks in a
frame. The watermark extraction rule of a frame is as follows.
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Rule 1. Watermark extraction rule of a frame
if (The total number of detected blocks != 2)
then Watermarks are not detected
else (The total number of detected blocks == 2)
if two watermarks are in the same set of watermark
then Watermarks are not detected
else (one is in the set of X and the other is in the set of Y )
then Watermarks are detected
end
end
For example, if sequences that are over the threshold are X10 , X20 , Y128 , it means
that a watermark is not detected. Also, if sequences that are over the threshold are Y48 , Y241 , it describes that a watermark is not detected. In contrast, if
sequences that are over the threshold are X149 , Y214 , it means that a watermark
is detected. In this way, the watermark extraction rule of a frame is applied to
each frame.
Fig. 5. Watermark extraction process
3
3.1
Experimental Results
Experimental Environment
Experimental Setup. We simulated our method using the sample video set
IWIHC provided, as shown in Fig. 7. (display resolution: 1920 × 1080, 30 frames
per seconds, 30 s, 12 Gbps, 8bits depth uncompressed AVI files) The proposed
watermarking algorithm was implemented in the MATLAB R2013a environment, and a computer of Intel i7-4770K (3.50 GHz) with 16 GB main memory
was used to evaluate the performance. We used ‘Fosmon HDMI to component
video (YPbPr) / VGA & SPDIF output converter box’ that supports 1280 × 720
resolution as a digital-to-analogue (DA) converter and ‘skyHD captureX HDMI’
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that supports real-time full high definition (FHD) recording as an analogueto-digital (AD) converter. The important point is that the video after DA-AD
conversion has different resolution from that of the original video since the maximum resolution of a DA converter is 1280 × 720.
The number of a robust-and-imperceptible block k and the number of the
selected block in a noise map m were experimentally set to 6 and 8 respectively. α1 , α2 and α3 used in Sect. 2.1 were set to 0.7, 0.6 and 0.5. Also, 25, 000
coefficients represented by N in Sect. 2.3 were selected at the fixed location in
the DCT domain. The determined coefficients were robust to scaling caused by
DA-AD conversion because the coefficients were used at middle frequency. The
experiments were implemented to test the visual quality and robustness. We
used peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) [13]
to evaluate imperceptibility, and we used bit error rate (BER) to evaluate the
watermarking performance.
The proposed watermarking scheme is designed to have robustness to MPEG4 compression and DA-AD conversion. The overall process of experiments is as
shown in Fig. 6. First, we embedded watermarks into videos using the proposed
watermark embedder and compressed the watermarked video using MPEG-4
part10 (H.264) codec that FFmpeg provided. Then, the compressed video was
transformed to analogue signal by a DA converter. The input of a DA converter
was a digital signal through high definition multimedia interface (HDMI) and
the output was an analogue component video formed by analogue color space
YPbPr through a component cable. After that, we converted an analogue YPbPr
signal to a digital signal using an AD converter and saved the digital signal as
uncompressed YUV422 format. Robustness to MPEG-4 compression and DAAD conversion was evaluated calculating BER of the digital signal. We embedded
the same watermark with 16 bits to all frames in 15 s.
Fig. 6. Test process in the proposed watermarking system
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Fig. 7. Compressed original images (first row), and compressed watermarked videos
for α = 0.4 (second row) (bit rate = 12,000Kbps, compressibility = 1/100)
3.2
Image Quality
We performed experiments to determine the relation between the robustness and
an image quality in the proposed watermark embedding algorithm by changing
various watermark strengths α.
To evaluate image quality, the following process was performed, as shown in
Fig. 6. The watermarked video and the original video were compressed with the
same bit rate by using the MPEG-4 part 10 (H.264) codec. In this paper, the size
of the compressed bit stream were set to have 1/100 of the original bit stream.
After that, two compressed video were decompressed and the luminance channel
Y was calculated from the RGB channel according to the following equation.
Y = 0.7152G + 0.0722B + 0.2126R
(8)
Then, the PSNR and SSIM were calculated for each pair of luminance channels.
Because the original video has a bit rate of 12 Gbps, the watermarked video
was compressed to have a bit rate of 12 Mbps, which is 1/100 of the bit rate of
the original video. Also, the BER(Bit Error Rate) was calculated to represent the
relation between image quality and the robustness to compression. For exact data
extraction without errors, BER should be zero during the watermark extraction
process. We consider the results having a non-zero BER as a failure in watermark
extraction.
Table 1 shows the PSNR, SSIM, and BER results at various watermark
strengths α for MPEG4 compression with 12,000Kbps. As results, there is no
bit error if α is larger than or equal to 0.3. When the watermark is robust to
MPEG-4 compression with a 12,000Kbps bit rate, the minimum of an average
PSNR is 43.92dB and the minimum of an average SSIM is 0.9965. In the case of
the ‘Walk2’ video, there is a great deal of data loss in MPEG-4 compression and
the PSNR and SSIM are lower than for other videos because the textured and
edge regions are larger than those of other videos. Also, the experiment shows
that there is little change in the PSNR and SSIM with increasing α because of
using a robust and imperceptible block selection method.
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Table 1. Test Result at various watermark strength α for MPEG-4 with 12,000 Kbps
WM strength α
PSNR(dB)
Basketball
SSIM
BER
PSNR(dB)
Lego
SSIM
BER
PSNR(dB)
Library
SSIM
BER
PSNR(dB)
Walk1
SSIM
BER
PSNR(dB)
Walk2
SSIM
BER
PSNR(dB)
average
SSIM
BER
0.2
46.69
0.9977
0.00
43.72
0.9961
0.00
43.76
0.9965
×
46.52
0.9982
0.00
39.17
0.9942
×
43.97
0.9965
×
0.3
46.63
0.9977
0.00
43.67
0.9960
0.00
43.72
0.9965
0.00
46.44
0.9982
0.00
39.11
0.9941
0.00
43.92
0.9965
0.00
0.4
46.57
0.9976
0.00
43.63
0.9960
0.00
43.68
0.9964
0.00
46.33
0.9981
0.00
38.99
0.9940
0.00
43.84
0.9964
0.00
0.5
46.50
0.9976
0.00
43.60
0.9960
0.00
43.63
0.9964
0.00
46.22
0.9981
0.00
38.83
0.9938
0.00
43.75
0.9964
0.00
0.6
46.43
0.9976
0.00
43.54
0.9959
0.00
43.57
0.9964
0.00
46.10
0.9981
0.00
38.70
0.9936
0.00
43.67
0.9963
0.00
0.7
46.35
0.9975
0.00
43.49
0.9959
0.00
43.50
0.9963
0.00
45.98
0.9980
0.00
38.53
0.9934
0.00
43.57
0.9962
0.00
1.0
46.11
0.9974
0.00
43.30
0.9957
0.00
43.22
0.9962
0.00
45.52
0.9978
0.00
37.94
0.9926
0.00
43.22
0.9959
0.00
1.5
45.64
0.9972
0.00
42.93
0.9955
0.00
42.67
0.9958
0.00
44.64
0.9975
0.00
37.16
0.9911
0.00
42.61
0.9954
0.00
× : watermark is not detected
3.3
Robustness
To protect the copyrights of the digital HDTV contents, a watermarking scheme
should be robust to various attacks such as compression and DA-AD conversion.
Also, during DA-AD conversion, the scaling of frames inevitably occurs. In the
proposed watermarking embedding algorithm, we select middle-low frequencies
to embed the watermark and the watermarked is embedded the fixed points
of DCT coefficients. Therefore, the proposed method is robust to attacks that
eliminate high frequencies, such as compression and scaling.
In this paper, the watermarking system is designed to extract 16 bits data
within any 15-seconds period of video. In order to achieve this requirement, two
watermarks should be extracted within any 7.5 s in the proposed method. If two
watermarks are not extracted within 7.5 s, we consider that the watermark is
not detected.
An experiment was performed to evaluate the robustness to compression and
DA-AD conversion at various watermark strength α while decreasing the bit rate
in compression. We selected α = 0.3, 0.7, 1.5 to determine the relation between
the bit rate and α. Tables 2, 3 and 4 show the BER in various bit rates with fixed
α. When α = 0.3, the embedded data is not extracted except for a 1,200Kbps
bit rate. When α = 1.5, the embedded data is exactly extracted within 1/1000
compressibility. Also, we determined that there is little change in the PSNR and
SSIMS as α is increased.
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Table 2. Robustness test results for α = 0.3 (PSNR 43.92 dB, SSIM 0.9965)
Bit rate
Compressibility
12,000 8,000 6,000 3,000 1,500 1,200
1/100 1/150 1/200 1/400 1/800 1/1000
Basketball
Lego
Library
Walk1
Walk2
BER
BER
BER
BER
BER
0.000
0.000
0.000
0.000
0.000
Average
BER 0.000
0.000
0.000
0.000
0.000
×
0.000
0.000
×
0.000
×
0.000
×
×
0.000
×
×
×
×
0.000
×
×
×
×
×
×
×
×
×
×
×
× : watermark is not detected
Table 3. Robustness test results for α = 0.7 (PSNR 43.57 dB, SSIM 0.9962)
Bit rate
Compressibility
12,000 8,000 6,000 3,000 1,500 1,200
1/100 1/150 1/200 1/400 1/800 1/1000
Basketball
Lego
Library
Walk1
Walk2
BER
BER
BER
BER
BER
0.000
0.000
0.000
0.000
0.000
Average
BER 0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
×
0.000
0.000
0.000 0.000 0.000 ×
0.000
×
×
0.000
×
×
× : watermark is not detected
Table 4. Robustness test results for α = 1.5 (PSNR 42.61 dB, SSIM 0.9954)
Bit rate
Compressibility
12,000 8,000 6,000 3,000 1,500 1,200
1/100 1/150 1/200 1/400 1/800 1/1000
Basketball
Lego
Library
Walk1
Walk2
BER
BER
BER
BER
BER
0.000
0.000
0.000
0.000
0.000
Average
BER 0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000 0.000 0.000 0.000 0.000
× : watermark is not detected
4
Conclusion
This paper has proposed a watermarking system to be robust to MPEG-4 compression and DA-AD conversion with high transparency. The watermarking system employs a robust-and-imperceptible block selection method we propose and
a spread spectrum watermarking method in DCT domain. Also, we apply a
visual masking method that fuses a saliency map based on visual saliency model
and a noise map based on a noise visibility function. As a result, the proposed
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scheme has high imperceptibility. The experimental results show robustness to
compression and DA-AD conversion as well as high imperceptibility with high
PSNR and SSIM. Also, the results indicate that the proposed watermarking
system extracts 16 bits data in every 15 s. However, the proposed method has a
disadvantage that it is weak to geometrical attacks because it uses spread spectrum watermarking based on DCT domain. Therefore, the improvement to be
robust to geometrical attacks should be needed.
Acknowledgements. This research project was supported by Ministry of Culture,
Sports and Tourism (MCST) and from Korea Copyright Commission in 2015.
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