International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 7, July 2013
ISSN 2319 - 4847
Robust and Oblivious Watermarking based on
Swapping of DCT Coefficients
Dr. K. Ramanjaneyulu 1, Dr. P. Pandarinath 2 and B. Rakesh Reddy 3
1
Electronics and Communication Engineering Department, PVP Siddhartha Institute of Technology, Vijayawada, India
2
Computer Science and Engineering Department, Gudlavalleru Engineering College, Gudlavalleru, India
3
Computer Science and Engineering Department, Sir CRR College of Engineering, Eluru, India
Abstract
In this paper, a robust digital image watermarking algorithm using the Discrete Cosine Transform (DCT) is proposed for the
secret message protection. Results show that this algorithm is robust to many image attacks. Various attacks were tested on a
number of 8-bit grayscale cover images of size 512×512, embedded with binary watermark images of size 32×32 and 64×64.
Visual quality of both watermarked image and extracted watermark is good. The performance of the proposed algorithm is
calculated in terms of PSNR, NCC, BER and SM.
Keywords: Digital Watermark, Discrete Cosine Transform, Robust, Jpeg compression and Attacks.
1. INTRODUCTION
In recent years digital watermarking is used to protect the secret data; it can be images, audio, video and documents.
Digital image watermarking can offer copyright protection of image data by hiding copyright information in the original
image [1]-[2]. Image watermarks may be visible or invisible, where a visible watermark is easily detected by observation
[3] while an invisible watermark is designed to be transparent to the observer and detected using signal processing
techniques [4]-[5].
Two different types of methods in watermarking they are spatial domain methods and frequency domain methods. Spatial
domain algorithms are simple and data embedding capacity is more and vulnerable [6]. The watermarking of image is
good with frequency domain that is on Discrete Cosine Transform (DCT) [7]-[9]. DCT converts data from spatial domain
to frequency domain. An invisible watermarking scheme was designed with robust and oblivious DCT based
watermarking process.
In this paper, an invisible watermarking algorithm is proposed in DCT domain which is robust and oblivious. Robust
indicates that there is high possibility of extracting watermark with less error even the watermarked image is subjected to
different attacks and Oblivious indicates that cover image is not needed during extraction. The cover image is firstly
transformed into DCT domain by 8×8 block DCT transform. The watermark bits are embedded into the cover image by
adjusting the relationship among a group of middle frequency coefficients. And the approach of embedding process is
based on swapping of two coefficients which are selected.
The Alignment of the paper is as follows. Section 2 describes Watermarking Requirements. In section 3, Proposed
Watermarking Method is explained. Section 4 provides Simulation Results and Conclusions are presented in Section 5.
2. WATERMARKING REQUIREMENTS
The main requirements for an acceptable quality [10] [11] of watermarking are as follows.
a) Imperceptibility: The watermark should not be easily noticed by simple visual inspection.
b) Key uniqueness: Different keys should produce different, statistically independent watermarks.
c) Non invertibility: It should not be computationally feasible to find the watermark by possessing a watermarked image.
d) Image dependency: A single key produces a single watermark; however, this watermark should be adapted to the image
content.
e) Reliable detection: The watermark should be efficiently detected for any value of false alarm probability up to a certain
threshold.
f) Tranparency: The watermark is not visible in the image under typical viewing conditions.
g) Capacity: Ability to detect watermarks with a low probability of error as the number of watermarked versions of the
image increases.
h) Robust: The watermark can still be detected after the image has undergone some linear or non linear operations.
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Volume 2, Issue 7, July 2013
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3. PROPOSED WATERMARKING METHOD
3.1 Coefficient Selection
Transform the cover image into 8×8 DCT blocks. Here, Fig. 1 represents a single 8×8 DCT block which is selected from
the cover image. Select the coefficients of two which are neighbor to each other. In Fig. 1, the selected two coefficients
are indicated in two colors. Red color coefficient is the selected coefficient one i.e. coeff1 (i, j) and dark blue color is the
second selected coefficient i.e. coeff2 (i, j).
(1,1)
(2,1)
(3,1)
(4,1)
(5,1)
(6,1)
(7,1)
(8,1)
(1,2) (1,3) (1,4) (1,5) (1,6) (1,7)
(2,2) (2,3) (2,4) (2,5) (2,6) (2,7)
(3,2) (3,3) (3,4) (3,5) (3,6) (3,7)
(4,2) (4,3) (4,4) (4,5) (4,6) (4,7)
(5,2) (5,3) (5,4) (5,5) (5,6) (5,7)
(6,2) (6,3) (6,4) (6,5) (6,6) (6,7)
(7,2) (7,3) (7,4) (7,5) (7,6) (7,7)
(8,2) (8,3) (8,4) (8,5) (8,6) (8,7)
Fig. 1 Coefficient Selection in a 8×8 DCT block
(1,8)
(2,8)
(3,8)
(4,8)
(5,8)
(6,8)
(7,8)
(8,8)
3.2 Description of Watermark Embedding Method
1) In order to authenticate the image successfully, the embedded information should be robust against different attacks.
The modified embedding process uses the proposed scheme and it utilizes the relationship among 2 selected coefficients
from each block to embed the watermark. This section will illustrate the embedding process in detail. The block diagram
of the watermark embedding process is shown in Fig. 2.
Watermark image
Cover Image
8×8 DCT
8×8 Blocks and
coefficients Selection
Embedding
Process
Inverse DCT
Watermarked image
Fig. 2 Watermark Embedding Process
Now we see details of embedding process in stepwise below:
a. Spilt the Original Image into 8×8 blocks, and then apply DCT transformation on each block.
b. Select two coefficients from each 8×8 DCT block as per Fig. 1 and denote them as coeff1 and coeff2 respectively.
When the watermark is inserted in low frequency coefficients then it reduces the image quality and if inserted high
frequency it is easily influenced by attacks such as filtering. So, mid band DCT coefficients for embedding the watermark
are selected.
c. Introduce three variables x and y which are used to modify the coefficients values after proper initialization.
Experimentally, it is found that the following values are good for Mountsea.bmp cover image and 32×32 size watermark.
x=3.5 and y=2.45
Modify the values of coefficients; coeff1 (i, j) = x×coeff2 (i, j) and then coeff2 (i, j)= (coeff1(i,j)+y×coeff1(i,j)).
While swapping the coefficients for 64×64 size watermark image, initialize the variables x=1.6 and y=1.7 to modify the
values of coefficients; coeff1 (i, j) = x×coeff2 (i, j) and then coeff2 (i, j)= y×coeff1(i,j).
d. Mid band DCT coefficients are compared and adjusted for each DCT block based on the following rules.
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Where,
represents the
bit of watermark and for simulation of results use coeff1 (5, 2) and coeff2 (4, 3) and coeff1
(i, j) or coeff2 (i, j) represents
DCT coefficients. If the watermark bit is 0 then DCT transform coeff1 (5, 2) should
be greater than coeff2 (4, 3). If the watermark bit is 1 then DCT transform coeff2 (4, 3) should be greater than coeff1 (5,
2). If any of above cases is false then this two coefficients are swapped.
3.3 Description of Watermark Extraction Method
The watermark extraction process does not require the original image, i.e. the paper scheme is oblivious. The watermark
embedding position and the block selection sequence during watermark embedding are utilized to extract the watermark.
The following Fig. 3 describes the extraction process.
Suspected image
8×8 DCT
2 Neighbor 8×8 Blocks
and coefficients
Selection
Extraction
Process
Extracted
watermark
Fig 3: Watermark Extraction Process
Watermark Extraction Method steps are given below.
a. Spilt the possibly attacked watermarked image into 8×8 blocks, and then apply DCT transformation on each block.
b. Select two coefficients from each 8×8 DCT block as per Fig. 1 and identify them as coeff1 (i, j) and coeff2 (i, j).
c. Watermark extraction process can be done with following rule.
Where, w (i, j) is extracted watermark.
d. The decoded watermark bits are organized into m×n matrix form W’. Compare the extracted watermark with the
original watermark W. The bit error rate (BER) formula can be defined as follows:
Where, ⊕ is XOR operation.
e. The peak signal-to-noise ratio (PSNR) is used to evaluate the quality of the watermarked image in comparison with the
original cover image. PSNR Formula is as follows:
Where, m and n are the height and width of the image, respectively. f (i, j) and g (i, j) are the pixel values located at
coordinates (i, j) of the original image, and the attacked image, respectively.
f. After extracting the watermark, the normalized correlation coefficient (NCC) is computed using the original
watermark and the extracted watermark to measure the quality of an extracted watermark. It is defined as
Where, m and n are the height and width of the watermark, respectively. The symbols (i, j) and w’ (i, j) are the bits
located at the coordinates (i, j) of the original watermark and the extracted watermark respectively. The symbols
and
are the values of the original watermark and the extracted watermark respectively.
g. The similarity factor is a metric which determines the similarity between the watermark inserted and the extracted
watermark. SM is the similarity factor ranging from 0 to 1. SM=1 indicates that original watermark and the extracted
watermark are the same and any other value indicates the deviation among them. Generally, SM >0.75 is accepted for
visible similarity. SM can be calculated using the following equation.
Where, w and w’ represents the original and the detected watermarks respectively.
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Volume 2, Issue 7, July 2013
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4. SIMULATED RESULTS
Proposed Algorithm has been executed on several standard 512×512 grayscale images, including Mountsea.bmp,
paddy.bmp and road.bmp and y×y pixels of watermark image (both 32×32 pixels and 64×64 pixels). Both binary
watermark images of size 32×32 and 64×64 are used in this work. Fig. 4 shows the embedding process with
Mountsea.bmp (512×512) cover image and 32×32 size watermark. Parameters x and y are initialized as x=3.5 and
y=2.45. The simulation results are shown in Table 1.
Mountsea.bmp (512×512)
PSNR = 37.46 dB
watermark
image (32×32)
Watermarked.bmp (WI)
PSNR = 37.43 dB
Fig. 4 Embedding Process from cover image to Watermarked image
Table 1 describes all measures like Quality Factor of JPEG Compression, Extracted watermark image, Attacked PSNR
(dB), Bit-Error-Rate (BER), Similarity factor Measure (SM) and Normalized Cross-Correlation Coefficient (NCC). Table
2 describes the different types of attacks on watermarked image like Gaussian Filtering (2×2) at density 0.2, Gaussian
Filtering (3×3) at density 0.6, Gaussian Filtering (3×3) at density 0.4,Gaussian Filtering (5×5) at density 0.3, Gaussian
Noise at 0.00005, Blurring at 0.6, Sharpening at 0.6, Motion 2bits with 90 degrees, Motion Blurred Image at 20,45,
Cropping left-side (1,1,255,255) and Rotating watermarked image to 5 degree and then rotated back.
Let us take same cover image (Mountsea.bmp) of size 512×512 which is embedded with 64×64 size binary watermark
image. Fig. 5 describes the embedding process.
Mountsea.bmp (512×512)
watermark
Watermarked Image (512×512)
PSNR = 34.12 dB
image (64×64)
PSNR = 34.10 dB
Fig. 5 Embedding Process from cover image to Watermarked image
The values for x and y given are x=1.6 and y=1.7.
The simulated results for quality factor of JPEG Compression attack on watermarked image listed in the Table 3.
Table 4 describes with different types of attacks on watermarked image like Gaussian Filtering (2×2) at density 0.2,
Gaussian Filtering (3×3) at density 0.6, Gaussian Filtering (3×3) at density 0.4,Gaussian Filtering (5×5) at density 0.3,
Gaussian Noise at 0.00005, Blurring at 0.6, Sharpening at 0.6, Motion 2bits with 90 degrees and soon.
Table 1 Simulated Results of JPEG Compression attack on Watermarked image
S.NO
QF of JPEG
Compression
Attack
Extracted
Watermar
k image
PSNR (dB)
of
attacked
Watermark
image
Quality of the Extracted Watermark
BER
SM
NCC
1
10
26.92
0.3018
0.8046
0.1432
2
20
28.83
0.2930
0.8000
0.2692
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3
30
29.88
0.2461
0.8315
0.3969
4
40
30.55
0.2217
0.8498
0.4412
5
50
31.09
0.1846
0.8743
0.5487
6
60
31.61
0.1602
0.8911
0.6118
7
70
32.34
0.1250
0.9160
0.6833
8
80
33.37
0.0977
0.9349
0.7457
9
90
35.16
0.0439
0.9710
0.8825
10
100
37.28
0.0146
0.9904
0.9596
Table 2 Simulated Results for Other Attacks on Watermarked Image
S.NO
1
Attacked
Watermarked Image
Gaussian
Filtering(2×2) at 0.2
PSNR (dB)
Extracted
Image
BER
SM
NCC
28.69
0.1484
0.8991
0.6432
2
Gaussian
Filtering(3×3) at 0.6
33.81
0.0459
0.9696
0.8798
3
Gaussian
Filtering(3×3) at 0.4
37.71
0.0146
0.9904
0.9598
4
Gaussian
Filtering(5×5) at 0.3
37.54
0.0068
0.9955
0.9810
5
Gaussian
Noise,0.00005
36.37
0.0547
0.9639
0.8538
6
Blurring, 0.6
37.71
0.0146
0.9904
0.9598
7
Sharpening, 0.6
19.71
0.0469
0.9690
0.8782
8
Motion 2bits, 90
degree
34.28
0.0781
0.9478
0.8035
9
Motion Blurred
Image, at 20,45
23.34
0.3555
0.7425
0.2486
10
Cropping, left-side
7.96
0.0508
0.9684
0.8571
11
Rotate 5 degree
26.49
0.0742
0.9505
0.8117
Table 3 Simulated Results of JPEG Compression attack on Watermarked image
S.NO
QF of JPEG
Compression
Attack
Volume 2, Issue 7, July 2013
Extracted
Watermark
image
PSNR (dB) of
attacked
Watermark
image
Quality of the Extracted Watermark
BER
SM
NCC
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1
10
26.54
0.2766
0.8240
0.1824
2
20
28.23
0.2395
0.8403
0.3683
3
30
29.07
0.2068
0.8606
0.4749
4
40
29.61
0.1577
0.8952
0.5831
5
50
30.06
0.1313
0.9132
0.6466
6
60
30.48
0.1174
0.9222
0.6880
7
70
31.02
0.1060
0.9297
0.7211
8
80
31.75
0.0674
0.9555
0.8222
9
90
32.90
0.0466
0.9694
0.8746
10
100
34.10
0.0276
0.9820
0.9243
Table 4 Simulated Results for Other Attacks on Watermarked Image
S.NO
Types of Attack
PSNR (dB) of
attacked
Watermark
image
Extracted
Watermark
image
Quality of the Extracted Watermark
BER
SM
NCC
1
Gaussian
Filtering(2×2) at 0.2
28.21
0.1482
0.8997
0.6420
2
Gaussian
Filtering(3×3) at 0.6
32.46
0.0564
0.9628
0.8511
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3
Gaussian
Filtering(3×3) at 0.4
34.43
0.0332
0.9782
0.9099
4
Gaussian
Filtering(5×5) at 0.3
34.18
0.0254
0.9834
0.9303
5
Gaussian
Noise,0.00005
33.59
0.0581
0.9617
0.9303
6
Blurring, 0.6
34.43
0.0334
0.9781
0.8472
7
Sharpening, 0.6
19.44
0.0747
0.9506
0.9093
8
Motion 2bits, 90
degree
32.65
0.0723
0.9522
0.8039
9
Motion Blurred
Image, at 20,45
23.33
0.4399
0.6713
0.1189
10
Cropping, left-side
7.95
0.0535
0.9666
0.8459
11
Rotate 5 degree
26.20
0.0732
0.9514
0.8117
5. CONCLUSION
In this work, Robust and oblivious watermarking algorithm is proposed and its performance against various image attacks
is good. Algorithm is designed for embedding both 32×32 size and 64×64 size watermarks. Proposed method is modified
version of the existing method [12] for 32×32 size watermark image. It is an extension of the proposed method for 64×64
size watermark. The performance of proposed method against various attacks for watermark images of 32×32 size and
64×64 size is presented in terms of PSNR, BER, NCC and SM. Visual quality of both watermarked image and extracted
watermark is good.
References
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
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[7.] A. Piva,M. Barni, F. Bartolini, and V. Capellini, “DCT-based watermark recovering without resorting to the
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AUTHORS
K.Ramanjaneyulu completed his B.Tech in ECE in 1990 and M.Tech in ECE in 1993. He completed his
doctoral degree in Andhra University in 2012. He has a total experience of 20 years in teaching. Presently, he
is working as a Professor in PVP Siddhartha Institute of Technology, Kanuru, Vijayawada. He has guided
several UG & PG student projects. His areas of interest include Wireless Communications, Digital Watermarking, Data
Security & Cryptography and Analog & Digital Communications.
P. Pandarinath did his B.E in CSE in 1994 and M.Tech in CSE in 2001. He completed his
doctoral
degree in Andhra University in 2012. He has a total experience of 17 years. He is working as a Professor in
Gudlavalleru Engineering College. He has guided over 100 UG & PG student projects. His areas of interest
include Network Security and Cryptography, Advanced Computer Architecture, DataBase Management System,
Computer Organization, Computer Networks and Bio-informatics.
B. Rakesh Reddy completed his B.E in Computer Science and Engineering in 2009 at Kamban Engineering
College, Tiruvannamalai. Presently he is studying final year M.tech in Computer Science and Technology
(2012-2013) in Sir CRR college of Engineering, Eluru. His areas of interest include, Digital Watermarking,
Data Security & Cryptography and Software Engineering.
G. Nirmala completed her B.Tech in CSE in 2003 and M.Tech (Ph.D) in CSE in 2007. She has a total
experience of 10 years. She is working as a Sr. Associate Professor in Sir CRR College of Engineering, Eluru.
She has guided several UG & PG student projects. Her areas of interest include Computer Networks, Design and
Analysis of Algorithm, Operating System, Data Mining & Data Warehousing, Cryptography and Network Security and
System Programming.
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