A New Robust Watermarking Scheme for Document Images by

Proc. of Int. Conf. on Recent Trends in Information, Telecommunication and Computing, ITC
A New Robust Watermarking Scheme for Document
Images by Randomized Distribution of Watermark
Segments
S Nirmala1, P Naghabhushan2 and Chetan K.R.3
1
JNN College of Engineering/Dept. of ISE, Shimoga, India
Email: [email protected]
2
Mysore University/Department of Studies in Computer Science, Mysore, India
Email: [email protected]_mysore.ac.in
3
JNN College of Engineering/Dept. of CSE, Shimoga, India
Email: [email protected]
Abstract— A new robust watermarking scheme for protection of document image contents
using redundant watermark segments is proposed in this work. A wavelet-based
watermarking scheme for embedding a logo has been developed for robust watermarking.
At the sender side, a level-2 wavelet transformation is applied on the source document
image. LL-sub-band of level-2 of the transformed image is subdivided into blocks of
uniform size. A logo watermark of size same as the transformed image block is considered.
The watermark is divided into a number of segments. A number of sets of transformed
image blocks are formed pseudo-randomly and the total size of all the blocks in a set is
equal to the size of the watermark. The watermark segments are embedded into blocks of
each set using quantization technique. The amount of quantization is controlled based on its
strength. Thus, multiple copies of watermark are available and each input image block
need not include the entire watermark. At the receiver side, the extracted segments from
each set of blocks are merged to obtain a single extracted watermark. Based on the
quantization step-size, size of the logo and the level of wavelet transform, the watermark is
extracted without accessing the original image. The experimental results show that the
proposed technique is highly robust. The performance evaluation results show that the
proposed approach is better than the existing method [1].
Index Terms— Document Image, Robust watermarking, Haar Wavelet, Quantization based
Embedding, Watermark Extraction
I. INTRODUCTION
Digitization of documents is essential in this digital age. People share files on digital platforms rather than
physical papers. This digitization also facilitates unauthorized use, misappropriation, and misrepresentation.
Thus, there is great interest in developing technology that will help protect the integrity of a digital media
element and the intellectual property rights of its owners. Digital watermarking is the art of protecting the
digital content by inserting the proprietary mark which may be easily retrieved by the owner to verify about
its ownership or authenticity [1]. Generally watermarking algorithm consists of three parts:
(i) watermark,
which is unique to the owner, (ii) the encoder for embedding the watermark into the data and (iii) the decoder
DOI: 02.ITC.2014.5.93
© Association of Computer Electronics and Electrical Engineers, 2014
for extraction and verification [1].
The main properties of a digital watermarking system to be addressed are: the data payload or capacity
(amount of information that can be embedded within an image), robustness (watermark resistance against
intentional and unintentional image processing operations) and fidelity (similarity between original and
watermarked images). A gain in one of these properties usually comes at the expense of loss in others [1].
A variety of digital watermarking methods have been developed in recent past for authentication and tamper
detection of digital information [2-5]. The problem of detecting any sort of intentional manipulation inside
the digital images has been addressed in [6]. The digital watermarking techniques are broadly categorized as
can be divided into block-wise and pixel-wise techniques [6]. In block-wise watermarking, the host image is
divided into non-overlapping blocks for tamper detection. In pixel-wise watermarking techniques, each pixel
is used for tamper detection.
Digital documents bring various challenges for copyright protection [7]. They have limited capacity for
watermark embedding, since there is no redundancy in text as can be found in images, audio, and videos. In
addition, any transformation on the digital documents should preserve the meaning, fluency, syntactic
structure, and the order of the content present in it. Preserving the writing style of the author is very
important in some domains such as literature writing or editorial columns in e-news. Sensitive nature of some
documents such as legal documents, poetry, and quotes does not allow semantic transformations, because a
simple transformation might destroy both the semantic connotation and the meaning of text. Thus a more
robust watermarking is desired for digital documents.
II. RELATED WORK
Some works have been reported in the literature towards robust watermarking. In [8], a novel feature-based
watermarking method using scale-invariant keypoints is described. The feature points are extracted using the
scale-invariant keypoint extractor and are then decomposed into a set of disjoint triangles. These triangles are
watermarked by an additive way on the spatial domain. Tang and Hang [9] proposed a watermarking
algorithm based on image segmentation and Discrete Cosine Transform (DCT). The image is segmented
using Expectation Maximization (EM) algorithm [10]. Wu and Liu [11], developed a novel method for image
watermarking based on embedding multiple identical watermarks in the spatial and frequency domains of the
image representation. In the spatial domain, the processing method uses a non-linear neural network
segmentation to output the different zones of watermark embedding with respect to the image characteristics.
In [12], a novel content-based watermarking approach that uses geometric warping to embed watermarks is
presented. This approach provides greater robustness to strong lossy compression [12]. Chareyron et.al.,
[13] proposed a robust watermarking technique against geometric distortions. This robust technique is based
on the modification of the two dimensional color histogram. In [14], a localized image watermarking scheme
for resisting geometric attacks is presented. The watermark synchronization scheme is based on local
invariant regions, which can be extracted using scale normalization and image feature points. The extracted
local regions are invariant to rotation, scaling and various signal processing attacks. Garg et.al. [15] proposed
a robust scaling-based multi-bit watermarking approach in the wavelet transform domain. The host image is
segmented into blocks of smaller size. Further, blocks with higher entropy are selected for embedding. In
[16], a watermarking scheme for binary document image involving DCT and spatial domains is discussed.
The watermark patterns are generated as the DCT domain signals, then perceptually shaped through
weighting its components in the spatial domain with the perceptual masks. In [1], a wavelet-based logo
watermarking scheme is presented that performs embedding into all sub-blocks of the LLn sub-band of the
transformed host image using quantization technique. For document images, the approach [1] does not
provide good PSNR and high degree of robustness. From detailed literature survey, it is evident that some of
the works resist common image processing attacks like rotation, translation, histogram equalization and
noise. However, they are not efficient against document image attacks like semantic transformations of the
text, text repositioning, and font style.
This paper presents a robust and efficient watermarking scheme for document images. The remainder of the
paper is organized as follows: Section III discusses on the proposed watermarking system. Experimental
results are presented in Section IV. The comparative analysis is discussed in Section V. Conclusions are
summarized in the section VI.
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III. PROPOSED WATERMARKING SYSTEM
In the proposed work, a new watermarking scheme is proposed, where the watermark is spread throughout
the input document. The block diagram of the proposed watermarking system is as shown in
Fig. 1. It
comprises of two modules: (i) Embedding the watermark and (ii) Extraction of watermark. The embedding
and extraction modules are explored in Fig. 2 and 3 respectively. The embedding and extraction techniques
are discussed in detail in subsequent subsections.
A. Robust Watermark Embedding Technique
In the robust watermark embedding mechanism, the original image is transformed using Haar wavelets upto
2 levels. The use of wavelets allows decomposing of signals into coarse and fine details and LL
sub-band
itself captures most of the energy present in the signal [17]. The transformed image is divided into blocks of
equal size. We have conducted experiments by decomposing transformed image into blocks of size 8 X 8, 16
X 16 and 32 X 32. From experimental evaluation it is observed that an optimum value to balance the good
quality of the watermarked image and increased robustness was found for the input image blocks of size 16 X
16. Hence the LL-2 sub-band coefficients have been divided into blocks of size 16 X 16. A logo image is
used as watermark. The watermark is decomposed into number of segments. Experiments are conducted by
dividing the watermark into 2 segments to a maximum of 5 segments.
Input Image
Embedding
Watermarked
image
Extraction
Watermark
Watermark
Figure 1. Proposed watermarking system
Figure 2. Embedding Module
Figure 3. Extraction Module
A block set is formed by a set of pseudo randomly selected blocks of the Haar wavelet transformed image.
The number of blocks in the set is equal to the number of watermark segments. The watermark segments are
embedded into the Haar wavelet transformed image blocks of the block set. The embedding process is
illustrated in Fig. 4.
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A sample water mark logo has been divided into 5 segments. The input image is transformed using wavelet
transformation for 2 levels and the LL-2 subband is also shown in Fig. 4. The LL-2 subband has been divided
into blocks of uniform size. Some of these blocks have been pseudo randomly selected (highlighted blocks in
the Fig. 4) and they form the block set. The watermark segments are embedded into the blocks of this blockset. All the blocks in the block-set are not adjacent to each other. Thus, the watermark segments are
distributed.
Figure 4. Watermark Distribution
There are some advantages of this distributed embedding:. The amount of embedding is reduced and
consequently noise of watermarked image is greatly reduced. The randomness used in embedding watermark
allows more spreading of the watermark, which also accounts for greater robustness. Further, security is also
provided as the receiver needs to know the pseudo-random permutation of blocks for proper extraction and
authentication. Having more number of watermark segments, increases robustness during extraction and
allows watermarked image to sustain more attacks. Hence, even if watermark is altered in few regions, it
could be extracted from other regions and thus robust to various attacks such as resizing, cropping and other
geometrical attacks [1].
The embedding of watermark bit into a wavelet coefficient is achieved through the following quantization
based spreading algorithm:
Algorithm
If W(i,j) is 0 then
385
= (⌊(. )⌋, )
if(rem > 0.75*Q)
(, ) = ∗ ⌊(. )/⌋ + 0.8 × end-if
else
= (⌊ (. )⌋, )
if(rem <= 0.75*Q)
(, ) = ∗ ⌊(. )/ ⌋ + 0.8 × end-if
end-if
where qk(i,j) and qk’(i,j) represents the wavelet coefficient of a block k, before and after quantization
respectively and Q represents the quantization factor. In this embedding technique, it can be observed that
embedding is done only if the remainder of the wavelet coefficient on Quantization factor is more than 75%.
This is because, more than 75% of the Quantization factor needs to be added to existing wavelet coefficient,
if the coefficient has to sustain most of the common image processing and document image attacks.
B. Robust Watermark Extraction Technique
In the robust watermark extraction mechanism, the watermarked image is transformed using
Haar
wavelets upto two levels. Watermark is extracted from the wavelet coefficients of level-2 LL sub-band. The
LL-2 sub-band coefficients are divided into blocks of size 16 X 16. The block-set comprising of pseudo
randomly selected blocks used in the watermark embedding is securely communicated to the receiver. The
watermark segments are extracted from the blocks of the block-set and are merged, which is illustrated in the
Figure 5. In this figure, the LL-2 subband of the watermarked image is shown along with the subdivided
blocks. The LL-2 subands of the watermarked image is visually similar to the LL-2 subbands of the original
image. The same block-set (shown as highlighted blocks in Fig. 5) used at the sender is used for extracting
the watermark segments. The watermark segments are further merged to get the watermark logo as shown in
Figure 5.
The extraction mechanism is detailed in the following algorithm:
Algorithm
= (⌊(. )⌋, )
if(rem >= 0.75*Q)
wm(wgcnt,j,k)=1;
else
wm(wgcnt,j,k)=0;
end
where (. ) ) represents the wavelet coefficient of a block k, Q represents the quantization factor and wm
represents the watermark segment, wgcnt gives the segment number of the watermark useful during merging
the segments back to watermark.
In the extraction algorithm, the remainder after dividing wavelet coefficient and quantization factor is
computed. If this remainder exceeds 75% of the Quantization factor, watermark extracted is set to 1. This
setting allows great degree of robustness against most of the common image processing and document image
attacks. The extracted logos should be exactly same as watermarked logos, under no distortions.
However, in the case of distortions, decision on the authentication of the image is obtained by selecting the
watermark with highest normalized Correlation coefficient [18] between original and extracted watermarks.
IV. EXPERIMENTAL RESULTS
For the experimental study of the proposed watermarking system, we have created an image corpus. The
corpus contains 60 images belonging to five different classes (Markscards, Certificates, ID-Cards, Cheques
and Bills). The robustness of the proposed watermark scheme is tested by applying various attacks such as
horizontal cropping, vertical cropping, resizing, noise, JPEG compression and rotation.
The degree of robustness obtained is evaluated in terms of Normalized Correlation Coefficient (NCC):
(2)
NCC=
386
Figure 5. Merging of Watermark segments
where represents original and the extracted watermark logo. Normalized correlation is one of the
methods used for template matching, a process used for finding incidences of a pattern or object within an
image. It ranges between 0 to 1. Higher values of NCC are desired for robust watermarking for all different
types of incidental attacks. The embedding capacity and quality of the watermarked image is evaluated using
Peak-Signal-to-Noise Ratio (PSNR) [18]. The formulae for Mean Square Error (MSE) and Peak Signal to
Noise Ratio (PSNR) are as follows:
(3)
∑ ∑
=
( (, ) − (, ))
∗ !
(4)
"#$ = 20 ∗ %& ('*- ) − 10 ∗ %& ()
where ‘I’ and ‘W’ represent the pixels of original and watermarked image respectively. AXI is the maximum
possible pixel value of the image.
Fig. 6(a) shows the original document image (e.g. marks card), Fig. 6(b) the watermark logo and Fig. 6(c) the
watermarked image.
The robustness of any watermarked document image under various image processing attacks were analyzed
by varying the number of segments of the watermark from 1 to 5. The values of NCC for different attack
scenarios on a sample watermarked document image in the image corpus for different number of segments
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are tabulated in Table I. It could be observed from the results shown in Table I that NCC values in all the
cases are above 0.7. From the experimental evaluations, it is observed that with a threshold value of 0.5,
extracted watermark is clearly identifiable. Based on this, threshold value of 0.5 is selected. All the values
obtained are sufficiently above the chosen criterion and thus sufficiently robust. Further, it could be observed
that NCC values do not vary much, with increase in the number of segments of the watermark. Also the
desired level of robustness could also be achieved with more number of watermark segments. Consequently,
there is a significant improvement in the quality of the watermarked image (as less watermark is embedded)
and less time for embedding and extraction without compromising on the level of robustness being achieved.
The measure of robustness (NCC) was also tested for a large image corpus and the effect on increasing the
number of segments was analyzed. Fig. 7 depicts robustness achieved, when all the images in the corpus
were watermarked and subjected to various attacks. The number of watermark segments was varied from 1 to
5. It is evident for the Fig. 7 that the range of average NCC values of all the images in the corpus for different
attacks is 0.76 to 0.86. Thus, even with more number of segments, NCC values are not affected and hence,
one can use many number of watermark segments. Fig. 8 shows the perceptual quality of the watermarked
image for varying number of watermark segments (1 to 5).
The advantage of more number of segments is the improved perceptual quality of the watermarked image and
reduced time taken for embedding and execution. The perceptual quality of the watermarked image is
measured in terms of PSNR. The PSNR values of the watermarked image for different number of segments
are depicted in Fig. 8. It is clearly evident that higher PSNR values are possible with more number of
segments. This quantifies our claim of better perceptual quality. However, the robustness level falls below
0.7, when number of segments of the watermark is increased beyond 5. Hence the maximum number of
segments to achieve good robustness is selected as 0.5.
(a)
(b)
(c)
Figure 6. (a) Original Document Image (b)Watermark logo and (c) Watermarked Image
TABLE I. R OBUSTNESS OF THE PROPOSED SCHEME FOR VARYING NUMBER OF SEGMENTS FOR A SAMPLE IMAGE IN T HE C ORPUS
No. of
segments of
watermark logo
Value of NCC
No attack
1
2
3
4
5
1
1
1
1
1
JPEG
compres
sion
0.85
0.789
0.82
0.74
0.75
Salt &
pepper
noise
0.88
0.85
0.86
0.80
0.85
Horizontal
crop
Vertical
crop
Resize
Rotate
1
1
1
0.98
0.98
1
0.98
1
0.98
0.99
1
1
1
1
0.81
1
1
1
1
0.82
V. COMPARATIVE ANALYSIS
For comparative analysis we implemented an existing method [1]. In this method, the image is wavelet
transformed into 2-levels. The LL-2 sub-band is divided into blocks of size 16 X 16. In this method,
watermark is embedded into all image block using Quantization based embedding. The watermarked image
in both existing method [1] and proposed method are subjected to many image processing attacks for
instance, horizontal and vertical cropping of the segments of the document image, rotation of the core portion
of the document image, JPEG compression (about 70%), adding noise (salt and pepper ,Gaussian noise) and
388
Figure 7. Average NCC values for different attacks
Figure 8. Average PSNR
resizing of the image. The results of the proposed method (number of watermark segments = 3) and existing
method [1] for various attacks are shown in Fig. 9. It is evident from the results shown in Fig. 9 that the
visual appearance of the extracted watermark of the proposed approach is of good quality and hence the
watermark scheme is robust.
In the case of horizontal cropping, the central region of the watermarked image was cropped. The proposed
method was able to recover a clearly visible watermark logo compared to the existing method [1], in which
recovered watermark logo had some noises and some portions were not clearly visible. In the case of vertical
cropping the watermark logo extracted with existing method [1] is partly visible where as the proposed
method extracts a properly visible watermark logo. Similarly in case of rotation and resizing some portions of
the watermarked document image, the watermark logo extracted has some noise on the top portions and
proposed method extracts a clearly visible watermark logo. In case of compression, the existing method [1]
produces a lot of noise is added to the watermark logo, as entire watermarked image is affected from JPEG
compression. Even in this case, the watermark logo extracted from the proposed method is clearly
perceivable compared to existing method [1]. Noise in the form of salt and pepper also affects the
watermarked image in its entirety. Since, in the proposed method watermark is randomly and distantly
distributed, proper of extraction of the watermark logo has been possible.
Watermarked Image with Attacks
Extracted Watermarks
Existing Method [1]
(a)
Horizontal crop
389
Proposed Method
(b)
Vertical crop
(c) Rotation of the portion of the
document image (e.g. name of the
candidate in the marks card)
(d) Resizing portion of the document
(example increasing size of the logo in
the marks card)
(e) JPEG compression (70%)
(f) Salt and Pepper noise
Figure 9. Extracted Watermark from proposed and existing method [1] after various attacks
390
The robustness of the proposed watermarking scheme has been analyzed by computing NCC values for
various attacks and tested for varying number of image segments. Various analysis of the results were carried
out on all images in the image corpus. First, the robustness performance was compared between the existing
method [1] and proposed method for 5 segments. The NCC values of a watermarked document image for
various attacks are plotted and corresponding graph is shown in Fig. 10. It can be observed that the proposed
method clearly outperforms the existing method [1]. From the plot shown in Fig. 10, it is observed that NCC
values of the watermarked document image are closer to 1. Hence the proposed method outperforms the
existing method [1].
Figure 10. Robustness of Existing[1] and Proposed methods
VI. CONCLUSIONS
In this paper, a highly robust watermarking scheme using wavelets and randomized distribution of segments
of the watermark has been proposed. The robustness of the proposed work is justified by the higher NCC
values (>0.75) for various image processing attacks on the watermarked document images. The use of
varying number of segments of the watermark has been analyzed. It was observed that a maximum of five
segments result in good quality watermark extraction. Further, it benefits in improving visual clarity of
watermarked image. This was quantified using PSNR as perceptual metric and higher PSNR values were
exhibited as number of segments was increased. The dynamic number of segments of the watermark and
each segment of the watermark of variable size is considered as a future enhancement of the current work.
REFERENCES
[1] W.-T. Huang, S-Y. Tan, Y.-J Chang and C.-H. Chen, “A robust watermarking technique for copyright protection
using discrete wavelet transform”, WSEAS Trans. on Computers, Vol. 9, No. 5, 2010, pp. 485-495.
[2] H. Mirza, H. Thai, and Z. Nakao, “Color image watermarking and self-recovery based on independent component
analysis,” Lecture Notes in Computer Science, 2008, Vol. 5097, pp. 839-849.
[3] M. S. Wang and W. C. Chen, “A majority-voting based watermarking scheme for color image tamper detection and
recovery”, Computer Standards & Interfaces, 2007, Vol. 29, pp. 561-571.
[4] R. Poli, J. Kennedy and T. Blackwell, “Particle swarm optimisation: an overview”, Swarm Intelligence Journal,
2007, Vol. 1, No. 1, pp. 33–57.
[5] T.-Y. Lee and S. D. Lin, “Dual watermark for tamper detection and recovery,” Pattern Recognition, 2008, Vol. 41,
No. 11, pp. 3497-3506.
[6] Xiaochuan Gao, Chun Qi and Haitao Zhou, “An Adaptive Compressed-DCT-Domain Watermarking”, 8th
International Conference on Signal Processing, 2006, Vol. 4, pp. 1-4.
[7] J. T. Brassil, S. Low, and N. F. Maxemchuk, “Copyright Protection for the Electronic Distribution of Text
Documents”, Proceedings of the IEEE Trans. on Multimedia, Vol. 87, No. 7, 1999, pp.1181-1196
[8] Hae-yeoun Lee , Choong-hoon Lee , Heung-kyu Lee and Jeho Nam , “Feature-based image watermarking method
using scale-invariant keypoints”, Lecture Notes In Computer Science, 2005, pp. 312-324.
391
[9] Tang CW and Hang HM, “A feature-based robust digital image watermarking scheme. IEEE Trans Signal Process”,
2003, Vol. 51, No. 4, pp. 950–959.
[10] Tim-kun Lin and Chung-Lin Huang , “Digital Image Forensics Using EM Algorithm”, Advances in Multimedia
Information Processing, Lecture Notes in Computer Science, 2009, Vol. 5879, pp. 994-998.
[11] M. Wu and B. Liu, “Data hiding in binary images for authentication and annotation”, IEEE Trans. Multimedia,
2004, Vol. 6, No. 4, pp. 528–538.
[12] D. Pröfrock, M. Schlauweg and E. Müller, “Video Watermarking by Using Geometric Warping Without Visible
Artifacts”, Lecture Notes in Computer Science- Information Hiding, 2007, Vol. 4437, pp. 78-92
[13] Chareyron, G., Macq, B., and Tremeau A, ‘Watermarking of color images based on segmentation of the XYZ color
space’, Second Eur. Conf. on Color in Graphics, Imaging and Vision, Aachen, Germany, April 2004, pp. 178-182.
[14] Alghoniemy M and Tewfik AH. “Geometric invariance in image watermarking”, IEEE Trans. on Image Processing,
2004, Vol. 13, No. 2, pp. 145–53.
[15] T. M. Ng and H. K. Garg, “Maximum-likelihood detection in DWT domain image watermarking using Laplacian
modeling,” IEEE Signal Process. Lett., 2005, Vol. 12, No. 4, pp. 345–348.
[16] Xinshan Zhu, Member, IEEE, Lei Wen, and Yanming Chen, “Novel Binary Document Image Watermarking
Exploiting the Features of Double Domains”, International Journal of Computer and Electrical Engineering, 2012,
Vol.4, No.1, pp. 87-92
[17] Soheili, M.R., “A Robust Digital Image Water marking Scheme Based on DWT”, Journal of Computer Engineering,
2009. Vol. 1, pp. 3-11.
[18] Jaejin Lee, Chee Sun Won “A Watermarking Sequence Using Parities of Error Control Coding For Image
Authentication And Correction”, Consumer Electronics, IEEE Transactions, Vol. 46, No. 2, 2000, pp. 313 -317.
392

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