In this paper a new robust image watermarking algorithm based on blocks classification and visual cryp tography (VC) is presented. First the original image is decomposed into non-overlapping blocks. Then, we use canny edge detection and the support vector machine (SVM) classification method to categorize these blocks into smooth and non-smooth classes. The VC technique is used to generate two image shares: A master share that is constructed according to the block classification results and an owner share generated by using the master share together with a binary watermark. To verify the ownership of the image the watermark can be retrieved by stacking the master share and the owner share. By skipping blocks which are not robust against attacks, the robustness of our proposed watermarking method is significantly improved. Our method is completely imperceptible because the watermark pattern is concealed without modifying the original host image.

1. Journal of Information Security and Applications 45 (2019) 71–78
Contents lists available at ScienceDirect
Journal of Information Security and Applications
journal homepage: www.elsevier.com/locate/jisa
A highly robust and secure image watermarking based on
classification and visual cryptography
Ali Fatahbeygi, Fardin Akhlaghian Tab∗
Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran
a r t i c l e i n f o
Article history:
Keywords:
Image watermarking
Visual cryptography
Image blocks classification
Support vector machine
Robust blocks
a b s t r a c t
In this paper a new robust image watermarking algorithm based on blocks classification and visual cryp-
tography (VC) is presented. First the original image is decomposed into non-overlapping blocks. Then, we
use canny edge detection and the support vector machine (SVM) classification method to categorize these
blocks into smooth and non-smooth classes. The VC technique is used to generate two image shares: A
master share that is constructed according to the block classification results and an owner share gener-
ated by using the master share together with a binary watermark. To verify the ownership of the image,
the watermark can be retrieved by stacking the master share and the owner share. By skipping blocks
which are not robust against attacks, the robustness of our proposed watermarking method is signifi-
cantly improved. Our method is completely imperceptible because the watermark pattern is concealed
without modifying the original host image.
© 2019 Elsevier Ltd. All rights reserved.
1. Introduction
Nowadays, with the development of the internet and digital
technology around the world, the availability and use of digital in-
formation has increased quickly. People can process, exchange and
store digital contents more simply than ever. However, against this
advantage, a new set of problems concerning security such as un-
restricted duplication, manipulation and distribution of multimedia
have arisen. Therefore, ownership protection and content verifica-
tion have become significant issues. Among the existing techniques
to solve these problems, watermarking is considered as a strong
technique.
Digital watermarking is a process of embedding digital infor-
mation called watermark into the multimedia data. Watermarking
technique is used for several purposes including content authen-
tication, owner identification, copy control and data integrity. Ac-
cording to the data needed during the watermark extraction pro-
cess, digital watermarking can be roughly categorized into non-
blind, semi-blind and blind methods. Non-blind methods require
the original image to extract the embedded watermark. The semi-
blind watermarking schemes usually require the watermark or
auxiliary information such as keys to detect or recover the water-
mark. Finally, a watermarking technique is referred to as blind if
∗
Corresponding author.
E-mail addresses: a.fatahbeygi@uok.ac.ir (A. Fatahbeygi), f.akhlaghian@uok.ac.ir
(F. Akhlaghian Tab).
neither the original image nor the watermark is needed during the
watermark extraction phase.
Depending on the watermark embedding domain, digital water-
marking methods can be categorized into two main classes: spatial
domain and transform domain. In spatial domain techniques, the
watermark is inserted by directly altering pixel intensities of the
cover image [1–5]. Altering least significant bits (LSB) of the host
image is one of the common spatial domain based watermarking
techniques. Spatial domain methods have low computational com-
plexity, but usually are not robust against most image processing
or other attacks. On the other hand, in transform domain methods,
the original image is first transformed into the frequency domain
by means of several transformation methods, such as discrete co-
sine transform (DCT) [6–8], discrete fourier transform (DFT) [9–11],
discrete wavelet transform (DWT) [12–14] and Hadamard trans-
form [15–17]. Then based on the certain criteria, the transform do-
main coefficients are altered for embedding the watermark and fi-
nally inverse transform is applied to obtain the watermarked digi-
tal media. Watermarking methods in this domain have high com-
putational complexity, but are always more robust than spatial do-
main watermarking schemes.
In the block-based watermarking technique the original image
is divided into non-overlapping blocks. Some watermarking meth-
ods first transform image blocks into the transformation domain
and then watermark is inserted into these blocks. The advantage
of these methods is lower transform computation regards to the
small size of blocks. In recent years, some block-based watermark-
https://doi.org/10.1016/j.jisa.2019.01.005
2214-2126/© 2019 Elsevier Ltd. All rights reserved.

2. 72 A. Fatahbeygi and F. Akhlaghian Tab / Journal of Information Security and Applications 45 (2019) 71–78
ing methods based on the visual cryptography (VC) technique have
been proposed [18–24].
The idea of using the VC technique for watermarking was in-
troduced by Hwang [18] in 2000. Hwang proposed a watermark-
ing embedding scheme using VC. In his scheme, a number of pix-
els of the original image are randomly selected using a secret key.
Then the most significant bit of each selected pixel with a bi-
nary watermark is used for generation of the owner share image.
Hsu and Hou [20] proposed a watermarking scheme for digital im-
ages based on statistics and VC. They used binary images of any
size as the watermark, and the master share was constructed from
the original host image and sampling distribution of means. Wang
and Chen [23] proposed a hybrid DWT–SVD copyright protection
method based on VC. They decompose the original image by ap-
plying two-level DWT. Then, a set of pixel positions is selected
randomly only from the LL2 sub-band. Next, the SVD is imple-
mented on a window centered at each selected pixel position, and
the first singular values of each window are used to generate a fea-
ture vector. The k-means clustering technique is then employed to
classify the extracted feature vectors into two classes. The master
share, then, is generated by utilizing the clustering result. Finally,
an owner share image is constructed by comparison of binary wa-
termark and the master share.
Rawat and Raman [24] proposed a watermarking technique
based on discrete fractional Fourier transform (DFrFT) and VC.
In their scheme the host image is divided into non-overlapping
blocks. According to the watermark size, a number of blocks are
randomly selected using pseudo-random number generator. Next
selected blocks are transformed into the frequency domain by us-
ing DFrFT. Then SVD is performed on all transformed blocks and
a matrix is generated by collecting the first singular value of each
block. Next, a binary map is formed with the help of this created
matrix. A master share image is constructed by using the binary
map. Finally, an owner share image is generated by using the mas-
ter share and a binary watermark. Their scheme is extremely ro-
bust against various image processing attacks. Wu et al. [8] pro-
posed another image watermarking method based on DCT and
SVD. First, the host image is divided into overlapping blocks. Then,
DCT is applied to each block and DC coefficients are extracted to
construct a DC-map. SVD is performed on a sequence of random
points on the map and a master share is created from the diagonal
matrix of SVD. Finally, an owner share is generated by employing
XOR operation between the master share and the watermark im-
age.
Tayebe and Mohsen [25] proposed an image watermarking
scheme based on DWT, SVD, scale invariant feature transform
(SIFT) and visual cryptography. First, master shares are extracted
using DWT, SVD and (SIFT) on a group of original images. Then, an
owner share is created by using XOR operation between the mas-
ter share and the watermark image. Makbol et al [26] presented a
robust hybrid block based image watermarking scheme based on
DWT, SVD and HVS characteristics. In their method, the original
image is decomposed into 8 × 8 non-overlapping blocks. Then, the
blocks with the lowest edge entropy values are selected as the best
embedding regions. After the first level of DWT transformation, the
SVD is applied to the LL sub-band to modify some elements in
its U matrix. Ali and Chang [27] introduced a lossless image wa-
termarking technique using combination of DWT, SVD, fractional
fourier transform (FFT) and visual cryptography. The method uses
DWT and FFT to achieve the invariant domain of the original host
image. Then, random location of the FFT matrix is selected to in-
sert the watermark image. The largest singular values which corre-
spond to the most conceptually important regions in the image are
selected as reliable features. A master share is constructed based
on these reliable features. Finally, an owner share produced from
the master share and the watermark image.
In this paper, a new robust semi-blind watermarking scheme
based on block classification and visual cryptography is proposed.
Considering stability of image edges and avoiding a few unsuit-
able blocks for watermarking, significantly increases the robustness
of the proposed algorithm. Since the proposed algorithm is totally
performed in spatial domain, it has low computational complexity
and it efficiently considers the local features of different regions of
the image. In our proposed scheme, the original image is divided
into 8 × 8 non-overlapping blocks. Then, these blocks are classified
into smooth and non-smooth classes by using the canny edge de-
tection technique and the SVMs classification method. In the next
stage, a master share is generated according to the blocks classifi-
cation result. Finally, the master share and binary watermark data
are used to construct an owner share based on the (2,2) VC tech-
nique. Experimental results show that the proposed method is ef-
ficiently robust against common image processing attacks such as
JPEG compression, Gaussian noise addition, filtering, and sharpen-
ing.
The rest of this paper is organized as follows: Section 2 de-
scribes the background regarding the canny edge detector, visual
cryptography and support vector machine. The proposed water-
marking scheme is explained in Section 3. The experimental re-
sults are presented in Section 4. Conclusions are finally drawn in
Section 5.
2. Background
In this section, the concepts of canny edge detector, visual cryp-
tography and support vector machines are briefly described.
2.1. Canny edge detection
Edge pixels are local variations of intensity in an image that de-
termine boundaries. The main goal of edge detection methods is
to reduce the amount of data in an image, while keeping the ba-
sic features of the image. Although many edge detection methods
have been proposed, some of them such as Sobel detector, Pre-
witt detector, and canny detector are more popular. Canny edge
detection is an optimal edge detection method proposed by John
F. Canny [28] in 1986. This edge detection algorithm is performed
based on the following steps:
• Smoothing: Apply Gaussian smoothing filter in order to remove
the noise.
• Calculating gradients: Find edges where the intensity changes
the most.
• Non-maximum suppression: is a method for edge thinning by
finding all local maxima in the gradient image.
• Hysteresis thresholding: Some of the edges detected in the
previous steps might be resulted from noisy pixels. For solv-
ing this problem two thresholds T1 and T2 (T2 > T1) are used.
Only edge pixels with gradient magnitude value stronger than
threshold T2 or between T1 and T2 would be preserved. Edge
pixels weaker than the low threshold are suppressed.
2.2. Visual cryptography
The idea of Visual Cryptography (VC) was proposed by Naor and
Shamir [29] in 1995 as a technique for encrypting and decrypting
the binary image. In a visual cryptography technique with parame-
ters (k, n), where k ≤ n, a binary visual secret image is decomposed
into n sharing images. The retrieving process of the VC scheme
does not require a complex computation mechanism. Instead, it
can be retrieved directly by the human visual system (HVS). There-
fore, the secret image can be visually recovered by overlaying k or
more than k sharing images, while any k-1 shares would display

3. A. Fatahbeygi and F. Akhlaghian Tab / Journal of Information Security and Applications 45 (2019) 71–78 73
Fig. 1. An example of (2,2) VC technique.
no information about the original image. In this paper, a (2,2) VC
method is used for the proposed watermarking scheme. In the en-
cryption process, each binary pixel of the secret image (watermark
data) is expanded into four (2 × 2) pixel blocks. Each blocks con-
taining an equal number of black and white pixels to the corre-
sponding two sharing images. In the decryption process, the secret
image can be retrieved by stacking the two sharing images. Thus,
the blocks corresponding to black secret pixels will have four black
pixels and those blocks corresponding to white secret pixels will
have two black pixels and two white pixels. Fig. 1 shows an exam-
ple of the (2,2) VC scheme performing on the binary image. Table 1
demonstrates the concept of the (2,2) VC scheme.
2.3. Support vector machine
In machine learning, classification algorithm is a learning pro-
cedure in which the computer program learns from the set of data
input given to it and then uses this learning to categorize new ob-
servation. Several machine learning techniques such as naïve Bayes
classifier, artificial neural networks, decision trees and support vec-
tor machines (SVMs) have been used for classification problems.
The SVM can overcome over-fitting weakness and provides better
classification result than other mentioned methods. The support
vector machines learning method was originally invented based on
the statistical learning theory (STL) by Vapnik [30] in 1990. SVMs
are widely used for solving pattern classification and function ap-
proximation problems.
The main advantages of SVMs include the absence of local min-
ima, solving the over-fitting problem and better classification. The
purpose of SVMs is to find the optimal hyperplane as a decision
boundary that maximizes the minimum distance (margin) between
training data points of different classes with the decision boundary.
Therefore, larger margin results in lower error of the classifier.
Given a dataset in the form where xi = (x1i, x2i, …, xki), (i = 1, …,
M) indicates a sample vector in k-dimensional space Rk, and yi de-
fines the labels of training samples as:
yi =
1 i f xi class I
−1 i f xi class II
(1)
The separating hyperplane is described by the equation:
f (x) = wT
x + b =
k
j=1
wjxj + b = 0 (2)
The SVM searches weight vector w and the scalar b for an opti-
mal hyperplane by maximizing the margin between classes. The
margin of the above hyperplane is described as the sum of the
minimum distances from the hyperplane to the support vectors of
class I and also the class II support vectors, which is equal to:
M = x+
− x−
.
w
w
=
2
w
(3)
x+ and x− , are the support vectors of the two classes. Margin M
is equal to 2
w
. Thus, the maximum margin is obtained by min-
imizing w . To find the optimum values of w and b, the opti-
mization problem solved by the SVM is equivalent to the following
minimization task:
⎧
⎨
⎩
Minimize 1
2
w + C
N
i=1
ξi
Subject to yi wT
∅(xi) + b ≥ 1 − ξi
⎫
⎬
⎭
(4)
where C denotes the penalty parameter to vectors misclassification
and ξi are slack variables.
For nonlinear classification, training patterns are first mapped
into a high dimensional space using kernel functions. Three kernel
functions that are used frequently are:
Polynomial : K xi, xj = (γ xT
i xj + c
q
(5)
Radial basis function : K(xi, xj ) = exp −
xi − xj
σ|2
2
(6)
Sigmoid function : K(xi, xj ) = tanh(γ (xT
i xj + r) (7)
with the decision function:
f (x) = sign
n
j=1
yjαjK x, xj + b (8)
3. Proposed scheme
In this section, we present a novel watermarking scheme in
detail. The proposed algorithm consists of three phases: the SVM
training phase, the owner share construction phase and the water-
mark extraction phase. The details of these three phases are given
below.
3.1. SVM training
We use two image sources with a size of M1 × M2 pixels for
generating the SVM training set. The first one is the original host
image H. The second source is five images generated by corrupting

4. 74 A. Fatahbeygi and F. Akhlaghian Tab / Journal of Information Security and Applications 45 (2019) 71–78
Table 1
Concept of (2,2) visual cryptography scheme.
the original image with five different attacks including Average fil-
ter (3 × 3), Gaussian noise (0.01), JPEG compression with compres-
sion ratio (20%), Median filter (3 × 3) and rotation (1°). These five
corrupted images are referred to as H1, H2, H3, H4, H5 respectively.
The details of generating dataset for SVM training with radial basis
function can be described as follows:
• Perform canny edge detector with specified threshold (α) and
variance (σ) on H, H1, H2, H3, H4, H5 to get their strong edge
images E, E1, E2, E3, E4, E5.
• Decompose edge images E, E1, E2, E3, E4, E5 into 8 × 8 non-
overlapping blocks respectively.
• For each block, if the number of edge pixels is greater than
a threshold (T), then the block is considered as a non-smooth
block. Otherwise, the block is considered as a smooth block.
• Obtain each block label according to:
Labelij =
+1 i f (blocktypeij = nonsmooth)
−1 i f (blocktypeij = smooth)
Where i and j are respectively the indices of the rows and
columns of the blocks. It should be noted that, in the proposed
method, each image source is decomposed into several 8 × 8 non-
overlapping blocks. Therefore, the range of the indices i and j is
bounded to 1 to
M1
8 and 1 to
M2
8 , respectively.
• Gradient magnitude of all edge pixels is calculated. Half of the
largest gradient magnitudes of each block (32 values for 8 × 8
blocks) are selected to construct sample feature vector Sij.
• Construct the SVM training set with Sij and Labelij:
Dij = Sij, Labelij
Train SVM classifier with training set Dij.
3.2. Owner share construction
Let the original image H be a gray level image with a size of
M1 × M2 and the watermark image W be a binary image of size
N1 × N2. The process of owner share construction is presented as
follows:
1. Perform canny edge detector with specified threshold (α) and
variance (σ) on the original image H to get edge image E.
2. Decompose edge image E into 8 × 8 non-overlapping blocks.
3. For each block, gradient magnitude of all edge pixels is calcu-
lated.
4. Half of the largest gradient values of each block are selected to
construct input vectors S ij.
5. Feed the trained SVM with S ij to get the set Label ij of input
vectors. The output of the classifier indicates the smooth or
non-smooth blocks.
6. Assume that M is a Master share of size 2N1 × 2N2 pixels. This
Master share is divided into non- overlapping 2 × 2 blocks and
is generated by the following generation rule:
If Label ij indicates non-smooth block then
mr =
1 0
0 1
Else if Label ij indicates smooth block then
mr =
0 1
1 0
Where “0” and “1” within block mr (1 ≤ r ≤ N1 × N2), represent a
black pixel and a white pixel, respectively.
1 Finally, the owner share O of size 2N1 × 2N2 is constructed by
using master share M and binary watermark W according to
the following algorithm:
If wr = 1 then or = mr
Else if wr = 0 then or =
1 1
1 1
− mr
Note that the canny edge detector threshold (α), variance (σ)
and owner share image O should be kept by the owner for the
watermark extraction phase.
3.3. Watermark extraction
The watermark extraction phase can be performed by the fol-
lowing algorithm:
1. Perform canny edge detector with specified threshold (α) and
variance (σ) on the suspected original image H to get edge im-
age E .
2. Decompose edge image E into 8 × 8 non-overlapping blocks.
3. For each block, gradient magnitude of all edge pixels is calcu-
lated.
4. Half of the largest gradient values of each block are selected to
construct input vectors S ij .
5. Feed the trained SVM with S ij to get the set Label ij of in-
put vectors. The output label indicates a smooth or non-smooth
block.
6. Generate the master share M according to the master share
generation rule.

5. A. Fatahbeygi and F. Akhlaghian Tab / Journal of Information Security and Applications 45 (2019) 71–78 75
Fig. 2. (a)–(d) are test images and (e) is a watermark image.
7. Retrieve the watermark image W by stacking the master share
M and the owner share O.
8. Divide the binary watermark image W into non-overlapping
2 × 2 blocks wr (1 ≤ r ≤ N1 × N2).
9. Perform the reduction process to get the reduced watermark
image W as follows:
Wr =
0 i f ni
< 2
1 i f ni
≥ 2
Where ni
represents the number of white pixels contained in
block wr .
3.4. Avoiding weak blocks for watermarking
After performing the proposed watermarking method with the
three phases above, it is obvious that some blocks of the original
host image are not robust against several attacks. A robust block
is a block that recovers the original watermark correctly even after
several attacks. Therefore, the robust blocks will not change after
treating the different attacks. For example, if the block is smooth;
it will be smooth after any attack. On the other hand, a non-
robust block is a block which its smooth/non-smooth feature may
change after treating some of the attacks. To distinguish between
the robust and non-robust blocks, a non-robust block is defined
as a weak block that cannot recover the original watermark after
at least two kinds of the attacks. Therefore, the robustness of the
proposed watermarking method can be significantly improved by
avoiding these weak blocks for generating the master share. How-
ever, this idea leads to a little decrease in watermarking capacity.
To address this problem, we can use some random robust blocks
twice for constructing the master share. The addresses of the ro-
bust blocks which are used twice apparently increase the key in-
formation. To overcome this event, one can use a pseudo-random
generator seeded with a secret key which produces the addresses
of these blocks, sequentially.
4. Experimental results
In this section, the experimental results of the proposed
method are presented. Four popular gray level images with a size
of 512 × 512 are selected as original host images in Figs. 2(a)–(d),
referred to as “Lena”, “Boat”, “Pepper” and “Airplane” . Also a bi-
nary image with a size of 64 × 64 is used as watermark that shown
in Fig. 2(e). Optimal canny edge detector is performed using Mat-
lab with threshold α = 0.45 and variance = 3.5 . We choose thresh-
Fig. 3. JPEG compression attack (PSNR = 35.0214). (a) Compressed image. (b) Ex-
tracted watermark image. (c) Reduced watermark image (NC = 0.9997).
Fig. 4. Average filter attack (PSNR = 26.1834). (a) Average filtered image. (b) Ex-
tracted watermark image. (c) Reduced watermark image (NC = 0.9984).
old T = 8 in the SVM training phase based on images block size.
Also, if one block is not robust against at least two attacks, this
block is disregarded for generating the master share.
PSNR (Peak Signal-to-Noise Ratio) is employed as a measure
for evaluating the quality of the watermarked image. PSNR is de-
scribed by the following equation:
PSNR = 10 log10
2552
MSE
(9)
where the mean square error (MSE) between the original and wa-
termarked image is defined as:
MSE =
1
MN
M−1
i=0
N−1
j=0
H(i, j) − ˆH(i, j)
2
(10)
Furthermore, the Normalized Correlation (NC) coefficient is
computed using the original watermark W and the extracted

6. 76 A. Fatahbeygi and F. Akhlaghian Tab / Journal of Information Security and Applications 45 (2019) 71–78
Fig. 5. Median filter attack (PSNR = 27.6679). (a) Median filtered image. (b) Ex-
tracted watermark image. (c) Reduced watermark image (NC = 0.9956).
Fig. 6. Gamma correction attack (PSNR = 15.5945). (a) Image after Gamma correc-
tion. (b) Extracted watermark image. (c) Reduced watermark image (NC = 0.9926).
Fig. 7. Histogram equalization attack (PSNR = 19.1329). (a) Image after Histogram
equalization. (b) Extracted watermark image. (c) Reduced watermark image
(NC = 0.9946).
Fig. 8. Gaussian noise attack (PSNR = 15.5758). (a) Gaussian noised image. (b) Ex-
tracted watermark image. (c) Reduced watermark image (NC = 0.9970).
watermark W for evaluating robustness, which is defined as fol-
lows:
NC =
1
mn
m
i=1
n
j=1
Wi,j Wi,j
(11)
where Wi,j and W i,j represent the original and extracted watermark
respectively. The symbol denotes the exclusive-or (XOR) opera-
tion, and mn is watermark image size.
Fig. 9. Resizing attack (PSNR = 28.8412). (a) Image after resizing. (b) Extracted wa-
termark image. (c) Reduced watermark image (NC = 1).
Fig. 10. Rotation attack (PSNR = 16.2579). (a) Image after rotation. (b) Extracted wa-
termark image. (c) Reduced watermark image (NC = 0.9162).
Fig. 11. Sharpening attack (PSNR = 24.6147). (a) Image after sharpening. (b) Ex-
tracted watermark image. (c) Reduced watermark image (NC = 0.9934).
Security, quality and robustness are three important charac-
teristics of a watermarking scheme. The security refers to resis-
tance of a watermarking approach to malicious attackers who will
manipulate the watermark. It should be noted that, the security
of our proposed method is ensured by using canny edge detec-
tor with specified threshold (α) and variance(σ), block classifica-
tion and (2,2) visual cryptography technique as keys. Therefore,
the proposed method guarantees that no malicious attacker can
extract and manipulate the correct watermark without knowing
these keys.
In order to evaluate the robustness of the proposed water-
marking scheme, nine image processing attacks including JPEG
compression, Gaussian noise addition, average filtering, histogram
equalization, median filtering, rotation, resizing, gamma correction
and sharpening are applied. All attacks have been simulated using
Matlab platform. The results obtained by performing the proposed
watermarking method on the “Lena” image are shown in Figs. 3–
11. A brief description of attacks and obtained coefficients is pro-
vided below:
JPEG compression: One of the usual attacks that must be ver-
ified in watermarking algorithm is image compression. To check
the robustness against image compression, the host image is com-
pressed by JPEG with a compression ratio of 50%. The image qual-

7. A. Fatahbeygi and F. Akhlaghian Tab / Journal of Information Security and Applications 45 (2019) 71–78 77
Table 2
Experimental results of proposed scheme under different attacks.
Attacks Lena Boat
PSNR NC PSNR NC
PMWRNRB Proposed method PMWRNRB Proposed method
JPEG compression with QF = 50 35.0214 0.9965 0.9997 34.7122 0.9931 0.9992
Average filter 9∗
9 26.1834 0.9809 0.9984 23.9112 0.9787 0.9968
Median filter 9∗
9 27.6679 0.9831 0.9956 24.4858 0.9690 0.9924
Gamma correction 0.6 15.5945 0.9682 0.9926 16.0163 0.9736 0.9985
Histogram Equalization 19.1329 0.9692 0.9946 17.3584 0.9345 0.9687
Gaussian noise addition 15.5758 0.9760 0.9970 15.6550 0.9722 0.9958
Resizing 28.8412 0.9946 1 26.2787 0.9870 0.9985
Rotation 16.2579 0.8974 0.9162 16.8712 0.8907 0.9081
Sharpening 30.6147 0.9804 0.9934 32.0557 0.9829 0.9980
Table 3
Experimental results of proposed scheme under different attacks.
Attacks Airplane Peppers
PSNR NC PSNR NC
PMWRNRB Proposed method PMWRNRB Proposed method
JPEG compression with QF = 50 36.5805 0.9916 0.9992 34.8038 0.9912 0.9995
Average filter 9∗
9 24.2724 0.9738 0.9970 26.8124 0.9722 0.9941
Median filter 9∗
9 25.1240 0.9648 0.9921 28.0390 0.9738 0.9934
Gamma correction 0.6 16.3418 0.9708 0.9970 16.1820 0.9697 0.9986
Histogram Equalization 11.8429 0.9328 0.9745 18.3902 0.9702 0.9914
Gaussian noise addition 15.9707 0.9697 0.9931 17.9825 0.9697 0.9952
Resizing 25.5886 0.9750 0.9990 30.6429 0.9868 0.9992
Rotation 16.5562 0.9074 0.9258 13.7825 0.8715 0.8918
Sharpening 30.4578 0.9838 0.9985 30.2590 0.9845 0.9992
Table 4
Comparison with existing algorithms for evaluating the robustness of our scheme.
Attacks Hsu et al.
scheme [20]
Rawat et al.
scheme [24]
Wu et al.
scheme [8]
Tayebe et al.
scheme [25]
Makbol et al
scheme [26]
Ali et al.
scheme [27]
PMWRNRB Proposed
method
JPEG
compression
with QF = 50
0.9690 0.9960 0.9956 0.9986 0.9858 0.9985 0.9931 0.9994
Average filter
9∗
9
0.9209 0.9752 0.9760 0.9964 0.9538 0.9910 0.9764 0.9965
Median filter
9∗
9
0.9350 0.9812 0.9893 0.9920 0.9687 0.9955 0.9726 0.9933
Gamma
correction 0.6
0.9338 0.9751 0.9787 0.9878 0.9586 0.9862 0.9705 0.9966
Histogram
equalization
0.9031 0.9307 0.9492 0.9845 0.9076 0.9822 0.9516 0.9823
Gaussian noise
addition
0.7624 0.9686 0.9748 0.9927 0.9055 0.9983 0.9719 0.9952
resizing 0.9057 0.9912 0.9926 0.9984 0.9459 0.9978 0.9858 0.9992
Rotation 0.7368 0.8892 0.8915 0.9180 0.7825 1.0000 0.8917 0.9104
Sharpening 0.8957 0.9948 0.9952 0.9987 0.9887 0.9979 0.9829 0.9972
Average 0.8847 0.9668 0.9714 0.9852 0.9330 0.9941 0.9662 0.9855
ity of the compressed image is 35.0214. The NC of the extracted
binary watermark is 0.9997.
Average filtering: The most common manipulation in a digital
image is smoothing. The image is blurred by applying average filter
with a 9 × 9 window. The filtered image has a PSNR of 26.1834. The
NC value of the extracted watermark is 0.9984.
Median filtering: A median filter with a 9 × 9 window is applied
to the image. The PSNR of the median filtered image is 27.6679.
The NC value of the extracted watermark is 0.9956.
Gamma correction: The gamma of the original image is reduced
to a value of 0.6. The quality of the image is reduced to 15.5945.
The NC of the extracted logo is 0.9926.
Histogram equalization: Histogram equalization is a contrast
enhancement method. The PSNR of the image is reduced to
19.1329. The NC of the logo extracted from the image after his-
togram equalization attack is 0.9946.
Gaussian noise: The test image is damaged by adding 30% Gaus-
sian noise. The noisy image has a PSNR of 15.5758. The NC of the
extracted watermark is 0.9970.
Resizing: We first down-sized the image from 512 × 512 to
128 × 128 pixels and then up-sized the image back to the original
size. The quality of the rescaled image is reduced to 28.8412 and
the NC of the extracted watermark is 1.
Rotation: Rotation clockwise, by a small amount (3°) is enough
to disarrange the entire bitmap. The PSNR of the rotated image is
16.2579. The NC of the revealed watermark after a 3° rotation is
0.9162.
Sharpening: The main goal of sharpening is to highlight details
in an image. The quality of the sharpened image is 24.6147. The

8. 78 A. Fatahbeygi and F. Akhlaghian Tab / Journal of Information Security and Applications 45 (2019) 71–78
extracted watermark has an NC value of 0.9934 after sharpening
attack.
The experimental results of the proposed method and the pro-
posed method without removing non-robust blocks (PMWRNRB)
for all images are summarized in Tables 2 and 3. We can see from
the tables that NC values of all the extracted watermark images
are close to 1, which shows that the proposed scheme is very effi-
cient for resisting various kinds of image processing attacks. In or-
der to further test the effectiveness of the proposed watermarking
scheme, we compared our method with six other visual cryptogra-
phy based watermarking schemes, and the results of the NCs are
given in Table 4.
The four host images and the binary watermark image, as
shown in Fig. 2, were used in the experiments. It can be concluded
from Table 4 that the NC values which are obtained by [20] and
[26], show the weak resistance of these schemes. The experimen-
tal results show that the methods which are proposed in [24] and
[8] are robust against JPEG compression, filtering, gamma correc-
tion, resizing and sharpening attacks. However, their schemes are
powerless to prevent noise addition, rotation and histogram equal-
ization attacks. The proposed method in [25] obtained higher NC
values than the other previously mentioned methods for all at-
tacks. Our proposed scheme has high NC values for all the image
processing attacks. When compared with Ali [27] method we see
that for JPEG compression, average filtering, gamma correction, his-
togram equalization and resizing attacks, NC values in our method
is higher than Ali method. Hence we can conclude that our scheme
has strong robustness against all the attack and perform better
than the existing methods for most of the attacks.
5. Conclusion
In this paper a new scheme for copyright protection based on
block classification and visual cryptography has been presented.
Our method is completely imperceptible because the binary water-
mark is concealed without modifying the original image, and it can
be revealed for rightful ownership by stacking two share images
without the aid of computers. Also, the combination of canny edge
detector, block classification and visual cryptography was used to
improve the security of the proposed watermarking scheme. Due
to the use of local and robust features of different regions of the
image and avoidance of some weak blocks for watermarking, the
robustness of the proposed scheme has been improved. The exper-
imental results show that the presented technique can effectively
resist common image processing attacks, such as JPEG compres-
sion, Gaussian noise, average filtering, histogram equalization, me-
dian filtering, gamma correction, rotation, resizing and sharpening.
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