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A robust color image watermarking technique using modified Imperialist Competitive Algorithm
Accepted Manuscript Title: A Robust Color Image Watermarking Technique Using Modified Imperialist Competitive Algorithm Author: Mohsen Ebrahimi Moghaddam Nasibeh Nemati PII: DOI: Reference:
S0379-0738(13)00413-1 http://dx.doi.org/doi:10.1016/j.forsciint.2013.09.005 FSI 7347
To appear in:
FSI
Received date: Revised date: Accepted date:
27-11-2012 21-8-2013 3-9-2013
Please cite this article as: M.E. Moghaddam, N. Nemati, A Robust Color Image Watermarking Technique Using Modified Imperialist Competitive Algorithm, Forensic Science International (2013), http://dx.doi.org/10.1016/j.forsciint.2013.09.005 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Title Page (with authors and addresses)
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A Robust Color Image Watermarking Technique Using Modified Imperialist Competitive Algorithm
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Mohsen Ebrahimi Moghaddam*, Nasibeh Nemati Electrical and Computer Engineering Department, Shahid Beheshti University G.C, Tehran, Iran
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*corresponding Author : [email protected] Tel: +989121405308 , Address: Electrical and Computer Engineering Department, Shahid Beheshti University, Evin Ave, Tehran , Iran
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*Manuscript (without author identifiers)
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A Robust Color Image Watermarking Technique Using Modified Imperialist Competitive Algorithm
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Abstract— In this paper, a novel robust watermarking technique using Imperialistic Competition Algorithm (ICA) in the spatial domain is proposed to protect the intellectual
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property rights of color images. The proposed method tries to insert the watermark in the blocks which are selected by Modified ICA. In this method, ICA has been customized for
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watermarking. The color band for watermark insertion is selected based on color dynamic range in each block. Besides, in the procedure of selecting blocks for watermark insertion and
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extraction, ensuring higher fidelity and robustness and resilience to several possible image attacks have been considered. The experimental results showed that the proposed method
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performance created watermarked images with better PSNRs and more robustness versus
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several attacks such as additive noise, blurring, etc in compare to related works.
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Index Terms—Color image, Robust watermarking, Imperialistic Competition Algorithm (ICA).
1.
Introduction
Digital media security is a principal concern in the today technological world. Therefore, illegal distribution, duplication, and modification of the media posed as a serious problem on protecting intellectual property. Protection of digital multimedia content has been furthermore become an increasingly important issue for content owners and service providers. On the contrary, digital watermarking is one of the growing and promising technologies to protect the digital data from being tampered and also widely used for copyright protection.
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2 Watermarking is the process of embedding a watermark into the digital media to protect the intellectual property rights. The watermarking process can be performed by visible or invisible embedding [1][19]. Visible watermarking is the process of embedding the watermark into some visible parts of the digital media while invisible watermarking embeds the watermark confidentially
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into the parts of the digital media known only by the owner. In the other hand, invisible watermarking process categorized as fragile, semi fragile and robust watermarking. Fragile watermarking is used to
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determine the slight modifications on the digital media, while semi-fragile watermarks commonly are
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used to detect malignant transformations. Robust watermarking is used to determine the owner of the digital media even if it is modified.
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Digital watermarking is classified according to the embedding domain into two classes namely spatial and transform domain [2]. In spatial domain embedding is done by directly manipulating the
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pixel values of the host media. Techniques in this category are less complex but they show poor robustness against common image processing attacks. Another category is transform domain
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techniques [3]. Some techniques in this group transform the original media to frequency domain by using some transformations such as discrete Fourier transformation, discrete Cosine transformation and discrete Wavelet transformation [4]. Then, the watermark is embedded by modifying coefficients
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and watermarked image is obtained by inverse transformation of the modified coefficient. Some other methods in this group may apply some transforms such as SVD (Singular Value Decomposition) [20], calculating moments [23] or other processing techniques to insert the watermark.
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Moreover, the extraction of the watermark [3] can be classified into the following categories: Firstly, non-blind scheme which requires the original image for the watermark extraction; is robust against various image processing attacks. Secondly, semi blind scheme which does not require the original image for the process of extraction, but it needs the watermark and additional information in this phase. And lastly the blind scheme, that does not require the original image, watermark and additional information but only secret key for the watermark extraction. The watermarking approach may be applied on gray or color images. However, there are a lot of papers in gray image watermarking in literature but it is possible to find some watermarking
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3 approaches which have been developed to embed a watermark into a color image. For example, an algorithm for still color image based on double scrambling technique and the wavelet transform has been proposed by S. Hongqin and L.V Fangliang [4]. In this method, the original color image is transformed from RGB to the YUV color space and scrambled by Fibonacci transformation. Tsui et.
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al. Method [5] encodes the chromatic content of a color image as CIE a* b* chromaticity coordinates, and color watermarks (yellow and blue) are embedded in the frequency domain of the chromatic
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channels by using the Spatial Chromatic Discrete Fourier Transform (SCDFT). S. Das et.al proposed
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a method that inserts the watermark in the LSB of ‗‘Blue‖ band of each pixel in individual tiles of host image in RGB color space. A robust blind method has been proposed in [20 ]. This method
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inserts the watermark by linear interpolation in all SVD components matrixes (U, S, V) of Red channel. There is another robust SVD based method for gray images in the literature [21] which
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modifies the singular value of cover image by multiple scale factor. However, the authors in [22] tried to show that proposed method in [ ] is not robust and has many false watermark detection.
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A semi-fragile watermarking approach based on fractal compression and differentials record theory which was restorable and robust against rotation was proposed by AnHu et.al [6]. In this method,
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three components (R, G, B) of a color image are processed respectively by fractal compression. A color image watermarking system of color images based on fast Quaternion Fourier Transform (QFT) has been proposed by Wei-jiang Wang et.al [7]. Quaternion is a type of hyper-complex number. Sanjay Rawat and Balasubramanian Raman worked on YCbCr color space [4]. Their proposed
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method was based on discrete Cosine transform, wavelet packet transform and singular value decomposition. Qian-Chuan Zhong et.al proposed using the chaotic maps in the YCbCr color space [8]. They used Lorenz map and the Arnold cat map to encrypt the watermark signal and find embedding position of the host image. A watermarking algorithm for color image based on HSI color space and Discrete Wavelet Transform (DWT) has been addressed in [9]. In this method, sub-blocks of intensity are divided and sorted according to the human visual system (HVS) characteristic. Then, binary watermark is embedded by modifying the DWT coefficients of low frequencies in the selected sub-blocks. A spatial
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4 blind, semi-fragile method has been proposed based on totalistic cellular automata in [10]. Sujoy Roy and Ee-Clzien Chang [11] presented a method for watermarking color histograms that uses Earth Mover Distance (EMD) to modify an image to a target histogram. Recently some methods have been proposed for color image watermarking based on artificial
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intelligence techniques which try to predict the relationship between the values of the pixels within the embedding watermark block and the watermark itself. For example, Pao-Ta Yu et. al. proposed a
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watermarking technique based on neural networks for color images [12]. This method cooperates
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neural networks to memorize the relations between a watermark and the corresponding watermarked image. Due to neural networks possessing, the learning and adaptive capabilities, the trained neural
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networks recover the watermark from the watermarked image against image processing attacks. A SVM-based Color Image Watermarking (SCIW) has proposed by Tsai et. al. [13] which employs a
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classification technique based on SVMs to extract a watermark from color images. Three image features, which are different among local image statistic and the luminance value of the centre pixel in three sliding windows with distinct shapes, were used to construct the input vectors of the training
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patterns. Following the results produced by the classifier (the trained SVM), the SCIW method retrieves the hidden signature without the original image during watermark extraction. Oğuz Fındık et.
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al. suggested a watermarking technique that uses Artificial Immune Recognition System(AIRS) to protect color image intellectual property rights [14]. The watermark is embedded in the blue channel of a color image. m-bit binary sequence embedded into the color image is used to train AIRS.
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Extracting the watermark is carried out using AIRS. Another watermarking technique has been introduced by previous writers [15] based on Particle Swarm Optimization (PSO) and k-nearest neighbour algorithm. In the embedding process, the color image is separated into non-overlapping blocks and each bit of the binary watermark is embedded into an individual block. By using the symmetric cross-shape, features are extracted from the blocks in which the watermark is embedded and the control blocks which would be used as training data in the extraction algorithm. Two centroids belonging to each of the watermark binary values are obtained by PSO utilizing the features obtained from the control blocks.
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5 A watermarking technique which uses genetic algorithm to identify locations within the cover image for watermark insertion is designed by [16] in spatial domain. Genetic search often produces same watermark locations in different populations for watermark insertion resulting in poor value of fidelity and robustness which need to be checked. Sliding window concept uses a set of
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a few genes which are serially shuffled to get new set of locations for watermarking during each population generation.
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However, several watermarking techniques in spatial and frequency domain have been
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proposed by various researchers in color image watermarking but most of them suffers from problems of poor robustness and fidelity.
blind watermarking technique based on Imperialist Competitive
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In this paper, a new robust
Algorithm (ICA) for digital color images has been proposed. First of all, the proposed method
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identifies the locations within the host image for watermark insertion in spatial domain by using modified ICA. The modified ICA has an extra step in compare to ICA which makes it more proper for
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watermarking. Second, a neighbourhood of 5*5 pixels is chosen surrounding the selected locations and least significant color in neighbourhood of each pixel is chosen for embedding. Selecting best
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positions using ICA and using least significant color channel in each neighbourhood are somewhat novel in this paper that increase the quality of watermarked image. Furthermore, another concept have been used in insertion and extraction for ensuring higher fidelity and robustness and resilience to several possible image attacks. The proposed method was applied on the several bench-mark images
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and the experimental results showed method performance in resulted image quality together with its robustness versus different attack by check board software. The rest of this paper is organized as follows. In Section 2, Imperialist Competitive Algorithm is described. Section 3 introduces proposed algorithm for optimization of Robustness and Fidelity using Imperialist Competitive Algorithm. Section 4 shows experimental results and Conclusion is given in section5.
2. Imperialist Competitive Algorithm
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6 ICA is an algorithm for optimization inspired by the imperialistic competition [17]. The goal of optimization is to find an optimal solution in terms of the variables of the problem. This algorithm forms an array of variable values to be optimized. In GA terminology, this array is called ―chromosome‖, but here the term ―country‖ is used for this array. Like other evolutionary ones, this
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algorithm starts with an initial population of size Npop. Population individuals called country are in two types: colonies and imperialists that all together form some empires. Algorithm selects Nimp of the
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most powerful countries to form the empires. The remaining Ncol of the population will be the colonies
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each of which belongs to an empire. Imperialistic competition among these empires forms the basis of the proposed evolutionary algorithm. Imperialistic competition is the main part of proposed algorithm
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and causes the colonies to converge to the global minimum of the cost function. During this competition, weak empires collapse and powerful ones take possession of their colonies. Imperialistic
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competition converges to a state in which there exists only one empire. The Fig. 1 describes a typical imperialist competitive algorithm in its simplified form.
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1. Generating Initial Empires
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The core components of the ICA are as follows:
2. Moving the Colonies of an Empire toward the Imperialist 3. Exchanging Positions of the Imperialist and a Colony 4. Total Power of an Empire
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5. Imperialistic Competition
6. Eliminating the Powerless Empires 7. Convergence
3.
Proposed watermarking algorithm
The proposed method is based on ICA to find the best place for watermark insertion in least significant color band. The overall structure of proposed algorithm is shown in Fig. 2. In next subsections, the steps of proposed method are described in more details.
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7 3.1
Watermark embedding
Step 1: Let the host image (h
) used to embed the watermark as:
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(1)
(2)
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and the watermark image (wimage) is as follows:
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Step 2:
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This produces a row vector.
entries as follows:
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Host image is reshaped into row vector containing
(3)
(4)
The size of the host image is given as
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Step 3:
The watermark image is converted into a single dimension vector as follows: (5)
The total number of watermark locations is given by Step 4: In order to find optimal locations for insertion, apply MICA.
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8
Start
Host image
Watermark image
Initialize the impires
Convert host and watermark images into vectors
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Move the colonies toward their relevant imperialist
Is there a colony in an empire which has lower cost than that of imerialist
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apply MICA to find best insertion location
Yes
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No
Exchange the posotion of that imperialist and the colony
Find the color that has the least quantity in each selected location neighborhood
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No
Is there an empire with no colonies
No
Optimal regions for embedding
No
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Yes
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Pick the weakest colony from the weakest empire and give it to the empire that hast the most likehood to possess it
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Compute the total cost of an empire
Termination
Eliminate this empire
Stop condition satisfied Yes
Yes
Watermarked image
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Done
Fig.
1:
Flowchart
of
typical
imperialist competitive algorithm.
Fig. 2: Steps of the proposed watermark embedding algorithm
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9 Step 4-1: The arrays
and
which contain best locations for insertion and
total insertion cost in these locations are initialized to
. (6)
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(7)
Step 4-2 Generating Initial Empires:
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Step 4-2-1:
. A country is a
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To start the optimization algorithm the initial population size is considered as
(8)
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array. This array is defined by:
The variable values in the country show the position of corresponding index in the host image. ( values are randomly assigned).
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So Step 4-2-2:
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For the purpose of watermark insertion, a
mask is considered as the neighbourhood of the
selected location. In each neighbourhood, via Eucledian distance the least significant color is found by
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following equations:
(9)
(10)
(11)
(12)
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10 Step 4-2-3: Afterwards, another
mask is considered as the neighbourhood of the selected location. This
process takes place on the color channels according to results of step 4-2-2 for each selected pixel. The pixel intensity value of the selected pixel is modified by
,
In this equation,
is embedding strength and neighbours.
shows watermark bit while
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is the average of
(13)
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according to average neighbourhood pixel intensity as follows:
Step 4-2-4:
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The cost of a country is found by evaluating the cost function at the variables
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so:
(14)
The similarity between the modified host image and the original host image is termed as cost
When
(15) (16)
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fitness function calculated as following equation:
is given by:
(17) Step 4-2-5: Now max cost and best individual positions are derived when:
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11 (18)
Step 4-2-6: We select Nimp of the most powerful countries to form the empires. The remaining Ncol of the
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population will be the colonies each of which belongs to an empire. Then two types of countries
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exists; imperialist and colony. Step 4-2-7:
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To form the initial empires, the colonies are divided among imperialists based on their power. That is the initial number of colonies of an empire should be directly proportionate to its power. To divide
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the colonies among imperialists proportionally, the normalized cost of an imperialist is defined as:
is the cost of nth imperialist and
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Where
(19)
is its normalized cost. Having the normalized cost of
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all imperialists, the normalized power of each imperialist is defined by:
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(20)
From another point of view, the normalized power of an imperialist is the portion of colonies that should be possessed by that imperialist. Then the initial number of colonies of an empire is:
is the initial number of colonies of nth empire and Ncol is the number of all colonies.
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Where
(21)
To divide the colonies, for each imperialist, choose
of the colonies randomly and give them to
it. These colonies together with the imperialist form nth empire. Step 4-3 Moving the Colonies of an Empire toward the Imperialist: At this level imperialist countries start to improve their colonies. This fact has modelled by moving all the colonies toward the imperialist. In this movement, colonies moves toward the imperialist by units. The direction of the movement is a vector from colony to imperialist. with uniform (or any proper) distribution. Then for
is a random variable
we have:
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12 (22) Where U means uniform distribution, colony and imperialist.
is a number greater than
is the distance between
causes the colonies to get closer to the imperialist state from both
sides.
values of
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Where
is a random number with uniform (or any proper) distribution. Then:
(23)
is a parameter that adjusts the deviation from the original direction. Nevertheless the are arbitrary.
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Step 4-4 Exchanging Positions of the Imperialist and a Colony:
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movement.
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To search different points around the imperialist add a random amount of deviation to the direction of
While moving toward the imperialist, a colony may reach to a position with higher cost than that of
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imperialist. In such a case, the imperialist moves to the position of that colony and vise versa. Then
position. Step 4-5 Find Repetition:
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algorithm continues by the imperialist in a new position and then colonies start moving toward this
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During watermarking with ICA, there is always a possibility of getting the similar locations for watermark insertion. So we add a new step to this algorithm. In this step, to prevent repetition, elements are examined and their repetitive values are replaced with a new random value. To avoid
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sensible variation, this new value is within the previous value scope. The repeated use of this technique ensures different watermark locations to be selected and thus avoiding the limitation and probability of finding same locations in the search space. This causes enhanced robustness and fidelity as indicated by various experiments. Step 4-6 Total Power of an Empire: Total power of an empire is mainly affected by the power of imperialist country and the colonies of an empire. This fact has modeled by defining the total cost by (24)
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13 Where
is the total cost of the nth empire and
less than 1. A little value for
is a positive number which is considered to be
causes the total power of the empire to be determined by just the
imperialist and increasing it will increase the role of the colonies in determining the total power of an empire. We have used the value of 0.1 for
in our method.
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Step 4-7 Imperialistic Competition:
To start the competition, first, find the possession probability of each empire based on its total
(25)
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Where
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power. The normalized total cost is obtained by:
are total cost and normalized total cost of nth empire respectively.
and
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Having the normalized total cost, the possession probability of each empire is given by:
To divide colonies among empires, we form the vector
(26)
based on possession probability of
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mentioned colonies as: (27)
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Then we create a vector with the same size as whose elements are uniformly distributed
(28)
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random numbers.
Then we form vector
by simply subtracting
from .
(29)
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14 Referring to vector
we will hand the mentioned colonies to an empire whose relevant index in
is maximum. Step 4-8 Eliminating the Powerless Empires: Powerless empires will collapse in the imperialistic competition and their colonies are assigned to
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other empires.
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Step 4-9 Convergence:
After a while all the empires except the most powerful one will collapse and all the colonies will be
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under the control of this unique empire. Step 4-10 one unit.
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Increase
Repeat steps 4-3 to 4-9 until
.
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Now the best locations in this iteration and the total cost are obtained Step 5:
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MakeKey function creates the key according to location of each watermark pixel in
. Also,
in each key index, the color channel which insertion taken place on that is saved.
Step 1:
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3.2. Watermark extracting
For extracting watermark by key which was made in MakeKey function we can simply extract
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inserted watermark. In the first place, reshape the watermarked image(
) into row vector:
(30)
This produces a row vector: (31) Step 2: pursuing this further for each index of key, find the related position in the watermarked image:
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15
(32)
Step 3: then calculate the average pixel intensity of selected location
pixels surrounding the concerned pixel.
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channel in key, by using a neighborhood of
in mentioned color
finally
compare
the
Pixel
intensity
value
of
selected
location
specified
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(33)
Experimental Results
ed
4.
by
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, with selected location:
the
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Step 4:
In this study, the peak signal to noise ratio (PSNR) is used to qualify the watermarked image and
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the extracted watermark quality respectively. To measure the method robustness, the Mean Absolute Error (MAE) has been employed which measures the difference between an original signature
and
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the corresponding extracted signature . The PSNR and MAE are calculated as follows:
(34)
(35)
(36) Where MSE stands for Mean Square Error. A lower MAE reveals that the extracted signature resembles the original signature
more closely. The robustness of a watermarking method is assessed
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16 by comparing with , where is extracted from the watermarked image
which is further degraded
by attacks. If a method has a lower MAE, it is more robust. To test the performance of proposed method, various bench-mark color images such as Lena, Baboon, and F16 with size
and watermarks with different sizes, were employed. Also, to imag which was used in
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make the proposed method comparable with related ones a binary
the evaluation of related methods was employed. In the embedding process, the embedding strength and the dimension of the neighbors for insertion and extraction was a 3
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was used as
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kernel.
TABLE 1 represents the various parameters setting used in the experiments for MICA. In most for
and about
for
have resulted
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experimental results of ICA usage, a value of about
in good convergence of countries to the global maxmum, so we use the same.
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The watermarking performance of the proposed method is compared with some related methods such as Kutter, Yu, and SCIW in TABLE 2. To investigate the robustness of these methods, several
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attacks were simulated using check-board software to degrade the watermarked. Besides the quantitative results in terms of the PSNR and the MAE, the experiment also provides visual
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comparison results (in Figure. 3).
By considering the results we could see that not only the watermarked images by the proposed algorithm have good PSNR, we also could observe high quality extracted watermarks. Conclusion
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5.
In this paper, a new neighbourhood concept method is used for finding a suitable location in spatial domain via modified imperialistic competition algorithm (ICA) for watermark insertion. Using ICA helps us to find a more suitable location for inserting watermarks bits in different color channels. At the same time, because of using neighbourhood concept during watermark insertion and extraction, the proposed watermarking method is robust against various attacks unless jpeg compression. Moreover, this study is a novel one that uses different color channel for each neighbourhood and ICA for watermark embedding process in the watermarking concept according to
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17 the literature review. Besides, the proposed method could be explored in the frequency domain as a prospective work.
TABLE I: Proposed method Parameters Values 25
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5
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Parameters
200
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0.1 2
5*5 3*3 25
Baboon
Extracted watermark from Baboon
F16
Extracted watermark from F16
MAE=0.0029
PSNR=43.0466
MAE=0.0044
PSNR=44.6549
MAE=0.0061
PSNR=28.0783
MAE=0.0273
PSNR=28.3123
MAE=0.0234
PSNR=30.0847
MAE=0.0180
PSNR=24.5114
MAE=0.0446
PSNR=25.4611
MAE=0.0366
PSNR=24.5388
MAE=0.0378
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PSNR=45.6295
Noise 5%
Extracted watermark from Lena
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Lena
No-attack
ed
M
an
0.1
Noise 10%
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18
Blurring (0.1)
MAE=0.0131
PSNR=29.2967
MAE=0.0148
PSNR=35.2000
MAE=0.0617
PSNR=25.9538
MAE=0.0705
PSNR=24.0699
MAE=0.0119
PSNR=16.3320
PSNR=22.6527
MAE=0.0119
PSNR=15.3903
PSNR=38.0500
MAE=0.0166
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PSNR=38.4825
cr
Blurring twice
MAE=0.0178
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PSNR=34.7079
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Sharpening
PSNR=32.1533
MAE=0.0166
MAE=0.0043
PSNR=24.2855
MAE=0.0065
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Sharpening twice
MAE=0.0085
Fig. 3: Watermarked images Lena, Baboon and F16 and extracted waterm arks after the image processing attacks.
Attack number
1
Attacks
No-attack
Test image
Baboon F16 Lena Baboon F16 Lena Baboon F16 Lena Baboon F16 Lena Baboon F16 Lena Baboon F16
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0
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TABLE II: Comparison of proposed method with some related ones
Noise 5%
2
Noise 10%
3
Blurring (0.1)
4
Blurring twice
5
Sharpening
PSNR Proposed SCIW method 42.7600 43.8422 39.6300 44.6549 41.8300 45.6295 21.1300 30.0441 18.6900 30.0847 21.2600 30.2402 18.3100 24.3124 15.6500 24.5388 18.1200 24.5114 25.5100 29.2967 30.0500 38.6626 24.4900 38.4825 25.2100 25.9538 29.2000 34.7079 28.6800 35.2000 19.5700 23.4127 25.0700 32.1533
Kutter’s [18] 0.1745 0.0830 0.0754 0.1679 0.0563 0.2119 0.1936 0.1911 0.1240 0.2312 0.1372 0.0871 0.2731 0.3020 0.1176 0.1030 0.0158
MAE Yu’s Tsai [12] [13] 0.1333 0.0964 0.0148 0.0065 0.0312 0.0219 0.1391 0.1218 0.0249 0.0131 0.0595 0.0449 0.1870 0.1616 0.0458 0.0363 0.1201 0.1125 0.1867 0.1386 0.0290 0.01391 0.0495 0.0307 0.2280 0.1679 0.0493 0.0197 0.0708 0.0354 0.1071 0.0854 0.0102 0.0041
Proposed method 0.0044 0.0061 0.0029 0.0031 0.0068 0.0046 0.0598 0.0192 0.0192 0.0627 0.0305 0.0227 0.1245 0.0839 0.0659 0.0029 0.0061
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19
6
Sharpening twice
Lena Baboon F16 Lena
25.4600 13.5700 15.7600 15.1300
5.
31.7732 15.3903 24.2855 22.6527
0.0703 0.0817 0.0039 0.0195
0.0241 0.0959 0.0139 0.0222
0.0170 0.0793 0.0036 0.0175
0.0043 0.0043 0.0065 0.0119
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[3] Rawat, S.; Raman, B.; , "A new robust watermarking scheme for color images," IEEE 2nd
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Scrambling Technique," Third International Symposium on Intelligent Information Technology and Security Informatics (IITSI),, pp.781-785, 2-4 April 2010.
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[5] Tsz Kin Tsui; Xiao-Ping Zhang; Androutsos, D.; , "Color Image Watermarking Using the SpatioChromatic Fourier Transform," IEEE International Conference on Acoustics, Speech and Signal Processing, 2006, vol.2, pp.14-19 May 2006.
[6] An Hu; He Xiaolong; Zuo Yan; Cheng Sipeng; Zhang Bing; , "A Semi-fragile Watermarking algorithm against rotation based on fractal compression and differentials record theory," International Conference on Networking and Information Technology (ICNIT) , pp.153-156, 11-12 June 2010.
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[7] Wei-jiang Wang; Wei Zuo; Xue-mei Yan; , "New gray-scale watermarking algorithm of color images based on Quaternion Fourier Transform," Third International Workshop on
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Computational Intelligence (IWACI), pp.593-596, 25-27 Aug. 2010.
[8] Qian-Chuan Zhong; Qing-Xin Zhu; Ping-Li Zhang; , "A Spatial Domain Color Watermarking
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Analysis, 2008. ICACIA 2008. , vol., no., pp.137-142, 13-15 Dec. 2008.
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Scheme based on Chaos," International Conference on Apperceiving Computing and Intelligence
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[9] Fanzhi Kong; Yizhun Peng; , "Color image watermarking algorithm based on HSI color space," 2nd International Conference on Industrial and Information Systems (IIS) , vol.2, pp.464-467, 10-11
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July 2010.
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[10] Radulescu, M.; Ionescu, F.; Dogaru, R.; , "Improving Multimedia Techniques For Watermarking Color Images Using Cellular Automata," International Symposium on Signals, Circuits and Systems,
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2007, vol.2, pp.1-4, 13-14 July 2007.
[11] Roy, S.; Chang, E.-C.; , "Watermarking color histograms,", 2004. International Conference on
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Image Processing (ICIP), vol.4, pp. 2191- 2194, 24-27 Oct. 2004.
[12] Pao-Ta Yu; Hung-Hsu Tsai; Jyh-Shyan Lin; , "Digital watermarking based on neural networks for color images," Signal Processing, Vol.81, no.3, pp.V663-V671, March 2001
[13] Hung-Hsu Tsai; Duen-Wu Sun; , "Color image watermark extraction based on support vector machines ," Information Sciences, Vol.177, no.2,pp.V550-V569, 15 Jan. 2007 .
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21 [14] Oğuz Fındık; İsmail Babaoğlu; Erkan Ülker; , " A color image watermarking scheme based on artificial immune recognition system, " Expert Systems with Applications, vol.38, no.3,pp.V1942V1946, Aug. 2010.
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[15] Oğuz Fındık; İsmail Babaoğlu; Erkan Ülker; , "A color image watermarking scheme based on hybrid classification method: Particle swarm optimization and k-nearest neighbor algorithm," Optics
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Communications, vol.283, no.24, pp.V4916-V4922, 23 Jul. 2010.
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[16] Goyal, S.; Gupta, R.; , " Sliding Window used for Robustness and Fidelity Optimization Employing Neighborhood Concept and Genetic Algorithm with Tournament Selection," International
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[17] Atashpaz-Gargari, E.; Lucas, C.; "Imperialist competitive algorithm: An algorithm for in the proceeding of IEEE Congress on
ed
optimization inspired by imperialistic competition,"
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Evolutionary Computation , pp.4661-4667, 25-28 Sept. 2007.
[18] M Kutter; F Jordan; F Bossen; "Digital watermarking of color images using amplitude modulation, " J. Electron. Imaging , vol.7, no. 2, pp.326-332, April 1998.
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[19] S.Kiani, M.Ebrahimi Moghaddam, ―A multi-purpose digital image watermarking using fractal block coding‖, journal of systems and software, volume 84, issue 9, pp 1550-1562, 2011.
[20] Abdelhamid Benhocine, Lamri Laouamer, Laurent Nana, and Anca Christine Pascu, "New Images Watermarking Scheme Based on Singular Value Decomposition", Journal of Information Hiding and Multimedia Signal Processing, Vol. 4, No. 1, pp. 9-18, January 2013.
[21] Chih-Chin Lai, ― A digital watermarking scheme based on singular value decomposition and tiny genetic algorithm‖, Digital signal processing journal, vol 21, pp 522-527, 2011.
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[22] Khaled Loukhaoukha, "On The Security of Digital Watermarking Scheme Based on Singular Value Decomposition and Tiny Genetic Algorithm", Journal of Information Hiding and Multimedia Signal Processing, Vol. 3, No. 2, pp. 135-141, April 2012.
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[23] Leida Li, Shushuan Li, Ajith Abraham and Jeng-Shyang Pan, "Geometrically invariant image
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S0379-0738(13)00413-1 http://dx.doi.org/doi:10.1016/j.forsciint.2013.09.005 FSI 7347
To appear in:
FSI
Received date: Revised date: Accepted date:
27-11-2012 21-8-2013 3-9-2013
Please cite this article as: M.E. Moghaddam, N. Nemati, A Robust Color Image Watermarking Technique Using Modified Imperialist Competitive Algorithm, Forensic Science International (2013), http://dx.doi.org/10.1016/j.forsciint.2013.09.005 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Title Page (with authors and addresses)
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A Robust Color Image Watermarking Technique Using Modified Imperialist Competitive Algorithm
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Mohsen Ebrahimi Moghaddam*, Nasibeh Nemati Electrical and Computer Engineering Department, Shahid Beheshti University G.C, Tehran, Iran
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*corresponding Author : [email protected] Tel: +989121405308 , Address: Electrical and Computer Engineering Department, Shahid Beheshti University, Evin Ave, Tehran , Iran
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*Manuscript (without author identifiers)
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A Robust Color Image Watermarking Technique Using Modified Imperialist Competitive Algorithm
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Abstract— In this paper, a novel robust watermarking technique using Imperialistic Competition Algorithm (ICA) in the spatial domain is proposed to protect the intellectual
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property rights of color images. The proposed method tries to insert the watermark in the blocks which are selected by Modified ICA. In this method, ICA has been customized for
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watermarking. The color band for watermark insertion is selected based on color dynamic range in each block. Besides, in the procedure of selecting blocks for watermark insertion and
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extraction, ensuring higher fidelity and robustness and resilience to several possible image attacks have been considered. The experimental results showed that the proposed method
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performance created watermarked images with better PSNRs and more robustness versus
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several attacks such as additive noise, blurring, etc in compare to related works.
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Index Terms—Color image, Robust watermarking, Imperialistic Competition Algorithm (ICA).
1.
Introduction
Digital media security is a principal concern in the today technological world. Therefore, illegal distribution, duplication, and modification of the media posed as a serious problem on protecting intellectual property. Protection of digital multimedia content has been furthermore become an increasingly important issue for content owners and service providers. On the contrary, digital watermarking is one of the growing and promising technologies to protect the digital data from being tampered and also widely used for copyright protection.
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2 Watermarking is the process of embedding a watermark into the digital media to protect the intellectual property rights. The watermarking process can be performed by visible or invisible embedding [1][19]. Visible watermarking is the process of embedding the watermark into some visible parts of the digital media while invisible watermarking embeds the watermark confidentially
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into the parts of the digital media known only by the owner. In the other hand, invisible watermarking process categorized as fragile, semi fragile and robust watermarking. Fragile watermarking is used to
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determine the slight modifications on the digital media, while semi-fragile watermarks commonly are
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used to detect malignant transformations. Robust watermarking is used to determine the owner of the digital media even if it is modified.
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Digital watermarking is classified according to the embedding domain into two classes namely spatial and transform domain [2]. In spatial domain embedding is done by directly manipulating the
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pixel values of the host media. Techniques in this category are less complex but they show poor robustness against common image processing attacks. Another category is transform domain
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techniques [3]. Some techniques in this group transform the original media to frequency domain by using some transformations such as discrete Fourier transformation, discrete Cosine transformation and discrete Wavelet transformation [4]. Then, the watermark is embedded by modifying coefficients
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and watermarked image is obtained by inverse transformation of the modified coefficient. Some other methods in this group may apply some transforms such as SVD (Singular Value Decomposition) [20], calculating moments [23] or other processing techniques to insert the watermark.
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Moreover, the extraction of the watermark [3] can be classified into the following categories: Firstly, non-blind scheme which requires the original image for the watermark extraction; is robust against various image processing attacks. Secondly, semi blind scheme which does not require the original image for the process of extraction, but it needs the watermark and additional information in this phase. And lastly the blind scheme, that does not require the original image, watermark and additional information but only secret key for the watermark extraction. The watermarking approach may be applied on gray or color images. However, there are a lot of papers in gray image watermarking in literature but it is possible to find some watermarking
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3 approaches which have been developed to embed a watermark into a color image. For example, an algorithm for still color image based on double scrambling technique and the wavelet transform has been proposed by S. Hongqin and L.V Fangliang [4]. In this method, the original color image is transformed from RGB to the YUV color space and scrambled by Fibonacci transformation. Tsui et.
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al. Method [5] encodes the chromatic content of a color image as CIE a* b* chromaticity coordinates, and color watermarks (yellow and blue) are embedded in the frequency domain of the chromatic
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channels by using the Spatial Chromatic Discrete Fourier Transform (SCDFT). S. Das et.al proposed
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a method that inserts the watermark in the LSB of ‗‘Blue‖ band of each pixel in individual tiles of host image in RGB color space. A robust blind method has been proposed in [20 ]. This method
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inserts the watermark by linear interpolation in all SVD components matrixes (U, S, V) of Red channel. There is another robust SVD based method for gray images in the literature [21] which
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modifies the singular value of cover image by multiple scale factor. However, the authors in [22] tried to show that proposed method in [ ] is not robust and has many false watermark detection.
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A semi-fragile watermarking approach based on fractal compression and differentials record theory which was restorable and robust against rotation was proposed by AnHu et.al [6]. In this method,
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three components (R, G, B) of a color image are processed respectively by fractal compression. A color image watermarking system of color images based on fast Quaternion Fourier Transform (QFT) has been proposed by Wei-jiang Wang et.al [7]. Quaternion is a type of hyper-complex number. Sanjay Rawat and Balasubramanian Raman worked on YCbCr color space [4]. Their proposed
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method was based on discrete Cosine transform, wavelet packet transform and singular value decomposition. Qian-Chuan Zhong et.al proposed using the chaotic maps in the YCbCr color space [8]. They used Lorenz map and the Arnold cat map to encrypt the watermark signal and find embedding position of the host image. A watermarking algorithm for color image based on HSI color space and Discrete Wavelet Transform (DWT) has been addressed in [9]. In this method, sub-blocks of intensity are divided and sorted according to the human visual system (HVS) characteristic. Then, binary watermark is embedded by modifying the DWT coefficients of low frequencies in the selected sub-blocks. A spatial
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4 blind, semi-fragile method has been proposed based on totalistic cellular automata in [10]. Sujoy Roy and Ee-Clzien Chang [11] presented a method for watermarking color histograms that uses Earth Mover Distance (EMD) to modify an image to a target histogram. Recently some methods have been proposed for color image watermarking based on artificial
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intelligence techniques which try to predict the relationship between the values of the pixels within the embedding watermark block and the watermark itself. For example, Pao-Ta Yu et. al. proposed a
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watermarking technique based on neural networks for color images [12]. This method cooperates
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neural networks to memorize the relations between a watermark and the corresponding watermarked image. Due to neural networks possessing, the learning and adaptive capabilities, the trained neural
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networks recover the watermark from the watermarked image against image processing attacks. A SVM-based Color Image Watermarking (SCIW) has proposed by Tsai et. al. [13] which employs a
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classification technique based on SVMs to extract a watermark from color images. Three image features, which are different among local image statistic and the luminance value of the centre pixel in three sliding windows with distinct shapes, were used to construct the input vectors of the training
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patterns. Following the results produced by the classifier (the trained SVM), the SCIW method retrieves the hidden signature without the original image during watermark extraction. Oğuz Fındık et.
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al. suggested a watermarking technique that uses Artificial Immune Recognition System(AIRS) to protect color image intellectual property rights [14]. The watermark is embedded in the blue channel of a color image. m-bit binary sequence embedded into the color image is used to train AIRS.
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Extracting the watermark is carried out using AIRS. Another watermarking technique has been introduced by previous writers [15] based on Particle Swarm Optimization (PSO) and k-nearest neighbour algorithm. In the embedding process, the color image is separated into non-overlapping blocks and each bit of the binary watermark is embedded into an individual block. By using the symmetric cross-shape, features are extracted from the blocks in which the watermark is embedded and the control blocks which would be used as training data in the extraction algorithm. Two centroids belonging to each of the watermark binary values are obtained by PSO utilizing the features obtained from the control blocks.
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5 A watermarking technique which uses genetic algorithm to identify locations within the cover image for watermark insertion is designed by [16] in spatial domain. Genetic search often produces same watermark locations in different populations for watermark insertion resulting in poor value of fidelity and robustness which need to be checked. Sliding window concept uses a set of
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a few genes which are serially shuffled to get new set of locations for watermarking during each population generation.
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However, several watermarking techniques in spatial and frequency domain have been
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proposed by various researchers in color image watermarking but most of them suffers from problems of poor robustness and fidelity.
blind watermarking technique based on Imperialist Competitive
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In this paper, a new robust
Algorithm (ICA) for digital color images has been proposed. First of all, the proposed method
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identifies the locations within the host image for watermark insertion in spatial domain by using modified ICA. The modified ICA has an extra step in compare to ICA which makes it more proper for
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watermarking. Second, a neighbourhood of 5*5 pixels is chosen surrounding the selected locations and least significant color in neighbourhood of each pixel is chosen for embedding. Selecting best
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positions using ICA and using least significant color channel in each neighbourhood are somewhat novel in this paper that increase the quality of watermarked image. Furthermore, another concept have been used in insertion and extraction for ensuring higher fidelity and robustness and resilience to several possible image attacks. The proposed method was applied on the several bench-mark images
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and the experimental results showed method performance in resulted image quality together with its robustness versus different attack by check board software. The rest of this paper is organized as follows. In Section 2, Imperialist Competitive Algorithm is described. Section 3 introduces proposed algorithm for optimization of Robustness and Fidelity using Imperialist Competitive Algorithm. Section 4 shows experimental results and Conclusion is given in section5.
2. Imperialist Competitive Algorithm
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6 ICA is an algorithm for optimization inspired by the imperialistic competition [17]. The goal of optimization is to find an optimal solution in terms of the variables of the problem. This algorithm forms an array of variable values to be optimized. In GA terminology, this array is called ―chromosome‖, but here the term ―country‖ is used for this array. Like other evolutionary ones, this
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algorithm starts with an initial population of size Npop. Population individuals called country are in two types: colonies and imperialists that all together form some empires. Algorithm selects Nimp of the
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most powerful countries to form the empires. The remaining Ncol of the population will be the colonies
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each of which belongs to an empire. Imperialistic competition among these empires forms the basis of the proposed evolutionary algorithm. Imperialistic competition is the main part of proposed algorithm
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and causes the colonies to converge to the global minimum of the cost function. During this competition, weak empires collapse and powerful ones take possession of their colonies. Imperialistic
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competition converges to a state in which there exists only one empire. The Fig. 1 describes a typical imperialist competitive algorithm in its simplified form.
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1. Generating Initial Empires
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The core components of the ICA are as follows:
2. Moving the Colonies of an Empire toward the Imperialist 3. Exchanging Positions of the Imperialist and a Colony 4. Total Power of an Empire
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5. Imperialistic Competition
6. Eliminating the Powerless Empires 7. Convergence
3.
Proposed watermarking algorithm
The proposed method is based on ICA to find the best place for watermark insertion in least significant color band. The overall structure of proposed algorithm is shown in Fig. 2. In next subsections, the steps of proposed method are described in more details.
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7 3.1
Watermark embedding
Step 1: Let the host image (h
) used to embed the watermark as:
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(1)
(2)
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and the watermark image (wimage) is as follows:
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Step 2:
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This produces a row vector.
entries as follows:
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Host image is reshaped into row vector containing
(3)
(4)
The size of the host image is given as
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Step 3:
The watermark image is converted into a single dimension vector as follows: (5)
The total number of watermark locations is given by Step 4: In order to find optimal locations for insertion, apply MICA.
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8
Start
Host image
Watermark image
Initialize the impires
Convert host and watermark images into vectors
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Move the colonies toward their relevant imperialist
Is there a colony in an empire which has lower cost than that of imerialist
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apply MICA to find best insertion location
Yes
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No
Exchange the posotion of that imperialist and the colony
Find the color that has the least quantity in each selected location neighborhood
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No
Is there an empire with no colonies
No
Optimal regions for embedding
No
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Yes
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Pick the weakest colony from the weakest empire and give it to the empire that hast the most likehood to possess it
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Compute the total cost of an empire
Termination
Eliminate this empire
Stop condition satisfied Yes
Yes
Watermarked image
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Done
Fig.
1:
Flowchart
of
typical
imperialist competitive algorithm.
Fig. 2: Steps of the proposed watermark embedding algorithm
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9 Step 4-1: The arrays
and
which contain best locations for insertion and
total insertion cost in these locations are initialized to
. (6)
cr
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(7)
Step 4-2 Generating Initial Empires:
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Step 4-2-1:
. A country is a
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To start the optimization algorithm the initial population size is considered as
(8)
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array. This array is defined by:
The variable values in the country show the position of corresponding index in the host image. ( values are randomly assigned).
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So Step 4-2-2:
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For the purpose of watermark insertion, a
mask is considered as the neighbourhood of the
selected location. In each neighbourhood, via Eucledian distance the least significant color is found by
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following equations:
(9)
(10)
(11)
(12)
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10 Step 4-2-3: Afterwards, another
mask is considered as the neighbourhood of the selected location. This
process takes place on the color channels according to results of step 4-2-2 for each selected pixel. The pixel intensity value of the selected pixel is modified by
,
In this equation,
is embedding strength and neighbours.
shows watermark bit while
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is the average of
(13)
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according to average neighbourhood pixel intensity as follows:
Step 4-2-4:
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The cost of a country is found by evaluating the cost function at the variables
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so:
(14)
The similarity between the modified host image and the original host image is termed as cost
When
(15) (16)
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fitness function calculated as following equation:
is given by:
(17) Step 4-2-5: Now max cost and best individual positions are derived when:
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11 (18)
Step 4-2-6: We select Nimp of the most powerful countries to form the empires. The remaining Ncol of the
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population will be the colonies each of which belongs to an empire. Then two types of countries
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exists; imperialist and colony. Step 4-2-7:
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To form the initial empires, the colonies are divided among imperialists based on their power. That is the initial number of colonies of an empire should be directly proportionate to its power. To divide
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the colonies among imperialists proportionally, the normalized cost of an imperialist is defined as:
is the cost of nth imperialist and
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Where
(19)
is its normalized cost. Having the normalized cost of
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all imperialists, the normalized power of each imperialist is defined by:
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(20)
From another point of view, the normalized power of an imperialist is the portion of colonies that should be possessed by that imperialist. Then the initial number of colonies of an empire is:
is the initial number of colonies of nth empire and Ncol is the number of all colonies.
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Where
(21)
To divide the colonies, for each imperialist, choose
of the colonies randomly and give them to
it. These colonies together with the imperialist form nth empire. Step 4-3 Moving the Colonies of an Empire toward the Imperialist: At this level imperialist countries start to improve their colonies. This fact has modelled by moving all the colonies toward the imperialist. In this movement, colonies moves toward the imperialist by units. The direction of the movement is a vector from colony to imperialist. with uniform (or any proper) distribution. Then for
is a random variable
we have:
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12 (22) Where U means uniform distribution, colony and imperialist.
is a number greater than
is the distance between
causes the colonies to get closer to the imperialist state from both
sides.
values of
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Where
is a random number with uniform (or any proper) distribution. Then:
(23)
is a parameter that adjusts the deviation from the original direction. Nevertheless the are arbitrary.
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Step 4-4 Exchanging Positions of the Imperialist and a Colony:
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movement.
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To search different points around the imperialist add a random amount of deviation to the direction of
While moving toward the imperialist, a colony may reach to a position with higher cost than that of
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imperialist. In such a case, the imperialist moves to the position of that colony and vise versa. Then
position. Step 4-5 Find Repetition:
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algorithm continues by the imperialist in a new position and then colonies start moving toward this
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During watermarking with ICA, there is always a possibility of getting the similar locations for watermark insertion. So we add a new step to this algorithm. In this step, to prevent repetition, elements are examined and their repetitive values are replaced with a new random value. To avoid
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sensible variation, this new value is within the previous value scope. The repeated use of this technique ensures different watermark locations to be selected and thus avoiding the limitation and probability of finding same locations in the search space. This causes enhanced robustness and fidelity as indicated by various experiments. Step 4-6 Total Power of an Empire: Total power of an empire is mainly affected by the power of imperialist country and the colonies of an empire. This fact has modeled by defining the total cost by (24)
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13 Where
is the total cost of the nth empire and
less than 1. A little value for
is a positive number which is considered to be
causes the total power of the empire to be determined by just the
imperialist and increasing it will increase the role of the colonies in determining the total power of an empire. We have used the value of 0.1 for
in our method.
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Step 4-7 Imperialistic Competition:
To start the competition, first, find the possession probability of each empire based on its total
(25)
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Where
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power. The normalized total cost is obtained by:
are total cost and normalized total cost of nth empire respectively.
and
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Having the normalized total cost, the possession probability of each empire is given by:
To divide colonies among empires, we form the vector
(26)
based on possession probability of
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mentioned colonies as: (27)
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Then we create a vector with the same size as whose elements are uniformly distributed
(28)
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random numbers.
Then we form vector
by simply subtracting
from .
(29)
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14 Referring to vector
we will hand the mentioned colonies to an empire whose relevant index in
is maximum. Step 4-8 Eliminating the Powerless Empires: Powerless empires will collapse in the imperialistic competition and their colonies are assigned to
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other empires.
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Step 4-9 Convergence:
After a while all the empires except the most powerful one will collapse and all the colonies will be
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under the control of this unique empire. Step 4-10 one unit.
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Increase
Repeat steps 4-3 to 4-9 until
.
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Now the best locations in this iteration and the total cost are obtained Step 5:
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MakeKey function creates the key according to location of each watermark pixel in
. Also,
in each key index, the color channel which insertion taken place on that is saved.
Step 1:
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3.2. Watermark extracting
For extracting watermark by key which was made in MakeKey function we can simply extract
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inserted watermark. In the first place, reshape the watermarked image(
) into row vector:
(30)
This produces a row vector: (31) Step 2: pursuing this further for each index of key, find the related position in the watermarked image:
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15
(32)
Step 3: then calculate the average pixel intensity of selected location
pixels surrounding the concerned pixel.
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channel in key, by using a neighborhood of
in mentioned color
finally
compare
the
Pixel
intensity
value
of
selected
location
specified
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(33)
Experimental Results
ed
4.
by
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, with selected location:
the
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Step 4:
In this study, the peak signal to noise ratio (PSNR) is used to qualify the watermarked image and
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the extracted watermark quality respectively. To measure the method robustness, the Mean Absolute Error (MAE) has been employed which measures the difference between an original signature
and
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the corresponding extracted signature . The PSNR and MAE are calculated as follows:
(34)
(35)
(36) Where MSE stands for Mean Square Error. A lower MAE reveals that the extracted signature resembles the original signature
more closely. The robustness of a watermarking method is assessed
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16 by comparing with , where is extracted from the watermarked image
which is further degraded
by attacks. If a method has a lower MAE, it is more robust. To test the performance of proposed method, various bench-mark color images such as Lena, Baboon, and F16 with size
and watermarks with different sizes, were employed. Also, to imag which was used in
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make the proposed method comparable with related ones a binary
the evaluation of related methods was employed. In the embedding process, the embedding strength and the dimension of the neighbors for insertion and extraction was a 3
cr
was used as
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kernel.
TABLE 1 represents the various parameters setting used in the experiments for MICA. In most for
and about
for
have resulted
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experimental results of ICA usage, a value of about
in good convergence of countries to the global maxmum, so we use the same.
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The watermarking performance of the proposed method is compared with some related methods such as Kutter, Yu, and SCIW in TABLE 2. To investigate the robustness of these methods, several
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attacks were simulated using check-board software to degrade the watermarked. Besides the quantitative results in terms of the PSNR and the MAE, the experiment also provides visual
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comparison results (in Figure. 3).
By considering the results we could see that not only the watermarked images by the proposed algorithm have good PSNR, we also could observe high quality extracted watermarks. Conclusion
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5.
In this paper, a new neighbourhood concept method is used for finding a suitable location in spatial domain via modified imperialistic competition algorithm (ICA) for watermark insertion. Using ICA helps us to find a more suitable location for inserting watermarks bits in different color channels. At the same time, because of using neighbourhood concept during watermark insertion and extraction, the proposed watermarking method is robust against various attacks unless jpeg compression. Moreover, this study is a novel one that uses different color channel for each neighbourhood and ICA for watermark embedding process in the watermarking concept according to
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17 the literature review. Besides, the proposed method could be explored in the frequency domain as a prospective work.
TABLE I: Proposed method Parameters Values 25
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5
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Parameters
200
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0.1 2
5*5 3*3 25
Baboon
Extracted watermark from Baboon
F16
Extracted watermark from F16
MAE=0.0029
PSNR=43.0466
MAE=0.0044
PSNR=44.6549
MAE=0.0061
PSNR=28.0783
MAE=0.0273
PSNR=28.3123
MAE=0.0234
PSNR=30.0847
MAE=0.0180
PSNR=24.5114
MAE=0.0446
PSNR=25.4611
MAE=0.0366
PSNR=24.5388
MAE=0.0378
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PSNR=45.6295
Noise 5%
Extracted watermark from Lena
ce pt
Lena
No-attack
ed
M
an
0.1
Noise 10%
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18
Blurring (0.1)
MAE=0.0131
PSNR=29.2967
MAE=0.0148
PSNR=35.2000
MAE=0.0617
PSNR=25.9538
MAE=0.0705
PSNR=24.0699
MAE=0.0119
PSNR=16.3320
PSNR=22.6527
MAE=0.0119
PSNR=15.3903
PSNR=38.0500
MAE=0.0166
ip t
PSNR=38.4825
cr
Blurring twice
MAE=0.0178
us
PSNR=34.7079
an
Sharpening
PSNR=32.1533
MAE=0.0166
MAE=0.0043
PSNR=24.2855
MAE=0.0065
ed
M
Sharpening twice
MAE=0.0085
Fig. 3: Watermarked images Lena, Baboon and F16 and extracted waterm arks after the image processing attacks.
Attack number
1
Attacks
No-attack
Test image
Baboon F16 Lena Baboon F16 Lena Baboon F16 Lena Baboon F16 Lena Baboon F16 Lena Baboon F16
Ac
0
ce pt
TABLE II: Comparison of proposed method with some related ones
Noise 5%
2
Noise 10%
3
Blurring (0.1)
4
Blurring twice
5
Sharpening
PSNR Proposed SCIW method 42.7600 43.8422 39.6300 44.6549 41.8300 45.6295 21.1300 30.0441 18.6900 30.0847 21.2600 30.2402 18.3100 24.3124 15.6500 24.5388 18.1200 24.5114 25.5100 29.2967 30.0500 38.6626 24.4900 38.4825 25.2100 25.9538 29.2000 34.7079 28.6800 35.2000 19.5700 23.4127 25.0700 32.1533
Kutter’s [18] 0.1745 0.0830 0.0754 0.1679 0.0563 0.2119 0.1936 0.1911 0.1240 0.2312 0.1372 0.0871 0.2731 0.3020 0.1176 0.1030 0.0158
MAE Yu’s Tsai [12] [13] 0.1333 0.0964 0.0148 0.0065 0.0312 0.0219 0.1391 0.1218 0.0249 0.0131 0.0595 0.0449 0.1870 0.1616 0.0458 0.0363 0.1201 0.1125 0.1867 0.1386 0.0290 0.01391 0.0495 0.0307 0.2280 0.1679 0.0493 0.0197 0.0708 0.0354 0.1071 0.0854 0.0102 0.0041
Proposed method 0.0044 0.0061 0.0029 0.0031 0.0068 0.0046 0.0598 0.0192 0.0192 0.0627 0.0305 0.0227 0.1245 0.0839 0.0659 0.0029 0.0061
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19
6
Sharpening twice
Lena Baboon F16 Lena
25.4600 13.5700 15.7600 15.1300
5.
31.7732 15.3903 24.2855 22.6527
0.0703 0.0817 0.0039 0.0195
0.0241 0.0959 0.0139 0.0222
0.0170 0.0793 0.0036 0.0175
0.0043 0.0043 0.0065 0.0119
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