# Image Watermarking Algorithm Based on Multiobjective Ant Colony Optimization and Singular Value Decomposition in Wavelet Domain

Authors: Khaled Loukhaoukha
Journal: Journal of Optimization
 (2013)

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## Abstract

We present a new optimal watermarking scheme based on discrete wavelet transform (DWT) and singular value decomposition (SVD) using multiobjective ant colony optimization (MOACO). A binary watermark is decomposed using a singular value decomposition. Then, the singular values are embedded in a detailed subband of host image. The trade-off between watermark transparency and robustness is controlled by multiple scaling factors (MSFs) instead of a single scaling factor (SSF). Determining the optimal values of the multiple scaling factors (MSFs) is a difficult problem. However, a multiobjective ant colony optimization is used to determine these values. Experimental results show much improved performances of the proposed scheme in terms of transparency and robustness compared to other watermarking schemes. Furthermore, it does not suffer from the problem of high probability of false positive detection of the watermarks.


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## Body

## 1. Introduction

With the advent of numeric era at the end of 20th century, the exchange of digital documents became a very easy task. This extraordinary technical revolution from analog to numerical technology was not achieved without generating anxiety in terms of the protection of the authors rights since multimedia documents can be quite easily duplicated, modified, and illegally attacked without deterioration. Affected by significant revenue losses multimedia documents author’s are motivated more than ever to secure their documents. In this context digital watermarking was introduced: it consists of inscribing invisible (or visible) data into the multimedia documents. This is done in two stages: embedding and extracting process. Digital watermarking schemes for images can be classified into different classes according to embedding domain, embedding rule, imperceptibility, and permanency.In terms of robustness, the watermarking algorithm can be classified into three categories: fragile, semifragile, and robust. Fragile watermarking is designed to detect any modification in such a way that slight modifications or tampering on the watermarked image will destroy the watermark. This type is employed to ensure the integrity and image authenticity. Conversely, robust watermarking is designed to be resistant against attacks that attempt to remove or destroy the watermark without degrading the visual quality of the watermarked image significantly. Robust watermarking is typically employed for copyright protection and ownership verification. Semifragile watermarking combines the properties of fragile and robust watermarking in order to detect unauthorized manipulations while still being robust against authorized manipulations.Generally, watermarking algorithms operate either in the spatial domain or in a transform domain such as discrete wavelet transform (DWT) [1–3], discrete Fourier transform (DFT) [4–6], and discrete cosine transform (DCT) [7–9]. Spatial domain watermarking has the advantage of low calculation complexity compared to that in transform domains. However, most watermarking schemes in the scientific literature operate in the transform domain because it provides enhanced imperceptibility and robustness compared to those in spatial domain. However, decomposition of images in a standard basis set using the transform domain does not necessarily leads to the optimal representation of an image. Therefore, different representations were investigated for watermarking: these include nonnegative matrix factorization (NMF) [10–12] and singular value decomposition (SVD) [13–15]. Watermarking schemes based on SVD are advantageous since slight changes in the singular values do not affect significantly the image quality. Unfortunately, several SVD-based watermarking schemes suffer from typically high probability of false positive watermark detection [16–18].According to embedding rule, watermarking algorithm can be classified into three categories: multiplicative, additive, or substitution. In multiplicative and additive embedding schemes, the trade-off between imperceptibility and robustness is controlled by a single scaling factor (SSF). Cox et al. [19] suggest to use multiple scaling factors (MSFs) instead of one. They state that single scaling factor may not be applicable for altering all the pixel values of the original image. Determining the optimal values of the multiple scaling factors can be viewed as optimization problem which is unfortunately a difficult problem. In this paper, a multiobjective ant colony optimization (MOACO) is used to solve this difficult problem and to determine these optimal values.The rest of this paper is organized as follows. Section2 reviews the basic concepts of the ant colony optimization (ACO). In Section 3, we presented the proposed watermarking algorithm based on discrete wavelet transform (DWT) and singular value decomposition (SVD). The proposed watermarking algorithm using multiobjective ant colony optimization is described in Section 4. The experimental results are provided in Section 5. Finally, the concluding remarks are given in Section 6.


## 2. Ant Colony Optimization Principle

The ant colony optimization (ACO) was introduced by Dorigo [20] as solution for hard optimization problems. It is inspired by observation of real ant colonies. Ants explore randomly the area surrounding their nest in order to find food. If an ant finds a food source, it evaluates and carries some food to the nest. During the return travels the ant deposits on the ground a chemical substance called pheromone trail. Other ants can smell the pheromone and follow it with some probability. This way, ants can communicate via pheromone and find the optimal path between the food source and the nest. This capability of real ant colony to find optimal paths has led to the definition of artificial ant colonies that can find the optimal solution for hard optimization problems [21] such as the traveling salesman problem. The outline of the generic ACO algorithm is presented in Figure 1.Figure 1
Generic ant colony optimization scheme [22].The first point to take into account in ant colony optimization is how the colony is represented. For continuous variables, a colony ofm ants is represented as m×n matrix C, where C=[x1x2⋯xm]T such that x=[x1x2⋯xn] is a vector of n design variables that corresponds to a single ant. The second point to consider is how to model the pheromone communication scheme. Socha and Dorigo [21] suggest to use a normal distribution for a continuous model implementation:
(1)fpheromone(x)=e(-(x-xmin)2/2σ2),
where xmin is the optimal point found within the design space and the standard deviation σ as an index of the ants aggregation around the current minimum. To initialize the algorithm, xmin is randomly chosen in the design space, using a uniform distribution, and σ is taken at least three times greater than the length of the design space, to uniformly locate the ants within it. As shown in Figure 1, at each iteration, the ACO algorithm updates the values of each design variable, and this is for all the ants of the colony; that is, at each iteration each ant sets the values for the trial solution as per the distribution in (1). At the end, the pheromone distribution over the design space is updated by collecting the information acquired throughout the optimization steps. Since the pheromone is modeled by (1), it is necessary only to update xmin and σ as
(2)σ=std(colony),
where std(colony) makes use of the colony of ants (candidate solution) to return a vector containing the standard deviation for each design variable [23]. The accumulation of pheromone increases in the vicinity of the candidate towards the optimum solution. However, to avoid premature convergence, negative update procedures are not discarded: for this, a simple method is used, which consists in dissolving the pheromone. The principle is to spread the amount of pheromone by changing the current standard deviation (for each variable) according to
(3)σnew=γ·σold,
where γ>1 is the dissolving rate.


## 3. Watermarking Algorithm Based on SVD and DWT

The SVD-DWT watermarking algorithm presented in [14] is described by the following embedding and extraction processes.


### 3.1. Embedding Process

Consider an original imageI of size N×N, and let the watermark W be a binary image of size M×M. The embedding process is as follows. (1)
Decompose the original imageI into 3ℓ+1 subbands by applying a ℓ-level discrete wavelet transform (DWT).(2)
Select one sub-band (SB) among the three following sub-bands: HHℓ, HLℓ, and LHℓ.(3)
Compute the inverse DWT of the selected sub-band (SB):
(4)X=DWT-1(SB).(4)
Apply a singular value decomposition (SVD) of matrixX:
(5)X=UX·SX·VXT.(5)
Encrypt watermark imageW using image encryption algorithm to get encrypted watermark denoted by WC.(6)
Apply a singular value decomposition to watermark matrixWC.
(6)W=UWC·SWC·VWCT.(7)
Compute the one-way hash functions for matricesUWC and VWC:
(7)HU=hash(UWC),HV=hash(VWC).(8)
MatricesUWC and VWC and their hash values HU and HV are stored in the private key. (Steps (5) and (7) are necessary to mitigate the problem of false positive detection of watermark. This solution was proposed in [24].)(9)
Compute matrixSY according to
(8)SY=SX+α·SWC,
where α is the watermark strength factor that controls the trade-off between imperceptibility and robustness of the watermarking scheme. This parameter can be used as single scaling factor (SSF) or multiple scaling factors (MSFs).(10)
Compute matrixZW, according to
(9)ZW=UX·SY·VXT.(11)
Compute the discrete wavelet transform of matrixZW,
(10)SBW=DWT(ZW).(12)
The watermarked imageIW is computed by applying the inverse ℓ-level discrete wavelet transform to the modified sub-band SBW and the 3ℓ unmodified sub-bands.


### 3.2. Extracting Process

The extracting process is summarized by the following steps.(1)
A safety test is first done: hash values of matricesUWC and VWC, possibly altered by an attacker as U~W and V~W, are computed to give HU~ and HV~. These hash values are compared with these stored during the embedding process:
(11){ifHU=HU~,andHV=HV~⟶goto2,ifHU≠HU~orHV≠HV~⟶stop(probableattack).(2)
Decompose the original and watermarked images,I and IW, by applying the ℓ-level discrete wavelet transform.(3)
Select the same sub-band (SB) used in 2 of the watermark embedding process. SBI and SBIW are, respectively, the subbands selected for the original and watermarked images.(4)
Compute the inverse discrete wavelet transform of selected subbandsSBI and SBIW:
(12)XI=DWT-1(SBI),XIW=DWT-1(SBIW).(5)
Apply the singular value decomposition on matricesXI and XIW:
(13)XI=UXI·SXI·VXIT,XIW=UXIW·SXIW·VXIWT.(6)
Compute matrixSW^C as follows:
(14)SW^C=SXIW-SXIα.(7)
Determine the extracted encrypted watermark,W^C, by computing
(15)W^C=U~WC·SW^C·V~WCT.(8)
DecryptW^C to get the extracted watermark W^.


### 3.3. Protection against False Positive Detection

To protect the proposed image watermarking scheme against false positive detection, two countermeasures have been proposed in [24]. The first countermeasure consists of computing a one-way hash function for the matrices UW and VW. The hash function algorithm such as message digest 5 (MD5) or secure hash algorithm 1 (SHA-1) can be used for this purpose. During embedding process, the hash values of matrices UW and VW denoted, respectively, by HU and HV are stored in a private key. In the extracting process, the hash function is first computed from received (and possibly altered by an attacker) matrices UW~ and VW~, denoted by HU~ and HV~. Thus, if HU≠HU~ or HV≠HV~, the watermark extracting process is stopped because UW~≠UW or VW~≠VW: otherwise the watermark extracting process is performed. The authors indicate that the hash function test can be also be applied on a combined matrix from UW andVW such as UW+VW andUW×VW.The second countermeasure consists in encrypting the watermark before the embedding process. The watermarkW is encrypted resulting in an encrypted watermark denoted by WC which will then be embedded in the original image I. Suppose that for watermark extraction process, an attacker uses his own watermark W~. Then matrices UW~ and VW~ will be used instead of proper matrices UWC and VWC. The first extracted watermark will be the same as W~, but the decryption process must be performed since the embedded watermark is encrypted during the watermark embedding process. Therefore, the extracted watermark W^ will be a random-like image. Note that, if there is no attack, the first extracted watermark W^C will indeed be the encrypted image, but after decryption, the extracted watermark W^ will have a high correlation value with the original watermark W. Figures 2 and 3 illustrate an example of one-way hash function and encryption countermeasures, respectively, using Lena as original image I and LOGO as watermark W.Figure 2
Example of the first countermeasure based on one-way hash function.Figure 3
Example of the second countermeasure based on encryption.


## 3.1. Embedding Process

Consider an original imageI of size N×N, and let the watermark W be a binary image of size M×M. The embedding process is as follows. (1)
Decompose the original imageI into 3ℓ+1 subbands by applying a ℓ-level discrete wavelet transform (DWT).(2)
Select one sub-band (SB) among the three following sub-bands: HHℓ, HLℓ, and LHℓ.(3)
Compute the inverse DWT of the selected sub-band (SB):
(4)X=DWT-1(SB).(4)
Apply a singular value decomposition (SVD) of matrixX:
(5)X=UX·SX·VXT.(5)
Encrypt watermark imageW using image encryption algorithm to get encrypted watermark denoted by WC.(6)
Apply a singular value decomposition to watermark matrixWC.
(6)W=UWC·SWC·VWCT.(7)
Compute the one-way hash functions for matricesUWC and VWC:
(7)HU=hash(UWC),HV=hash(VWC).(8)
MatricesUWC and VWC and their hash values HU and HV are stored in the private key. (Steps (5) and (7) are necessary to mitigate the problem of false positive detection of watermark. This solution was proposed in [24].)(9)
Compute matrixSY according to
(8)SY=SX+α·SWC,
where α is the watermark strength factor that controls the trade-off between imperceptibility and robustness of the watermarking scheme. This parameter can be used as single scaling factor (SSF) or multiple scaling factors (MSFs).(10)
Compute matrixZW, according to
(9)ZW=UX·SY·VXT.(11)
Compute the discrete wavelet transform of matrixZW,
(10)SBW=DWT(ZW).(12)
The watermarked imageIW is computed by applying the inverse ℓ-level discrete wavelet transform to the modified sub-band SBW and the 3ℓ unmodified sub-bands.


## 3.2. Extracting Process

The extracting process is summarized by the following steps.(1)
A safety test is first done: hash values of matricesUWC and VWC, possibly altered by an attacker as U~W and V~W, are computed to give HU~ and HV~. These hash values are compared with these stored during the embedding process:
(11){ifHU=HU~,andHV=HV~⟶goto2,ifHU≠HU~orHV≠HV~⟶stop(probableattack).(2)
Decompose the original and watermarked images,I and IW, by applying the ℓ-level discrete wavelet transform.(3)
Select the same sub-band (SB) used in 2 of the watermark embedding process. SBI and SBIW are, respectively, the subbands selected for the original and watermarked images.(4)
Compute the inverse discrete wavelet transform of selected subbandsSBI and SBIW:
(12)XI=DWT-1(SBI),XIW=DWT-1(SBIW).(5)
Apply the singular value decomposition on matricesXI and XIW:
(13)XI=UXI·SXI·VXIT,XIW=UXIW·SXIW·VXIWT.(6)
Compute matrixSW^C as follows:
(14)SW^C=SXIW-SXIα.(7)
Determine the extracted encrypted watermark,W^C, by computing
(15)W^C=U~WC·SW^C·V~WCT.(8)
DecryptW^C to get the extracted watermark W^.


## 3.3. Protection against False Positive Detection

To protect the proposed image watermarking scheme against false positive detection, two countermeasures have been proposed in [24]. The first countermeasure consists of computing a one-way hash function for the matrices UW and VW. The hash function algorithm such as message digest 5 (MD5) or secure hash algorithm 1 (SHA-1) can be used for this purpose. During embedding process, the hash values of matrices UW and VW denoted, respectively, by HU and HV are stored in a private key. In the extracting process, the hash function is first computed from received (and possibly altered by an attacker) matrices UW~ and VW~, denoted by HU~ and HV~. Thus, if HU≠HU~ or HV≠HV~, the watermark extracting process is stopped because UW~≠UW or VW~≠VW: otherwise the watermark extracting process is performed. The authors indicate that the hash function test can be also be applied on a combined matrix from UW andVW such as UW+VW andUW×VW.The second countermeasure consists in encrypting the watermark before the embedding process. The watermarkW is encrypted resulting in an encrypted watermark denoted by WC which will then be embedded in the original image I. Suppose that for watermark extraction process, an attacker uses his own watermark W~. Then matrices UW~ and VW~ will be used instead of proper matrices UWC and VWC. The first extracted watermark will be the same as W~, but the decryption process must be performed since the embedded watermark is encrypted during the watermark embedding process. Therefore, the extracted watermark W^ will be a random-like image. Note that, if there is no attack, the first extracted watermark W^C will indeed be the encrypted image, but after decryption, the extracted watermark W^ will have a high correlation value with the original watermark W. Figures 2 and 3 illustrate an example of one-way hash function and encryption countermeasures, respectively, using Lena as original image I and LOGO as watermark W.Figure 2
Example of the first countermeasure based on one-way hash function.Figure 3
Example of the second countermeasure based on encryption.


## 4. Watermarking Algorithm Using Multiobjective Ant Colony Optimization

In general, watermarking schemes are either based on an additive or a multiplicative rule. The embedding rules themselves are usually of the following form, whereI and IW are, respectively, original and watermarked image (or their representation in other domains such as FFT, DCT, and DWT),α is used to control the trade-off between imperceptibility and robustness of an image and generally is used as scaling factor:
(16)IW=I+α·W⟶additiverule,IW=I·(1+α·W)⟶multiplicativerule.Cox et al. [19] suggest the use of multiple scaling factors instead of one. They state that a single scaling factor may not be applicable for altering all the values of original imageI. Determining the optimal values of these multiple scaling factors is a complex computational problem. To solve it, we propose to use a multi-objective ant colony optimization (MOACO).Figure4 illustrates block diagram of multiobjective optimization which is closed-loop control system. System input is multiple scaling factors and objective measure as system output; this measure is calculated from original image I, watermarked image IW, watermark W, and the (T+1) extracted watermarks (W^ and W^j, where j={1,2,…,T}) under attacks.Figure 4
: Block diagram of multiobjective optimization.The steps for applying MOACO into SVD-DWT watermarking scheme are enumerated below.(1)
Define the colony size, the initial pheromone trail, the dissolving rate (σ), the objective function, and a generation number as the algorithm stopping criterion.(2)
Using (17), generate randomly an initial population of ants, which constitute a set of potential solutions. Each ant is denoted by X={x1,x2,…,xn},
(17)fpheromone(xi)=e((xi-xi*)2/2σi2),
where xi* is theith coordinate of the best point found by optimization within the design space at the current iteration and σi is the ants aggregation index for the ith coordinate of the design space. At the first iteration, xi* is chosen according a uniform distribution with σi taken as at least 3 times larger than the length of the search interval.(3)
For each antX of the population, consider the following.
(i)
Produce the watermarking imageIW using embedding process (Section 3.1) using the ant X as the watermark strength factor. Note that (8) in watermark embedding process is transformed into. (Note that, α is changed into multiple scaling factors instead of single scaling factor and diag(α)is diagonal matrix created from the vector X, that is, the ant X)
(18)SY=SX+diag(α)·SWC.(ii)
Compute the normalized correlationNC(I,IW) between original and watermarked images.(iii)
Apply a watermark attack out of a set ofT selected attacks upon the watermarked image IW. This leads to T different attacked watermarked images {I^W} for each original watermarked image IW.(iv)
Extract the watermarksW^i from the attacked watermarked images I^W using the extraction process, as described in Section 3.2, where i={1,2,…,T}.(v)
Compute the normalized correlation between the original watermarkW and the set of extracted watermarks {W^i}, that is, {NC(W,W^i)}.(vi)
Construct the vector of objective values,F(X), defined as
(19)F(p)=(1NC(I,IWp)1NC(W,W^)1NC(W,W^p,1)mm×1NC(W,W^p,2)⋯1NC(W,W^p,T))T.(vii)
Evaluate the vector of objective values according to theexponential weighted method for multiobjective optimization [26]:
(20)Fobj(X)=∑i=1T+2‍(ep·w-1)·ep·(F(X)-F0),
where p, w, and F0 are positive constants. In experiments, we take p=2, w=5 et F0=10.(4)
Find the best antXbest={x1*,x2*,…,xn*} as the one having the smallest objective value Fobj.(5)
Update the pheromone trail distribution using the formula (17); in this step the aggregation index for theith dimension σi is given by
(21)σi=1m∑i=1m‍yi-y-,
where y is theith column of the colony matrix C,y- is the mean value of the vector y, and m is the number of colony size.(6)
Save the best antXbest between this generation and the old one generation.(7)
If the generation number is reached, the optimization process of the multiple scaling factors (MSFs) is terminated, else it goes to the next step.(8)
Using the distribution of (17), generate a new population of ants and then go back to 3.


## 5. Experimental Results

To show the effectiveness of the proposed watermarking algorithm using multiple scaling factors optimized by multiobjective ant colony optimization instead of single scaling factor in terms of imperceptibility and robustness, a number of tests have been carried out using four256×256 gray-scale test images and a 32×32 binary watermark, which are depicted in Figure 5. Note that ℓ is set to be equal to 3.Original images and binary watermark.
(a)
Cameraman
(b)
Goldhill
(c)
Lena
(d)
Man
(e)
Letter AWe conducted several experiments to compare the imperceptibility and robustness of the proposed watermarking algorithm using multiple scaling factors optimized by multiobjective ant colony optimization (i.e., MSF-MOACO) and single scaling factor (SSF) with Xianghong et al. [1], Liu [3], Pai and Ruan [25], and Ouhsain and Hamza [12] algorithms. Table 1 lists the PSNR values of proposed and other algorithms for the same test images. It is clearly shown that our algorithm using multiple scaling factors (MSF-MOACO) outperforms all other algorithms in terms of the visual quality of the reconstructed watermarked image except Pai and Ruan algorithm [25]. Furthermore, the normalized correlation between watermark W and extracted watermark W^ of the proposed algorithm (MSF-MOACO) is very close to unity.Table 1
Imperceptibility test results.
Image
Algorithm
PSNR
(
I
,
I
W
)(dB)
NC
(
W
,
W
^
)
MSF-MOACO
50.118
1.000
SSF
49.341
1.000
Cameraman
Xianghong et al. [1]
49.075
1.000
Liu and Li [3]
45.529
0.998
Pai and Ruan [25]
55.289
1.000
Ouhsain and Hamza [12]
53.563
1.000
MSF-MOACO
54.219
1.000
Using SSF
51.124
1
Goldhill
Xianghong et al. [1]
49.075
0.999
Liu and Li [3]
45.320
1.000
Pai and Ruan [25]
54.844
1.000
Ouhsain and Hamza [12]
51.892
1.000
MSF-MOACO
50.942
1.000
SSF
48.899
1
Lena
Xianghong et al. [1]
49.075
1.000
Liu and Li [3]
45.472
0.997
Pai and Ruan [25]
55.186
1.000
Ouhsain and Hamza [12]
53.192
1.000
MSF-MOACO
51.026
1.000
SSF
50.735
1.000
Man
Xianghong et al. [1]
49.075
1.000
Liu and Li [3]
45.323
1.000
Pai and Ruan [25]
55.186
0.998
Ouhsain and Hamza [12]
52.569
0.999For the robustness tests, ten different attacks were selected in conjunction to multiobjective optimization (i.e.,T=10). These attacks are salt and peppers noise (with a density of 0.05), Gaussian filtering (3×3), cropping (1/8 of the image center), JPEG compression (Q=5), sharpening, scaling (256→512→256), histogram equalization, gray-scale quantization (1 bit), re-watermarking using other watermarks that differ from the watermark (Letter A) and collusion attack using five watermarks, which are denoted by SP, GF, CR, CM, SH, SC, HE, QN, RW, and CA, respectively. The normalized correlation values between embedded and extracted watermarks under these attacks are depicted in Table 2, where the MSF-MOACO algorithm outperforms other algorithms [1, 3, 12, 25].Table 2
Correlation coefficient comparison results between MSF-MOACO and SSF algorithms and other algorithms.
Image
Algorithm
SP
GF
CR
CM
SH
SC
HE
QN
RW
CA
MSF-MOACO
0.956
0.972
0.947
0.902
0.989
1
0.985
0.990
1.000
1.000
SSF
0.726
0.907
0.894
0.729
0.976
1.000
0.959
0.958
0.998
0.997
Cameraman
Xianghong et al. [1]
0.495
0.847
0.983
0.621
0.580
0.993
0.621
0.750
0.850
0.914
Liu and Li [3]
0.936
0.872
0.983
0.470
0.940
0.846
0.954
0.743
0.865
0.907
Pai and Ruan [25]
0.407
0.898
0.975
0.537
0.697
0.989
0.474
0.504
0.867
0.924
Ouhsain and Hamza [12]
0.710
0.840
0.881
0.775
0.653
0.982
0.920
0.532
0.860
0.902
MSF-MOACO
0.935
0.957
0.935
0.959
0.984
1
0.975
0.972
0.998
1.000
SSF
0.885
0.936
0.998
0.930
0.950
1.000
0.977
0.965
0.995
0.999
Goldhill
Xianghong et al. [1]
0.694
0.858
0.983
0.633
0.713
0.986
0.440
0.570
0.850
0.914
Liu and Li [3]
0.947
0.906
0.980
0.545
0.951
0.870
0.974
0.714
0.867
0.918
Pai and Ruan [25]
0.374
0.827
0.976
0.293
0.370
0.926
0.313
0.220
0.867
0.912
Ouhsain and Hamza [12]
0.789
0.835
0.852
0.796
0.675
0.978
0.950
0.523
0.850
0.925
MSF-MOACO
0.933
0.974
0.918
0.948
0.976
0.999
0.974
0.967
1.000
1.000
SSF
0.774
0.943
0.867
0.944
0.982
1.000
0.988
0.974
0.998
0.995
Lena
Xianghong et al. [1]
0.616
0.866
0.983
0.640
0.667
0.994
0.587
0.625
0.850
0.914
Liu and Li [3]
0.926
0.845
0.953
0.541
0.952
0.826
0.987
0.716
0.863
0.912
Pai and Ruan [25]
0.383
0.920
0.980
0.442
0.681
0.994
0.514
0.393
0.867
0.918
Ouhsain and Hamza [12]
0.769
0.882
0.853
0.775
0.685
0.983
0.962
0.546
0.849
0.936
MSF-MOACO
0.932
0.952
0.964
0.978
0.966
1.000
0.972
0.970
1
1.000
SSF
0.588
0.845
0.930
0.905
0.949
0.999
0.977
0.963
0.990
0.998
Man
Xianghong et al. [1]
0.738
0.876
0.983
0.641
0.692
0.993
0.828
0.456
0.850
0.914
Liu and Li [3]
0.944
0.876
0.994
0.532
0.966
0.845
0.983
0.803
0.865
0.915
Pai and Ruan [25]
0.412
0.866
0.981
0.324
0.432
0.983
0.468
0.282
0.867
0.920
Ouhsain and Hamza [12]
0.745
0.812
0.890
0.756
0.632
0.985
0.945
0.556
0.875
0.952The effectiveness of the proposed algorithm (MSF-MOACO) is also tested against other attacks such as gamma correction (γ=0.2), dithering, rotation (25°), motion blur (45°), and translation (25 pixels × 25 pixels), which were denoted by GC, DI, RT, MB, and TR, respectively. Table 3 depicts the normalized correlation values under these attacks, where one can observe that MSF-MOACO algorithm is more robust against these attacks compared to other algorithms. Figure 6 shows an example of watermarked images under these attacks, while Figure 7 depicts their corresponding extracted watermarks.Table 3
Correlation coefficient comparison results between the MSF-MOACO algorithms and other algorithms.
Image
Algorithm
GC
DI
RT
MB
TR
MSF-MOACO
0.970
0.994
0.927
0.989
0.755
Xianghong et al. [1]
0.847
0.001
0.368
0.428
0.411
Cameraman
Liu and Li [3]
0.988
0.688
0.665
0.652
0.659
Pai and Ruan [25]
0.616
0.532
0.421
0.546
0.462
Ouhsain and Hamza [12]
0.940
0.463
0.201
0.980
0.524
MSF-MOACO
0.979
0.979
0.904
0.974
0.951
Xianghong et al. [1]
0.915
0.001
0.493
0.516
0.444
Goldhill
Liu and Li [3]
0.997
0.676
0.702
0.643
0.752
Pai and Ruan [25]
0.313
0.267
0.271
0.304
0.278
Ouhsain and Hamza [12]
0.955
0.423
0.322
0.975
0.563
MSF-MOACO
0.970
0.970
0.916
0.966
0.877
Xianghong et al. [1]
0.875
0.001
0.446
0.584
0.419
Lena
Liu and Li [3]
0.986
0.686
0.641
0.666
0.683
Pai and Ruan [25]
0.535
0.435
0.332
0.527
0.374
Ouhsain and Hamza [12]
0.953
0.452
0.356
0.970
0.550
MSF-MOACO
0.924
0.968
0.847
0.962
0.875
Xianghong et al. [1]
0.912
0.001
0.518
0.518
0.492
Man
Liu and Li [3]
0.990
0.683
0.689
0.636
0.766
Pai and Ruan [25]
0.374
0.316
0.287
0.334
0.292
Ouhsain and Hamza [12]
0.902
0.432
0.324
0.965
0.543Watermarked images under different attacks.
(a)
Salt and peppers noise
(b)
Gaussian filter
(c)
Cropping
(d)
JPEG compression
(e)
Sharpening
(f)
Scaling
(g)
Histogram equalization
(h)
Gray-scale quantization
(i)
Rewatermarked
(j)
Gamma correction
(k)
Dithering
(l)
Motion blurExtracted watermarks under different attacks.
(a)
Salt and peppers noise
(b)
Gaussian filter
(c)
Cropping
(d)
JPEG compression
(e)
Sharpening
(f)
Scaling
(g)
Histogram equalization
(h)
Gray-scale quantization
(i)
Rewatermarked
(j)
Gamma correction
(k)
Dithering
(l)
Motion blur


## 6. Conclusion

In this paper, we present a new watermarking algorithm based on lifting wavelet transform (LWT) and singular value decomposition (SVD) using multiple scaling factors (MSF) optimized by multiobjective ant colony optimization (MOACO). The MSFs are used instead of single scaling factor (SSF) to achieve a highest possible robustness without losing watermark transparency. However, determining the optimal set of multiple scaling factors is a prohibitively complex problem; to solve this problem a multiobjective ant colony optimization is used. Experimental results demonstrate that the proposed watermarking algorithm (MSF-MOACO) outperforms other watermarking algorithms in terms of imperceptibility and robustness. Moreover, MSF-MOACO algorithm showed better imperceptibility and excellent resiliency against a wide range of watermarking attacks such as additive noise, compression, filtering, and geometrical attacks. Furthermore, the problem of false positive detection which affects most SVD watermarking algorithms is resolved using countermeasures based on one-way hash function and watermark encryption proposed in [24].



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*Source: 921270-2013-06-06.xml*