Adaptive color image watermarking based on a modified improved pixel-wise masking technique

Computers and Electrical Engineering 35 (2009) 673–695

Contents lists available at ScienceDirect

Computers and Electrical Engineering journal homepage: www.elsevier.com/locate/compeleceng

Adaptive color image watermarking based on a modified improved pixel-wise masking technique Hazem A. Al-Otum *, Allam O. Al-Taba’a Jordan University of Science and Technology, Electrical Engineering Department, P.O. Box 3030, Irbid 22110, Jordan

a r t i c l e

i n f o

Article history: Received 2 July 2008 Received in revised form 18 January 2009 Accepted 21 January 2009 Available online 18 March 2009 Keywords: Color imaging Image watermarking Pixel-wise masking

a b s t r a c t In this paper, a family of modified wavelet-based watermarking techniques is proposed. This family of techniques is based on the improved pixel-wise (PW) watermarking scheme. The basic proposed algorithm considerably improves the PSNR of the watermarked image (in the range of 2.20–7.28 dB), and is based on selecting specific locations in the three detailed sub-bands of the first level of the DWT decomposition of the image. The selective nature of the modified PW method (denoted as selective PW: SPW) allows the scheme to be adaptive in terms of the imperceptibility and the watermark size. Also, the PW and SPW methods were extended to be implemented with color images: (1) grayscale-wise PW method (G-PW) that embeds the watermark in the Y component of the YCbCr model. (2) Multi spectral-PW method (MS-PW) that embeds the watermark in the R, G, and B layers independently. (3) Multi spectral-SPW method (MS-SPW) that gains high PSNR value compared with MS-SPW, and (4) multi spectral-maximum PW method (MS-MPW) which is proposed to improve the PSNR value as well as the level of watermarking security, when compared with MS-PW method. Ó 2009 Elsevier Ltd. All rights reserved.

1. Introduction Over the past few years, there has been a tremendous growth in computer networks. This is coupled with the increase of computer performance which facilitated the distribution of multimedia data such as text, digital images, audio, video, and software. Publishers, artists, and photographers, however, may be unwilling to distribute data over the Internet due to a lack of security. This presents a challenge to copyright protection of multimedia data as unauthorized copying and distributing of digital images, video, audio, etc. Recently, researchers have considered embedding invisible information into digital data. The hidden information is known as digital watermark. It is embedded into the original data in such a way that it remains present as long as the perceptible quality of the content is at an acceptable level. The owner of the original data proves her/his ownership by extracting the watermark from the watermarked content in case of multiple ownership claims. Typically, a watermark can be a random signal, a meaningful message, or logo. It is viewed as an effective way to prevent user’s content from illegal distribution [1,2]. A general scheme for digital watermarking is given in Fig. 1. The secret watermark is embedded into the original image using the embedding algorithm. Then, the watermarked image passes through the transmission channel. The transmission channel includes possible attacks, such as lossy compression, geometric distortions, signal processing operations, etc. After the watermarked image being passed through these possible operations, the watermark is to be extracted using an

* Corresponding author. Tel.: +962 2 72 0 1000 22559; fax: +962 2 7095018. E-mail address: [email protected] (H.A. Al-Otum). 0045-7906/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.compeleceng.2009.01.007

674

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695

Fig. 1. General watermarking scheme.

extraction algorithm. In some watermarking schemes, a secret key at the embedding process is used. Only the owner of the data knows the key and it is not possible to remove the message from the data without the knowledge of the key. A good watermarking model has many important properties such as invisibility, robustness to various types of attacks (such as filtering, compression, and geometric transformations) [3], and accurate detection [4]. Although the main motivation behind digital watermarking is the copyright protection, its applications are not that restricted. There is a wide application area of digital watermarking, including broadcast monitoring, fingerprinting, authentication and covert communication [5–9]. Based on the application, one can divide watermarking into different categories: (1) robust watermarking: that is mainly used for copyright protection and ownership verification since it is designed to resist intentional or unintentional attacks; (2) visible/invisible watermarking: i.e. is seen/unseen by the human visual system (HVS), here, the invisible watermark is widely used and is embedded in the host image in a way that it will not be perceived by the HVS; (3) fragile/semi-fragile watermarking: any slight change to the watermarked image can be detected and localized using fragile watermarking, while in semi-fragile watermarking, the changes can be detected and localized if they exceeded a specified threshold; and, (4) blind/non-blind watermarking: if the original image is used in watermark extraction or detection process, then the watermark is called blind, otherwise, it is called non-blind watermark. Digital watermarking techniques must satisfy the following properties: (1) quality: the watermark is not visible in the image under typical viewing conditions. In addition, the embedded watermark must not spoil the quality of the image and this is mostly measured by peak signal to noise ratio (PSNR) of the watermarked image; (2) robustness to attacks: the watermark can still be detected under the effect of intentional or unintentional various signal processing operations or/ and geometric distortions [10]; and, (3) capacity: the watermarking technique must be capable of allowing a suitable watermark size to be inserted in an image. The relationship between these three properties is demonstrated in Fig. 2: as

Fig. 2. Relationship between robustness, capacity, and quality.

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695

675

the demand to robustness is increased, the other two properties will be decreased. For high quality and low visual distortion, the watermark must modify the information contained in the original image with as minimum as possible. However, increasing the quality will decrease the other two properties. In image watermarking schemes, the watermark embedding process can be performed in different ways, since images allow multiple manipulations without affecting their perceptual quality. However, since robustness is the most important watermarking property, questions such as where and how to place the watermarks are important issues. In order to increase the robustness of the watermarking scheme, many approaches were discussed in literature and were mainly based on the discrete cosine transform (DCT) or the discrete wavelet transform (DWT). Cox et al. [11] explained the principles that govern the watermarking procedure and watermarking applications. Thus, various properties of the watermarks, such as how they respond to common signal transformations or deliberate attacks, were discussed. The authors gave example of a basic class of watermarking methods in a mathematical model, and used restricted and unrestricted keys to achieve the secrecy of the watermark. In [9], Cox et al. invented the idea of using spread spectrum for embedding watermarks in DCT domain. Extraction was based on the knowledge of the original signal and exact frequency locations of the watermark bits. This method was robust to scaling, and JPEG compression. Kutter et al. [12] proposed a new HVS having optimized weighting function for hiding spread spectrum watermark in the image. This method increased the robustness by facilitating the insertion of higher energy watermark while maintaining the same low visual watermark distortion. Piva et al. [13] examined another global DCT-based blind method for watermark detection. The global DCT coefficients are reordered using a zigzag scan. Fixed DCT coefficients (e.g. 16,000th to 25,000th coefficients for an image with size larger than 256  256) are selected for watermark embedding and retrieval. This method has a common drawback, that is, the watermarking process is very slow due to the high computational complexity in calculating DCT coefficients. Piva et al. [14] proposed a model based on the exploitation of the characteristics of the HVS by building a mask for perceptual hiding of the watermark in the full-frame DCT domain. Xia et al. [15] proposed a watermarking scheme based on DWT. The watermark, modeled as a Gaussian noise, is added to the middle and high frequency bands of the image. Extraction was based on taking sections of the watermark that are extracted then correlated with sections of the original watermark. Wang et al. [16] discussed a blind wavelet-based algorithm for ownership verification of digital images. Here, the host image is transformed using multilevel DWT, the sub-band selected to embed the watermark is chosen from the middle frequency sub-bands, which enables minimum perceptual error and high robustness against filtering. The binary watermark is transformed into real numbered values using a rotational matrix; then, real numbered watermark is weighted by a suitable coefficient, and the insertion process is obtained by changing the selected sub-band by the weighted real valued watermark. However, the capacity of this method is not large enough to contain the owner data; moreover, it is easy to extract the watermark once the host sub-band is known. Yuan and Zhang [17] proposed a new multiscale fragile watermarking scheme based on the Gaussian mixture model (GMM) which describes the statistical characteristics of images in the DWT domain. With wavelet sub-spaces being divided into watermarking blocks, the GMM parameters of different watermarking blocks are adjusted to form certain relationships. A secret embedding key is added to securely embed the fragile watermark. Wang and Lin [18] proposed a wavelet-tree-based blind watermarking scheme for copyright protection. They benefited from [19], where the watermark is embedded in the wavelet coefficients in such a manner that each watermark bit is embedded by quantizing a single wavelet coefficient out of a set of coefficients corresponding to a particular spatial region. In [18], the wavelet coefficients of the host image are grouped into what is called super trees; the watermark bit is embedded by quantizing all the coefficients in super trees. The trees are quantized until enough difference is achieved between these trees. The difference between quantized and un-quantized trees carries the watermark information required to extract the watermark. The main shortcoming of this method is attributed to the quantization process required to embed each watermark bit that leads to a high computational cost. In [20], Al-Otum and Samara proposed an adaptive blind wavelet-based watermarking scheme using tree mutual differences that is based on exploiting mutual differences between the grouped coefficients of the so-called wavelet trees. Here, each watermark bit is embedded by introducing a predetermined difference between each tree pair, and that new host difference carries the same sign of the embedded bit. Watermark information is spread over a large spatial location. The main benefit of the introduced method is the robustness against the popular filtering operation and low quality JPEG/JPEG2000 compression. Barni et al. [21] proposed a new approach to mask the watermark according to the characteristics of the HVS. In contrast to conventional methods, operating in the wavelet domain, masking is accomplished, on a pixel-by-pixel basis, by taking into account the texture and the luminance content of all sub-bands. The watermark consists of a pseudorandom sequence which is adaptively added to the largest detailed sub-bands. For watermark detection, the watermark is detected by computing the correlation between the watermarked coefficients and the watermarking code. Thodi and Rodríguez [22] proposed a reversible watermarking scheme that enables the embedding of useful information in a host signal without any loss of host information. Previous reversible methods for data embedding suffered from undesirable distortion at low embedding capacities and lack of capacity control due to the need for embedding a location map. In [22], a histogram shifting technique is implemented as an alternative to embedding the location map. The proposed technique improves the distortion performance at low embedding capacities and mitigates the capacity control problem. Also, they propose a reversible data-embedding technique called prediction-error expansion that better exploits the correlation

676

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695

inherent in the neighborhood of a pixel. There is also a significant improvement in the quality of the watermarked image, especially at moderate embedding capacities. Several alternatives have been suggested to apply watermarking techniques to color images. One commonly used approach consists of marking the luminance component only, which makes the extension of grayscale techniques straightforward. The most relevant drawback to luminance watermarking is that luminance information is more perceptible to modifications than the chrominance. Kutter et al. [23] used the blue channel to embed the watermark, since it is claimed that the HVS is less sensitive to the blue band in the RGB space. This approach is similar to the grayscale schemes, since the watermark is embedded only in one layer of the RGB image. Piva et al. [24] have made an extension to the approach considered in [23]. Here, the three RGB channels were used to embed the watermark. The selection of the magnitude of the embedded watermark has taken into account the HVS sensibility to the spectrum: the magnitude of the watermark is 10 times more significant for the blue component compared to the green, and 5 times for the blue component compared to the red one. Fleet and Hegger [25] proposed a quite different approach which embeds a sum of sinusoids in the yellow-blue channels of the opponent-color representation. Here, the LAB space is extended to the S-CIELAB, which includes the spatial structure of the image. Zhao et al. [26] proposed an approach for the combined image authentication and compression of color images by making use of a digital watermarking and data hiding framework. The digital watermark is comprised of two components: a softauthenticator watermark for authentication and tamper assessment of the given image, and a chrominance watermark employed to improve the efficiency of compression. The multipurpose watermark is designed by exploiting the orthogonality of various domains used for authentication, color decomposition and watermark insertion. The approach is implemented as a DCT–DWT dual domain algorithm and is applied for the protection and compression of cultural heritage imagery. Among the mentioned algorithms, the work of Barni et al. [21] has interesting features in the sense that it allows for adapting the watermark strength according to the characteristics of the host image, however, the adaptivity can be increased by inserting the watermark bits selectively rather than into all wavelet coefficients in the three largest detailed sub-bands. In fact, the insertion of watermark bits in all locations of the mentioned sub-bands leads to a high perceptual degradation in the image quality and leads to a comparably low PSNR. Also, various versions of this algorithm can be extended to work with color images. In this work, a modified wavelet-based watermarking method is proposed and is based on the improved pixelwise (PW) watermarking scheme and is denoted as the selective PW (SPW). The proposed algorithm assigns specific locations to be candidates for holding watermark bits. This leads to improve the watermarked image quality and allows the scheme to be adaptive in terms of the imperceptibility and the watermark size. Also, the PW and SPW methods were extended to be implemented with color images. The organization of the remaining part of the paper is as follows: in Section 2, the PW method will be discussed in details and its features will be high lightened. In Section 3, the SPW algorithm will be introduced, and various methods that are considered as extensions of the PW and SPW methods are also introduced. Simulations and results are provided in Section 4, and concluding remarks are given in Section 5. Finally, the used references are listed.

2. Watermarking based on the PW method Image watermarking based on PW masking technique [21] has attracted our attention as an interesting and effective technique for image watermarking. This is attributed to the fact that PW exhibits a high watermarking payload as well as high robustness against attacks. 2.1. PW watermark embedding The main idea of the PW method is based on the HVS properties that are exploited to improve watermark robustness and invisibility in the DWT domain. Watermark strength is accomplished through a mask giving a pixel-by-pixel measure of the sensitivity of the human eye to the local image perturbations. The watermark consists of a binary pseudorandom sequence (±1) that is added to the DWT coefficients of the three largest detailed sub-bands of the image. To effectively hide the watermark, a pixel-by-pixel mask is built to give the maximum amount of modifications that can be applied to the DWT coefficients in the detailed sub-bands without considerably deteriorating watermark invisibility. Mask construction relies on a work by Lewis and Knowles [27], where a method is proposed to evaluate the optimum quantization step for each DWT coefficient according to psychovisual considerations. According to PW, to embed the watermark into the host image, it is decomposed using DWT into four levels. The DWT sub-bands at resolution level l {l = 0,1,2,3} and orientation h {h = 0,1,2,3} are denoted as Ihl (see Fig. 3). Here, I0l , I1l and I2l are the vertical, diagonal, and horizontal sub-bands respectively. At the embedding process, the watermark is embedded into the three largest detailed sub-bands (I00 ,I10 , and I20 ). These subbands were selected experimentally [21], based on a compromise between robustness and invisibility. Indeed, watermark embedding in the detailed sub-band I10 does not impose perceptible artifacts on the image and exhibits a good invisibility performance, at the expense of a low level of robustness. To compensate for this, watermark embedding is applied also to I00 and I20 sub-bands. Here, better robustness (e.g. compression or low-pass filtering) is achieved at the expense of a slight decrease in watermark visibility of disturbs added to these frequencies.

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695

677

Fig. 3. Four level DWT decomposition.

The watermark is a pseudorandom binary sequence (±1) arranged in 2D matrix xði; jÞ to have the same dimension as the sub-bands. This watermark is embedded by modifying the sub-bands wavelet coefficients according to:

~Ih ði; jÞ ¼ Ih ði; jÞ þ a  xh ði; jÞ  xði; jÞ 0 0

ð1Þ

where a is a scaling factor that specifies the watermark strength. w is a weighing function considering the local sensitivity of the image to noise. Ih0 is the original sub-band at level 0 and orientation h. The weighing function wh ði; jÞ, at pixel location ði; jÞ, considers how the eye perceives the distortion on the host image; therefore, this function represents the masking characteristics of the HVS. This mask adapts the quantization step of each wavelet coefficient according to the local noise sensitivity of the eye [27]. The following considerations have been taken into account in the PW method: (1) the eye is less sensitive to noise in high resolution bands, and in those bands having orientation of 45°; (2) the eye is less sensitive to noise in those areas of the image where brightness is high or low; and (3) the eye is less sensitive to noise in highly textured areas but, among these, more sensitive near the edges. Based on these considerations, the quantization step of each coefficient is computed as the weighted product of three terms [21]: h

qhl ði; jÞ ¼ Hðl; hÞ  Kðl; i; jÞ  Nðl; i; jÞ0:2

ð2Þ

The 1st term, Hðl; hÞ, takes into account how sensitivity to noise changes depending on the band, Hðl; hÞ is expressed as (1st consideration) [21]:

8 1:00 > ( pffiffiffi ) > > < 0:32 2 if h ¼ 1 Hðl; hÞ ¼  > 0:16 1 otherwise > > : 0:10

9 if l ¼ 0 > > > if l ¼ 1 = if l ¼ 2 > > > ; if l ¼ 3

ð3Þ

The 2nd term, Kðl; i; jÞ, takes into account the local brightness based on the gray level values of the low-pass version of the image (2nd consideration). This factor is computed in the following way [21]:

Kðl; i; jÞ ¼ 1 þ Lðl; i; jÞ

ð4Þ

where Lðl; i; jÞ [21]:

Lðl; i; jÞ ¼

     1 3 i j I3 1 þ 3l ; 1 þ 3l 256 2 2

ð5Þ

678

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695

and

L0 ðl; i; jÞ ¼



1  Lðl; i; jÞ; Lðl; i; jÞ;

if Lðl; i; jÞ < 0:5



otherwise

ð6Þ

The 3rd term, Nðl; i; jÞ, takes into account the texture area of the image where the eye is less sensitive while it is more sensitive near the edges (3rd consideration) [21]:

Nðl; i; jÞ ¼

3l 2 X 1 X 1 X 1 X k¼0

16k

h¼0 x¼0 y¼0

    2 i j i j Ihkþl y þ k ; x þ k  var I33 ð1 þ y þ 3l ; 1 þ x þ 3l Þ x ¼ 0; 1 2 2 2 2 y ¼ 0; 1

ð7Þ

Texture is measured as a product of two components: (1) the local square value of the DWT coefficient that represents the distance from the edges, in the detailed sub-bands of the host image, and (2) the local variance of the low-pass sub-band I33 which represents the texture. Both contributions are computed in a 2  2 neighborhood corresponding to the local pixel location (i, j). So, the PW model considers that any disturb with magnitude qhl ði; jÞ=2 or less is unperceivable. Thus, the weighing function xh ði; jÞ can be expressed as [21]:

xh ði; jÞ ¼ qhl ði; jÞ=2

ð8Þ

2.2. PW watermark detection Watermark detection is accomplished blindly without referring to the original image. The correlation q between the watermarked DWT coefficients ~Ih0 ði; jÞ and the watermark xði; jÞ, for all (i, j) locations used in the embedding process, is computed as follows [21]:

q¼

2 M 1 X N 1 X 1 X ~Ih ði; jÞxði; jÞ 3MN #¼0 i¼0 j¼0 0

ð9Þ

where MN is the size of the detailed sub-band at level 0. The computed q is compared to a threshold T q chosen in such a manner to allow a given probability of false positive detection, based on Neyman-Pearson criterion [28,29]. If q > Tp, then the watermark x is present; otherwise it is absent. To ensure that, the false detection probability does not exceed 108, the threshold Tp is chosen as [28]:

qffiffiffiffiffiffiffiffiffiffiffi T p ¼ 3:97 2r2qB where

r2qB [21]:

r2qB ¼ Here,

ð10Þ

1 ð3MNÞ2

2 M1 N1  2

X XX E ~Ih0 ði; jÞ h¼0

i¼0

ð11Þ

j¼0

r2qB is the worst case where a fake watermark is embedded other than the original watermark.

3. Watermarking based on modified PW methods It is evident, from the description of the PW method, that: (1) PW modifies all DWT coefficients in the detailed sub-bands, i.e. Ih0 ði; jÞ; 8ði; jÞ. In fact, this leads to a considerable decrease in the watermarked image quality (in terms of PSNR). (2) The approximation sub-band (I33 ) is not used to embed the watermark, therefore, the wavelet coefficients are static. However, qhl ði; jÞ depends on the local brightness Kðl; i; jÞ which is based on the gray level values of the low-pass version (I33 ) of the watermarked image. (3) PW was not extended to deal with color images. In fact, there is a variety of proposed techniques that can be extended from PW to be implemented with color images as will be seen later. Consequently, in the next section, various PW modifications will be proposed for adaptive grayscale as well as color image watermarking. 3.1. The proposed selective pixel-wise (SPW) watermarking algorithm It was shown that the PW method modifies all DWT coefficients in the host image to embed the watermark bits. This modification asserts on the quality of the image and the distortion can be observable, thus, the demand is to improve the

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695

679

image quality while maintaining high robustness against attacks. This modification is denoted as selective pixel-wise masking technique (SPW). SPW is a modified PW method where the bit insertion is made selectively, according to the local characteristics of the pixel under consideration. Here, the value of the weighing function is taken as a reference for watermark bit insertion. If the DWT coefficient at a location (i, j) in the detailed DWT sub-bands I00 ði; jÞ; I10 ði; jÞ&I20 ði; jÞ has a high enough xh ði; jÞ value, then such a location is considered to hold a watermark bit. To more precisely define ‘‘high enough‘‘, an adaptive threshold T pw is considered:

T pw ¼ b  maxðxh ði; jÞ; 8ði; jÞ 2 I00 ði; jÞ; I10 ði; jÞ & I20 ði; jÞÞ

ð12Þ

where, b is a constant used to calibrate the total number of watermark bits to be inserted. Here, T pw is calculated by finding the highest xh ði; jÞ for all locations (i, j) in the detailed DWT sub-bands at level 0, then, multiplying it by the selected value of b 2 [0, 1]. Note that when b is high, the threshold T pw is also high and vice versa. Consequently, If it occurs that for a given DWT coefficient at location (i, j), the value of the weighing function xh ði; jÞ P T pw , then such a location is considered as a good candidate for watermark bit insertion, otherwise, watermark bit insertion may lead to a considerable reduction in the image quality at the expense of a slight increase in robustness against attacks, and hence, no watermark insertion takes place. It is clear that the higher T pw , the lower the number of inserted watermark bits. This process can be controlled by the b parameter and can be varied according to the application under consideration: when robustness is of interest, b is to be small, while when the image quality is of great importance, b is to be high. Another key issue is the function Kðl; i; jÞ that has been skipped in the PW method. In fact, the approximation sub-band (I33 ) was not used to embed the watermark, therefore, the wavelet coefficients are static. In the SPW method, this Kðl; i; jÞ is used to specify a new threshold value based on calculating the mean value of the approximation sub-band I33 as:

T pA ¼ E½Kðl; i; jÞ ¼ 1 þ

    

1 i j E I33 1 þ 3l ; 1 þ 3l 256 2 2

ð13Þ

Note that hiding the watermark bits in high or low brightened regions of the image is less sensitive to the HVS. The mean value of local brightness is used as a complement threshold value for the SPW method. Here, the mean of high bright values gives high threshold value and the same thing is true for low bright values. From a detailed experimental test, it was found that T pA is relatively constant, regardless of the watermarked image being affected by attacks. Evidently, the watermark locations are selected for watermark bits insertion, if the DWT coefficient at location (i, j) has a weighing function xh ði; jÞ P T pw &xh ði; jÞ P T pA , otherwise, the location is discarded and no watermarking takes place, as illustrated in Fig. 4. Thus, SPW embedding can be described as:

~Ih ði; jÞ ¼ Ih ði; jÞ þ a  W h ði; jÞ  xði; jÞ 0 0 m

ð14Þ

where

W hm ði; jÞ ¼



xh ði; jÞ; if xh ði; jÞ P T pw & xh ði; jÞ P T pA 0;



otherwise

ð15Þ

As a result, the watermark size can be specified based on the selected locations. While T pA is slowly varying, T pW controls the selected locations for watermark embedding process. In other words, SPW method permits to adapt the watermark size with respect to the host image, while taking into account the trade-off between invisibility, robustness, and image quality. The watermark detection process is done based on the computation of the correlation coefficient (q), and the threshold value is taken based on the analysis in the PW method as:

q¼

M1 N1 XX 1 ~Iði; jÞ  xði; jÞ P T q 3ðW  SÞ i¼0 j¼0

ð16Þ

where W  S is the watermark size. Here, SPW calculates the q value for all locations, then applies the division by only W  S, since for locations with no watermark bits, ~Iði; jÞ  xði; jÞ ¼ 0. 3.2. Color watermarking using grayscale-wise PW method (G-PW) A direct extention of the PW method, is to embed the watermark bits in the grayscale version of the RGB image. Here, various formats can be used (YIQ, YUV, YCrCb). This version is called grayscale-wise PW (G-PW). The Y component is calculated based on the ITU-R601 standard, known as YCbCr [24], which is appropriate for digital imaging:

Y ¼ 0:229R þ 0:587G þ 0:114B

ð17Þ

Figs. 5 and 6 illustrate the G-PW embedding and detection steps, respectively, where, the RGB image is converted to YCbCr format. The watermark is embedded in the Y component using PW method, then the new watermarked RGB image is reconstructed again using the watermarked Y, that is combined with the original Cb, and Cr components.

680

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695

Fig. 4. SPW embedding and detection scheme.

Fig. 5. G-PW embedding process.

3.3. Color watermarking using multi spectral-PW method (MS-PW) In this version, watermark embedding is applied to each layer R, G, and B simultaneously. As a result, watermarking presents in the three spectral components, and this modification is called multi spectral-PW method (MS-PW). Here, MS-PW method deals with the RGB image as three grayscale independent layers that are used to embed the watermark using PW method. Here, three independent watermarking sequences are generated and applied to each layer independently.

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695

681

Fig. 6. G-PW detection process.

Fig. 7. MS-PW embedding process.

At the embedding step, the RGB image is splitted into the R, G, and B layers, then; DWT is obtained for each layer independently. The PW method is applied to the wavelet coefficients of the R, G, and B layers independently. Fig. 7 summarizes the MS-PW embedding process. At the detection step, the watermarked image is transformed to the wavelet domain after RGB decomposition being applied, then, the correlation coefficient q for each layer is calculated as in PW method:

682

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695

qR ¼

2 M1 N1 XX 1 X ~Ih ði; jÞxR ði; jÞ 3MN #¼0 i¼0 j¼0 0R

ð18Þ

qG ¼

2 M1 N1 XX 1 X ~Ih ði; jÞxG ði; jÞ 3MN #¼0 i¼0 j¼0 0G

ð19Þ

qB ¼

2 M 1 X N1 X 1 X ~Ih ði; jÞxB ði; jÞ 3MN #¼0 i¼0 j¼0 0B

ð20Þ

where qR ; qG &qB are the correlation coefficients for the watermarked R, G, and B layers, respectively, ~Ih0R ; ~Ih0G &~Ih0B are the watermarked DWT coefficients at level 0 and orientation h in the R, G, and B layers, respectively. xR ði; jÞ; xG ði; jÞ&xB ði; jÞ are the watermarks embedded in the R, G, and B layers, respectively. Next, the average q for the three layers is compared with the computed threshold value T q to judge watermark existence:

qAv g ¼

qR þ qG þ qB

ð21Þ

3

The block diagram in Fig. 8 demonstrates this process. It is clear that the watermark size is tripled. For instance, if the color image, under consideration, is of the size 512  512 pixels, then the size of the watermark will be 3  3  256  256 = 589,824 bits = 576 kb (with a payload of 0.75 bpp). Such an increase in watermark payload is a good achievement. However, the main shortcoming is its effect on the output PSNR. Sometimes, there is a great demand on having large watermark size at a given acceptable image quality. In the next section, a modified version of MS-PW is proposed to give a compromise solution between robustness and invisibility. 3.4. Color watermarking using multi spectral-SPW method (MS-SPW) The major point in this method is that it implements SPW instead of the PW in the MS-PW. The advantage of this method is to improve the image quality of the colored images, however, the watermark size is decreased due to the selective nature of the SPW method. Ultimately, one can calibrate the watermark size, invisibility, and PSNR by calibrating b. At the embedding step, as shown in Fig. 8, the RGB image is transformed to the wavelet domain. Each transformed layer is processed separately using the SPW embedding algorithm. Here, the threshold values, Tpw and TpA are found for each layer independently, then, a new mask is built for each R, G and B layers as: WmR(i, j), WmG(i, j), & WmB(i, j), respectively. The number of the selected locations varies according to the layer properties and the image characteristics. After that, the watermark can be embedded in each layer using Eqs. (12)–(15). At the detection step, the correlation coefficient for each layer is calculated as: M1 N1 XX 1 ~Ih ði; jÞ  xR ði; jÞ 3 W  SÞR i¼0 j¼0 0R

qR ¼

M 1 X N 1 X

1 qG ¼

3 W  SÞG

i¼0

ð22Þ

~Ih ði; jÞ  xG ði; jÞ 0G

ð23Þ

j¼0

M1 N1 XX 1 ~Ih ði; jÞ  xB ði; jÞ 3 W  SÞB i¼0 j¼0 0B

qB ¼

ð24Þ

where (W  S)R, (W  S)G, (W  S)B are the watermark sizes in the R, G, and B layers respectively. The obtained values qR ; qG &qB are averaged qAv g ¼ ðqR þ qG þ qB Þ=3 and compared to a predetermined threshold value T q and the detection process is similar to that shown in Fig. 9. 3.5. Color watermarking using multi spectral-maximum-based PW (MS-MPW) To introduce a higher level of security as well as to increase the PSNR value over what is possible using the MS-SPW method, an interesting version is proposed and called multi spectral-maximum-based PW method (MS-MPW). MS-MPW starts by applying DWT to the R, G, and B layers of the RGB image. Next, the quantization matrix is built for each location (i, j) in the three detailed sub-bands of the DWT level 0 of the R, G, and B layers, i.e. xhR ði; jÞ; xhG ði; jÞ&xhB ði; jÞ. Here, these weights are compared for each (i, j) location in the R, G, and B layers, and the highest value of which is selected to hold the watermark bit and the other two locations will not be watermarked. Evidently, the output RGB mask is made of three R, G, and B submasks. To construct a new sub-mask for the red component, i.e. WmR(i, j), set WmR(i, j) = 0 for all (i, j) locations. Next, if xhR ði; jÞ P xhG ði; jÞ&xhR ði; jÞ P xhB ði; jÞ, then WmR(i, j) = xhR ði; jÞ. The process is repeated for all locations in the three DWT detailed sub-bands. This can be formulated as:

 W mR ði; jÞ ¼

xhR ði; jÞ; if xhR ði; jÞ ¼ maxðxhR ði; jÞ; xhG ði; jÞ; xhB ði; jÞÞ 0;

elsewhere

 ð25Þ

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695

Fig. 8. MS-SPW embedding process.

683

684

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695

Fig. 9. MS-PW detection process.

The same procedure can be repeated to construct The G- & B- sub-masks WmG(i, j), and WmB(i, j), respectively:

W mG ði; jÞ ¼ W mB ði; jÞ ¼



xhG ði; jÞ; if xhG ði; jÞ ¼ maxðxhR ði; jÞ; xhG ði; jÞ; xhB ði; jÞÞ 0;



elsewhere  h  xB ði; jÞ; if xhB ði; jÞ ¼ maxðxhR ði; jÞ; xhG ði; jÞ; xhB ði; jÞÞ 0;

elsewhere

ð26Þ ð27Þ

Finally, the output watermark locations are defined by the non-zero components of the masks WmR(i, j), WmG(i, j), and WmB(i, j). For better illustration, Fig. 10 demonstrates the MS-MPW algorithm. It is worthy to mention that the size of the watermark is equal to the size of the mask implemented with the G-PW method, since only one bit is added to one location in the color triplets each time. Finally, the detection process is similar to the detection step in MS-SPW based on Eqs. (22)–(24). 4. Simulations and experimental results 4.1. Introduction In this section, the conventional PW as well as the proposed methods will be tested using a variety of well-known grayscale and true-color images, the size of which is 512  512 pixels with a resolution of 8 bpp for the grayscale and 24 bpp for color images respectively. Here, the used image database contains a large number of test images with there own properties including ‘‘Lena”, ‘‘Barbara”, ‘‘Baboon”, ‘‘Peppers”, and ‘‘Fishing Boat”. All the images used in the simulations are transformed into DWT using Daubechies-1 filtering kernel. Here, the embedded watermark is a binary pseudorandom sequence (±1). 4.2. Testing the PW method In the PW method, the watermark is added to the DWT coefficients of the three largest detailed DWT sub-bands. The watermark strength can be controlled by calibrating the factor a. It is important to select the value of a, to preserve the invisibility constraint and image quality without loosing robustness. Fig. 11 depicts the watermarked PSNR value for various test images vs. a. In Fig. 11 as a is increased, PSNR is decreased. This implies that there is a degradation in image quality and in the watermark

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695

685

Fig. 10. MS-MPW embedding process.

invisibility. Based on a detailed analysis using various test images, a has been selected as a = 0.2, which gives a trade-off between robustness, invisibility, and image quality. This value of a ensures that the output PSNR will be not less than 32–33 dB. In order to detect the watermark, the correlation between the watermark and the watermarked coefficients is computed as in Eq. (9). The value of q is compared to a threshold T q to decide whether the watermark is present or absent. The Neyman-Pearson criterion is used to specify the T q value as in Eq. (10). Here, given an input image and a watermark X, only three cases are possible:

686

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695

Fig. 11. The effect of adapting a on PSNR for different watermarked images.

Case A: image is not watermarked. Case B: image is watermarked with a code other than X. Case C: image is watermarked with the code X. The PW algorithm is tested for these three cases when applied to various test images. Results are shown in Table 1 where it is clear that PW method is able to detect the correct watermark (case C) since q P T q . 4.3. Testing the proposed SPW method The SPW method is proposed to improve the watermarked image quality based on modifying selected wavelet coefficients during the watermark embedding process. It was clear that the SPW method depends on two threshold values (T pW and T pA ); where T pA is relatively constant, while T pW has a crucial effect on selecting the watermark embedding locations. To check the validity of the SPW method, the watermarked images were applied to the detection process as prescribed for the conventional PW method. Table 2 presents the watermark detection results for the three cases A, B, and C. As well seen from Table 2, the SPW method could detect the correct watermark (case C).

Table 1 PW validity check for different test images for the cases A, B, and C. Image

Lena Peppers Baboon Barbara Boats

PSNR (dB)

37.48 33.67 37.84 35.28 36.78

W  M size

2

3  256 3  2562 3  2562 3  2562 3  2562

Tp

Case A

0.0323 0.0490 0.0799 0.0642 0.0478

Case B

Case C

q

W  M existence

q

W  M existence

q

W  M existence

0.0032 0.0021 0.0081 0.0012 0.0059

No No No No No

0.0071 0.0069 0.0164 0.0089 0.0133

No No No No No

0. 795 0.877 1 1 0.865

Yes Yes Yes Yes Yes

Table 2 SPW validity check for different test images for the case A, B and C. Image

Lena Peppers Baboon Barbara Boats

Tp

0.0311 0.0476 0.0789 0.0631 0.0464

Case A

Case B

Case C

q

W  M existence

q

W  M existence

q

W  M existence

0.0031 0.0032 0.0094 0.0013 0.0077

No No No No No

0.0037 0.0118 0.0128 0.0057 0.0101

No No No No No

0.415 0.433 0.431 0.523 0.404

Yes Yes Yes Yes Yes

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695

687

Next, let us study the influence of varying b on the output PSNR as well as on the watermark size and the resulting q value. Fig. 12 depicts PSNR vs. b, while Fig. 13 depicts the watermark size vs. b. For better illustration, Table 3 demonstrates the interaction of these parameters for different test images. An interesting test is to investigate the effect of changing the watermark size on the output PSNR value which can be used as a trade-off between robustness and imperceptibility. Fig. 14 depicts this relationship for different test images.

Fig. 12. PSNR vs. b for different images using SPW method.

Fig. 13. The watermark size vs. b for different images using SPW method.

Table 3 Comparison of b, PSNR, the watermark size, and the correlation coefficient q for different images using the SPW method. Image name

Optimum b

PSNR (dB)

q

W  M size

Lena Baboon Peppers Barbara Boats

0.11 0.14 0.12 0.22 0.15

42.94 40.58 43.66 39.9 42.23

0.415 0.431 0.433 0.523 0.404

63,963 61,383 43,410 61,122 47,949

688

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695

Fig. 14. PSNR vs. the watermark size for different test images using SPW method.

4.4. PW vs. proposed SPW In this section, comparisons between the PW and the proposed SPW methods are presented and discussed. As prescribed, the SPW method was proposed to improve the PSNR of the watermarked image. A comparison between PW and SPW in terms of the output PSNR is illustrated in Fig. 15. Analyzing Figs. 12–15 and Table 3, we may draw the following notes: (1) A considerable improvement in the image quality is achieved when using SPW over what is possible with PW. Here, PSNR has been improved in the range of (2.2–7.28) dB with different test images. (2) Selecting high b values, leads to increasing the achieved PSNR while reduces the watermark size. In fact, the selection of b value provides a trade-off between the image quality and the watermark size. This selection can be considered as an adaptive feature in SPW over conventional PW in terms of the ability to vary b in an application-based fashion: when high output image quality is a demand, b is to be increased while if high robustness is required, then b is to be decreased. Next, the PW and SPW methods are tested against attacks. Here, a wide range of attacks were used including: Gaussian noise, tamper scaling, histogram equalization, rotation, image resizing, wiener filtering, salt & pepper (S&P) filtering, and median filtering at different window sizes. Results are shown in Table 4. Also, the well-known JPEG compression was investigated with PW and SPW methods as shown in Fig. 16. Here, it can be observed that: (1) Both methods passed the image enhancement attacks, i.e. tamper scaling and histogram equalization. (2) Both methods passed the JPEG compression test with high compression ratio (60–80%). In fact, PW slightly overcomes the SPW method, and this was expected as a result of improving the PSNR value at the expense of a slight deterioration in the algorithm robustness.

Fig. 15. PSNR values using PW method and SPW method for different images.

689

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695 Table 4 Evaluation of PW and SPW methods under different types of attacks for the test image Barbara. Applied attack

PW Tp = 0.0311

Gaussian noise r2 = 0.01 Tamper scaling Histogram equalization Rotation by 2° Rotation and re-rotation Image resize Wiener filter for Gaussian noise S&P intensity = 0.15 Median filter window size[3  3] for S&P Median filter window size [5  5] for S&P Median filter window size [7  7] for S&P

SPW Tp = 0.0464

q

W  M detect.

q

W  M Detect.

0.986 1 0.974 0.0131 1 0.459 0.219 0.962 0.0303 0.022 0.0118

Yes Yes Yes No Yes Yes Yes Yes No No No

0.5024 0.523 0.583 0.0138 0.523 0.202 0.142 0.416 0.021 0.0089 0.0043

Yes Yes Yes No Yes Yes Yes Yes No No No

Fig. 16. Testing (a) PW and (b) SPW with JPEG compression for the test image Lena.

(3) For the Gaussian noise attack, both methods can detect the watermark, while PW method was better in watermark detection than SPW method under the wiener filter attack. (4) When S&P noise, both methods also can detect the watermark, while removing the effect of S&P noise by using median filter has a strong effect when applying it with large window sizes (5  5 or more). (5) Finally, geometric operations (rotation and image resize) destroyed the watermark, so, the watermark detection process failed in both methods, while re-rotation operation passed the test for both methods.

4.5. Testing the proposed G-PW method As prescribed, G-PW is a direct extension of the grayscale PW to be implemented with color images by applying PW to the layer that represents the grayscale version of the color image, i.e. Y in YCbCr model. Here, G-PW is tested for validity as shown in Table 5. Evidently, the G-PW method has the ability to detect the correct watermark.

Table 5 G-PW validity check for the three cases applied to different test images. Image

Lena Peppers Baboon Barbara Boats

PSNR (dB)

37. 79 37.91 29.71 32.46 34.36

Tp

0.028 0.0422 0.0688 0.0533 0.0411

Case A

Case B

Case C

q

W  M existence

q

W  M existence

q

W  M existence

0.0045 0.0035 0.0136 0.0018 0.0088

No No No No No

0.006 0.0063 0.0143 0.0077 0.0107

No No No No No

0.6995 0.769 0.986 1 0.747

Yes Yes Yes Yes Yes

690

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695

4.6. Testing the proposed MS-PW method Obviously, MS-PW is considered as PW with applying PW to each layer of the color image (R, G, and B) simultaneously. This method is used with the same parameters that were used in G-PW method. The only difference is in watermark size which is equal 3  3  2562 bits because it deals with each layer as a grayscale version of the image and embeds the watermark in each layer. The same verification method and test under attacks are applied to the watermarked image. Results are shown in Table 6. 4.7. Testing the proposed MS-SPW method The MS-SPW method is similar to MS-PW, but with implementing SPW instead of PW with each layer in the color image. Here, MS-SPW succeeded to detect the correct watermark bits as shown in Table 7. It is worthy to remember that the selection of b plays an important role in defining the watermark bits locations, therefore, Figs. 17 and 18 depict the watermarked PSNR as well as the watermark size vs. b. The empirically selected values for b and the corresponding PSNR & W  M size for different images are presented in Table 8.

Table 6 MS-PW validity check for the three cases applied to different test images. Image

Lena Peppers Baboon Barbara Boats

PSNR (dB)

37.32 33.81 29.78 31.66 33.48

Tp

Case A

0.0554 0.0837 0.1395 0.1134 0.0836

Case B

Case C

q

W  M existence

q

W  M existence

q

W  M existence

0.0042 0.0040 0.0164 0.0018 0.0104

No No No No No

0.0063 0.0072 0.0171 0.0080 0.0129

No No No No No

0.779 0.879 1 1 0.866

Yes Yes Yes Yes Yes

Table 7 MS-SPW validity check for the three cases applied to different test images. Image

Tp

Lena Peppers Baboon Barbara Boats

0.0959 0.0812 0.137 0.111 0.0813

Case A

Case B

Case C

q

W  M existence

q

W  M existence

q

W  M existence

0.0045 0.0056 0.0216 0.0018 0.014

No No No No No

0.0068 0.0106 0.0268 0.0005 0.0234

No No No No No

0.567 0.862 0.772 0.814 0.747

Yes Yes Yes Yes Yes

Fig. 17. Plot of b vs. PSNR using MS-SPW method for different images.

691

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695

Fig. 18. Plot of b vs. watermark size using MS-SPW method for different images.

Table 8 Emperically selected b and the corresponding PSNR, watermark size, and correlation coefficient q for different images using the MS-SPW method. Image Name

Optimum b

PSNR (dB)

q

W  M size

Lena Baboon Peppers Barbara Boats

0.10 0.15 0.11 0.16 0.14

42.65 41.12 42.86 40.31 41.29

0.567 0.862 0.772 0.814 0.747

217,254 148,149 135,237 207,972 142,775

Evidently (Figs. 17 and 18), when increasing b, the watermarking image quality (in terms of PSNR) will be increased. On the other hand, b is inversely proportional to the watermark size, and this implies that the watermark size can be adapted according to the pre-assigned PSNR value. 4.8. Testing the proposed MS-MPW method This method depends on the MS-PW with embedding every time one bit only in one of the available three locations in R, G, and B layers. This is done by selecting the maximum value of the weighing function from each layer xhR ði; jÞ, xhG ði; jÞ, and xhB ði; jÞ. Then, the watermark bits are embedded in the selected locations to improve the image quality (PSNR) compared with the PW method. The watermark size is decreased compared with the MS-PW method, while PSNR is expected to be the highest. Another feature is that the watermark is fixed (3  2562) while the bit insertion is dynamic and can be every time in a different layer of the color image which increases the level of security as well. Table 9 shows MS-MPW validity check for different test images. As seen form Table 9, one can note that the correlation coefficient q is decreased when compared with that for the MSPW method. This is because of the spreading nature in selecting the embedding locations since the watermark bits are Table 9 MS-MPW validity check for the three cases applied to different test images. Image

Lena Peppers Baboon Barbara Boats

PSNR (dB)

45.19 43.29 40.45 41.21 43.55

Tp

0.0532 0.0815 0.137 0.111 0.0814

Case A

Case B

Case C

q

W  M existence

q

W  M existence

q

W  M existence

0.0046 0.0030 0.0100 0.002 0.0185

No No No No No

0.0072 0.0042 0.0127 0.0053 0.0264

No No No No No

0.277 0.338 0.456 0.465 0.326

Yes Yes Yes Yes Yes

692

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695

embedded selectively into different RGB layers, and thus, a noticeable increase in PSNR value is expected as well seen from Table 9. 4.9. Evaluation of the proposed methods for colored images After illustrating the proposed methods: G-PW, MS-PW, MS-SPW, and MS-MPW, it is useful to compare the performance, watermark size, and robustness of these methods. Let us start with the watermarked image quality, which is considered, in this work, as the most important factor in the watermarking process. Fig. 19 illustrates a comparative evaluation of the four methods in terms of the achieved PSNR values for different test images. As clear from Fig. 19, PSNR of MS-PW method is slightly smaller than with G-PW because the number of modified coefficients in MS-PW is three times more than when using G-PW. When comparing MS-SPW and MS-MPW, we note that MSMPW method exhibits better PSNR than MS-SPW by (2–3) dB, due to the fact that modified coefficients are fewer than the modified ones in MS-SPW. Finally, MS-MPW method exhibits the best PSNR performance. Beside the PSNR improvement, the watermark size is another important requirement that should be satisfied. Fig. 20. demonstrates a comparison between the four methods in terms of the watermark sizes. It is clear that MS-PW has the highest watermark size (b has been selected as b = 0.75xmax). The robustness against attacks is one of the important requirements in copyright protection. The robustness of the four methods is compared as illustrated in Table 10. Analyzing Table 10, the following notes can be drawn:  The proposed methods are robust to tamper scaling where the pixel values are increased randomly by amount of 30.  The four methods also passed the Gaussian noise attack (with q 2 [0.27, 0.77]) that has been added to the watermarked image with zero-mean and variance of 0.01.  S&P noise: is added to the watermarked images with 15% intensity. The methods can strongly detect the watermark under this type of noise.  Filtering: the wiener filter, with window size 5  5, is used to remove the effect of Gaussian noise that was applied to the watermarked image. This type of attack has a strong effect on the embedded watermark (shaded area); therefore, some methods can detect the watermark (G-PW and MS-PW). As a result the watermark could not survive this type of filtering for all images.

Fig. 19. Performance comparison between the proposed methods: G-PW, MS-PW, MS-MPW, and MS-SPW.

Fig. 20. Watermark size for the proposed methods: G-PW, MS-PW, MS-MPW, and MS-SPW.

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695

693

Table 10 Comparison of the four methods in watermark detection: G-PW, MS-PW, MS-MPW, and MS-SPW.

 Geometric operations: (1) rotating the watermarked image by more than 2° has a strong effect which led to destroy the hidden watermark. (2) Image resize: this type of attack has been passed and can be detected. (3) Rotating and re-rotating the watermarked image led to a slight reduction in the correlation coefficient q. Here, the rotation re-rotation process has been performed with an angle h 2 [45°, 45°].

694

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695

Fig. 21. Comparison between the four methods under JPEG compression.

Table 11 Systematic Comparison between the prposed color watermarking techniques: G-PW, MS-PW, MS-SPW and MS-MPW. Technique

Description

Payload bpp

Imperceptibility PSNR

Robustness JPEG

Rot.

Noise

Filtration – Good with Wiener for Gaussian – Weak with Median with 5  5 wind. and above – Good with Wiener for Gaussian – Weak with Median with 5  5 wind. and above – Weak with Wiener for Gaussian – Weak with Median with 5  5 wind. and above – Weak with Wiener for Gaussian – Weak with Median with 5  5 wind. and above

G-PW

PW Embed. into Y of YCbCr

(constant) 0.75 bpp

2[29.71–37.79]

58–85%

6 2%

– Guas.: up to r = 0.01 – S&P: up to 0.15 density

MS-PW

PW Embed. into R, G and B of RGB

(constant) 2.25 bpp

2[40.45–45.19]

48–71%

–

–

MS-SPW

SPW Embed. into R, G and B of RGB

(variable) up to 2.25 bpp according to b

2[40.31–42.65]

47-69%

–

–

MS-MPW

Embed. in one layer at a time with maximum x coef.

(constant at random locations) 0.75 bpp

2[40.45–45.19]

25–62%

–

–

 Color alteration: (1) reducing the number of colors from 24 to 8 bits/pixel affected the embedded watermark, so, q is decreased but the watermark still be detected. (2) Color adjustment is applied to the watermarked image by randomly changing the component values with 20% of its initial value. Here, the watermark is still detectable.  Histogram equalization is applied; then, watermark detection successfully passed the test with high value of q. Also, the four algorithms are compared in terms of its resistance to the well-known JPEG compression. The compression ratio has been increased gradually form 1 toward 100%, and the process has been stopped when reaching a critical value of q beyond which the watermark detection has failed. Results are shown in Fig. 21. As well seen from Fig. 21, the G-PW method outstands the other techniques for different test images. Finally, to summarize the achievements obtained using the various methods of the proposed family of digital watermarking algorithms, a systematic comparison for all of them, in terms of imperceptibility, capacity, and robustness (against compression, noise, and filtration) is demonstrated in Table 11. 5. Conclusions In this paper, a family of modified wavelet-based watermarking methods is proposed and is based on exploiting the characteristics of the HVS. The proposed algorithms are based on the pixel-wise (PW) watermarking scheme proposed in [21]. The PW method has been used with grayscale images only, and basically, modifies each DWT coefficient (in the three detailed sub-bands of the first DWT level) to embed the watermark bits. As a result, the quality of the watermarked image is considerably reduced (PSNR 6 38 dB). The proposed SPW method for grayscale images was a useful method to improve the image quality, when compared with the PW method. Here, PSNR has been improved in the range of (2.2–7.28) dB with

H.A. Al-Otum, A.O. Al-Taba’a / Computers and Electrical Engineering 35 (2009) 673–695

695

different test images. This was done by modifying only selected coefficients (based on a modified visibility criterion) instead of modifying all coefficients. A trade-off between invisibility, robustness, and image quality has been achieved using the SPW method. Here the SPW method: (1) improved the image quality without loosing the invisibility and robustness to different types of attacks; (2) allowed for adapting the watermark size (capacity) according to the number of selected watermark locations; (3) gives an ability to detect the watermark using the statistical characteristics of the watermarked image without referring to the original image (blind watermarking). Next, the SPW algorithm was tested under different types of attacks: (1) SPW was robust to various types of attacks except the geometric rotation by more than 2°; (2) SPW was robust to JPEG compression with high compression ratios (60–80%) based on the test image. The PW and the proposed SPW methods were extended to be implemented with color images. Here, four modifications were proposed: (1) The G-PW method that embeds the watermark bits into the Y component after converting the RGB image to YCbCr representation. (2) The MS-PW method that embeds the watermark into R, G, and B layers independently. It was shown that the quality of the watermarked image in the MS-PW is slightly lower than with G-PW. However, the two methods are robust to different types of attacks except geometric rotation by more than 2°. In addition, these two methods are robust to JPEG compression with high compression ratio without loss of invisibility. The advantage of using MS-PW method was in treating each layer separately as a grayscale version of the image which deals with a large watermark size (3 times the watermark size using G-PW). (3) The MS-SPW method that is obtained by applying the SPW method to each layer of the RGB image simultaneously. This method considerably improved the watermarked image quality (PSNR improvement reached 5 dB for various test images) when compared with the MS-PW method. The robustness against attacks and JPEG compression is slightly decreased compared with MS-PW. (4) The MS-MPW method that is based on choosing one location at a time (among the three available R, G, and B locations) based on its maximum x coefficient. This method has better PSNR values compared with MS-SPW by 2–3 dB depending on the test image. This improvement in quality asserts on the robustness of the method against some types of attacks. However, MS-MPW method is more robust to JPEG compression than MS-SPW. So, MS-SPW was better than MS-MPW in robustness to attacks while MS-MPW was better in image quality and invisibility.

References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29]

Xie L, Arce GR. A class of authentication digital watermarks for secure multimedia communication. IEEE Trans Image Proc 2001;10(11):1754–64. Zeng W, Lei S. Efficient frequency domain selective scrambling of digital. Video IEEE Trans Multimedia 2003;5(1):118–29. Lin CY, Wu M, Bloom JA. Rotation, scale, and translation resilient watermarking for images. IEEE Trans Image Proc 2001;10(5):767–82. Kutter M, Winkler S, Orched MT. A vision-based masking model for spread spectrum image watermarking. IEEE Trans Image Proc 2002;11(1):16–25. Celik M, Sharma GM, Tekalp A. Lossless watermarking for image authentication: a new framework and an implementation. IEEE Trans Image Proc 2006;15(4):1042–9. Lan T-H, Tewfik A. A novel high-capacity data-embedding system. IEEE Trans Image Proc 2006;15(8):2431–40. Kuribayashi M, Tanaka H. Fingerprinting protocol for images based on additive homomorphic property. IEEE Trans Image Proc 2005;14(12). Ning B, Sun Q, Huang D, Yang Z, Huang J. Robust image watermarking based on multiband wavelets and empirical mode decomposition. IEEE Trans Image Proc 2007;16(8):1956–66. Cox I, Kilian J, Leighton F, Shamoon T. Secure spread spectrum watermarking for multimedia. IEEE Trans Image Proc 1997;6(12):1673–87. Kundur D, Hatzinakos D. Diversity and attack characterization for improved robust watermarking. IEEE Trans Signal Process 2001;49(10):2383–96. Cox J, Miller M, Bloom J. Digital watermarking. Morgan Kaufmann series in multimedia information and system. Academic Press; 2001. Kutter M, Winkler S, Orched MT. A vision-Based masking model for spread spectrum image watermarking. IEEE Trans Image Proc 2002;11(1):1057–7149. Piva A, Barni M, Bartolini F, Cappellini V. DCT-based watermark recovering without resorting to the uncorrupted original image. In: Proc IEEE Int Conf on Image Proc (ICIP), Santa Barbara, CA, vol. 1; 1997. p. 520–3. Piva A, Barni M, Bartolini F, Cappellini V. Mask building for perceptually hiding frequency embedded watermarks. In: Proc 5th IEEE Int Conf Image Processing, Chicago, IL, vol. 1, October 4–7, 1998. p. 450–4. Xia X, Boncelet C, Arce G. A multiresolution watermark for digital images. In: Proc IEEE Int Conf on Image Proc, vol. I; 1997. p. 548–51. Wang Y, Doherty JF, Dyck RE. A wavelet-based watermarking algorithm for ownership verification of digital images. IEEE Trans Image Proc 2002;11(2):77–88. Yuan H, Zhang X. Multiscale fragile watermarking based on the Gaussian mixture model. IEEE Trans Image Proc 2006;15(10):3189–200. Wang SH, Lin YP. Wavelet tree quantization for copyright protection watermarking. IEEE Trans Image Proc 2004;13(2):154–65. Kundur D, Hatzinakos D. Digital watermarking using multi-resolution wavelet decomposition. In: Proc IEEE ICASSP, vol. 5; 1998. p. 2969–72. Al-Otum H, Samara N. Adaptive blind wavelet-based watermarking technique using tree mutual differences. J Electr Image 2006;15(4):043011.1–043011.12. Barni M, Bartolini F, Pive A. Improved wavelet-based watermarking through pixel-wise masking. IEEE Trans Image Proc 2001;5(10):783–91. Thodi D, Rodríguez J. Expansion embedding techniques for reversible watermarking. IEEE Trans Image Proc 2007;16(3):721–30. Kutter K, Jordan F, Bossen F. Digital signatures of color images using amplitude modulation. Proc SPIE 1997;3022:518–26. Piva A, Barni M, Bartolini F, Cappellini V. Exploiting the cross-correlation of RGB channels for robust watermarking of color images. In: Proc IEEEICIP’99, Kobe, Japan, vol. 1; 1999. p. 306–10. Fleet D, Hegger D. Embedding invisible information in color images. In: Proc IEEE-ICIP’97, Santa Barbara, CA; 1997. p. 532–5. Zhao Y, Campisi P, Kundur D. Dual domain watermarking for authentication and compression of cultural heritage images. IEEE Trans Image Proc 2004;13(3):430–48. Lewis AS, Knowles G. Image compression using the 2-D wavelet transfer. IEEE Trans Image Process 1992;1(2):244–50. Kundur D, Hatzinakos D. Digital watermarking using multi-resolution wavelet decomposition. In Proc IEEE ICASSP, vol. 5; 1998. p. 2969–72. Tsai P, Hu Y, Chang C. A color image watermarking scheme based on color quantization. Science Direct Trans Signal Processing 2004;84(1):95–106.