A Robust Watermarking Technique Resistant JPEG Compression*

JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 27, 163-180 (2011)

A Robust Watermarking Technique Resistant JPEG Compression* CHI-HUNG FAN, HUI-YU HUANG+ AND WEN-HSING HSU Department of Electrical Engineering National Tsing Hua University Hsinchu, 300 Taiwan + Department of Computer Science and Information Engineering National Formosa University Yunlin, 632 Taiwan The increasing popularity of the internet means that digital multimedia are transmitted more rapidly and easily. There is also a greater focus on intellectual property and copyright protection. To prevent piracy and other illegal activities, watermarking is a technique used to protect media data on the internet. An effective watermarking system based on a quantization index modulation (QIM) algorithm in the frequency domain is proposed to achieve copyright protection. The advantages of this approach are that it resists high JPEG compression ratios and maintains good image quality. The procedure consists of two steps: design of a robust embedding mechanism, and embedding and detection of the watermark. In the first step the robust embedding points and values for each image in the JPEG compression are identified. In the second step the QIM algorithm is used to insert a watermark into the image, and then a secure key is recorded. For detection, the embedded watermark is easily extracted using the secure key. Experiments demonstrate that the proposed technique can survive JPEG high compression ratios with good invisibility and robustness. Keywords: digital watermarking, embedding process, extraction process, quantization index modulation (QIM), JPEG compression, copyright protection

1. INTRODUCTION Because of the convenience and speed of the internet, multimedia data can easily be transmitted, duplicated, and reproduced by pirates. Moreover, with the continual expansion of digital multimedia and the internet, the problem of ownership protection of digital information is increasingly important. Copyright protection involves the authentication of data ownership and the identification of illegal behavior such as copying. Techniques are needed to prevent copying, forgery and unauthorized distribution of images and video. Digital watermarking is commonly used to copyright digital data that are freely available on the worldwide web to protect the owner’s rights. Many watermarking schemes have been applied to images, audio, and video streams. Several watermarking concepts have been reported [1-4], highlighting the transparency of data embedding and watermarking for audio, image, and video data. A digital watermark is an identification code that carries information about the copyright owner, the creator of the work, the authorized consumer, etc. It is permanently embedded into the digital data for copyright protection and may be used to check whether the data have been illegally modified [1]. In general, a watermark Received April 6, 2009; revised July 8, 2009; accepted September 3, 2009. Communicated by H. Y. Mark Liao. * The authors would like to thank the anonymous reviewer for valuable suggestions. This work was partly supported by the National Science Council of Taiwan, R.O.C., under Grant No. NSC 94-2213-E-007-075.

163

164

CHI-HUNG FAN, HUI-YU HUANG AND WEN-HSING HSU

must have the following properties: (1) imperceptible, (2) undetectable, (3) reliable, and (4) robust. Two processing domains are used to perform image watermarking: spatial domain [5-7] and the frequency domain [2-4, 8-10]. In the spatial domain, it is easy to insert a watermark into a host image by changing the gray levels of some pixels, but the inserted information can easily be detected using related techniques. On the other hand, most current watermarking schemes focus on the frequency domain rather than the spatial domain because the characteristics of the former are more robust, invisible, and stable. In the frequency domain, a watermark is inserted into coefficients obtained using an image transform process; common transform processes include the discrete Fourier transform (DFT), discrete cosine transform (DCT), and discrete wavelet transform (DWT). Some previous studies are briefly described in the following. Hsu and Wu [2] proposed a DCT-based embedded watermark scheme. A watermark processed using a pseudo-random permutation is embedded into the middle-frequency DCT coefficients within each image block. This technique can survive image processing operations and JPEG lossy compression. Cox et al. [3] proposed a secure (tamper-resistant) algorithm to construct a watermark as an independent and identically distributed Gaussian random vector that is inserted in a spread-spectrum-like fashion into spectral components of the data. The watermark is spread over many frequency coefficients, so that the number of coefficients that are modified is very small and they are difficult to detect. Wu et al. [8] proposed an algorithm to determine the embedding location of a frequency component. In general, low-frequency components exhibit high robustness, but can easily be sensed by some procedures. The authors stated that most recent studies have two problems. The first is that they use fixed information-embedding rules. The second is that they have no mechanisms to allocate appropriate frequency components. Therefore, the authors proposed a method to judge the degree of damage in embedded images. Shih and Wu [9] proposed a combinational image watermarking in which data are inserted into both the spatial and frequency domains. The authors used a new strategy for embedding large watermarks into a host image by splitting the watermark image into the spatial and frequency domains. Suhail and Obaidat [10] proposed a watermarking system based on a DCT transform that uses a pseudo-random sequence of real numbers in the DCT coefficients for each segment of the host image to embed the watermark. Owing to time-consuming computation for the spread-spectrum method, Chen and Wornell [11] proposed quantization index modulation (QIM) and distortion-compensated (DC-QIM) methods for watermark embedding. The QIM algorithm can be used to protect against arbitrary bounded and fully informed attacks and can be further applied to the popular spread-spectrum method. Seo et al. [12] proposed a QIM watermarking scheme for a digital image with two adaptive quantization step-sizes. Use of an angle QIM method to reduce scaling attacks in a watermark embedding system was proposed by Qurique et al. [13]. The watermark is embedded by quantizing the angle formed by a hyperspherical coordinated system. Li and Cox [14] proposed a perceptual model to adaptively select the quantization step size based on calculated perceptual slack to improve the fidelity of QIM watermarking. The authors combined a traditional QIM and rational dither modulation to modify the scaling problem and robustness, except for resistance to valumetric scaling for the QIM

A ROBUST WATERMARKING TECHNIQUE RESISTANT JPEG COMPRESSION

165

algorithm. Other watermarking techniques based on QIM methods have also been proposed [15-17]. Wong et al. [18] used the human visual system (HVS) model to estimate the JPEG-toJPEG data-hiding capacity of JPEG images and the maximum number of bits embedded in JPEG-compressed images. Wong and Au [19] proposed three techniques (single, multiple, and iterative watermark embedding) for embedding watermarks that retain good image quality and are robust to varying degrees to JPEG compression. This algorithm can successively embed watermarks when the quality factor (QF) is low, but the computation complexity of the iterative loop is high for low QF. Although these schemes yield good image quality and robustness, they suffer from either complexity or time-consuming computation. The aim of our study was to design an effective method to embed a digital watermark using QIM to identify robust embedding positions that can resist JEPG compression and maintain good image quality. The remainder of the paper is organized as follows. In section 2, we briefly describe related techniques. Section 3 describes the proposed method, including the identification of embedding points and values and the processes for embedding and detection. Section 4 presents the experimental results and discussion. Finally, conclusions are provided in section 5.

2. RELATED TECHNIQUES In this section we briefly describe techniques related to our proposed method. 2.1 Watermarking-based DCT Model and JPEG Model Our proposed method is based on a DCT transform in the frequency domain and we used the JPEG compression standard. In this subsection we review watermarking based on the DCT domain [10]. Cox et al. [3] and Shih and Wu [9] proposed a scheme based on embedding a pseudorandom sequence of real numbers in the DCT coefficients of each segment of the host image. The host image is first segmented based on a Voronoi diagram and feature extraction. Then a pseudo-random sequence of real numbers is embedded in the DCT domain of each image segment. This enhances the watermark robustness without affecting its invisibility. We used a Voronoi diagram to define a group of segments in the host image based on feature points to be watermarked. There are several ways to generate a Voronoi diagram [20-22]; we used an optimal method known as the plane sweep algorithm [22] in which a horizontal line sweeps over the image from top to bottom and information is obtained on intersection of the structure with the sweep line. The feature extraction used to form the Voronoi cells is based on the algorithm reported by Tommasini et al. [23]. The 2-D forward DCT kernel used in our algorithm can be defined as: g ( x, y, 0, 0) = 1 , N g ( x, y, u , v) = 1 3 [cos(2 x + 1)uπ ][cos(2 y + 1)vπ ], 2N

(1a) (1b)


166

for x, y = 0, 1, …, N − 1, and u, v = 0, 1, …, N − 1 [24]. Here, a watermark consists of a sequence of randomly generated real numbers. These numbers have a normal distribution with zero mean and unity variance expressed as: W = {w1, w2, …, wN}.

(2)

Then the DCT value of the whole image is computed. The DCT coefficients for watermarking are chosen and the watermark is added by modifying the DCT coefficients of each segment: C = {c1, c2, …, cN}.

(3)

Empirical values are determined according to the relation ci′ = ci + αciwi,

(4)

where i = 1, 2, …, N, α is a coefficient for tuning robustness that is set to 0.1, ci is the original DCT coefficient, and wi is the watermark coefficient for the ith block. Assume that the original image is denoted as I0 and the watermarked image is the distorted image I*w. A possibly corrupted watermark W* can then be extracted using the inverse DCT. The 2-D DCT pair is given by: C (0, 0) =

C (u, v) =

1 N

N −1N −1

∑ ∑ f ( x, y),

(5)

x =0 y =0 N −1N −1

1 2N 3

∑ ∑ f ( x, y)[cos(2 x + 1)uπ ] × [cos(2 y + 1)vπ ],

(6)

x =0 y =0

for u, v = 1, 2, …, N − 1, and f ( x, y ) =

1 1 C (0, 0) + N 2N 3

N −1N −1

∑ ∑ f ( x, y)[cos(2 x + 1)uπ ] × [cos(2 y + 1)vπ ],

(7)

x =0 y =0

for x, y = 0, 1, 2, …, N. To check the similarity between the embedded watermark (W) and the extracted watermark (W*), correlation between them can be expressed as:

ρ (W , W * ) =

W ⋅W * W * ⋅W *

,

(8)

where W ⋅ W* is the scalar product of the two vectors. In the proposed algorithm, the entire image is not watermarked, as carried out by Cox et al. [3]. Instead, a watermark is embedded in each segment of the original image. Therefore, each segment is subdivided into areas of 8 × 8 pixels in size. The DCT value of the block is computed and then the DCT coefficients are reordered into a zigzag scan that is similar to JPEG compression [25]. The JPEG standard defines three coding systems. One is the lossy baseline coding


167

system, which is based on the DCT process and is adequate for most compression applications. Then coefficients in the zigzag ordering of the DCT spectrum are selected and modified by computing Eq. (4). The coefficient α is mainly used to tune the watermark energy. The higher the α value, the more robust and visible is the watermark. The watermarked image is then obtained and the modified DCT coefficients are reinserted into the zigzag scan. Then the inverse DCT is applied and these blocks are merged together. Thus, we can obtain a watermarked image Iw after merging all the image segments. 2.2 Quantization Index Modulation We present an adaptive system to generate watermarks that can resist high JPEG compression ratio attacks. The proposed method consists of three steps. First, we identify the embedding point with the best ability to resist high compression ratio attacks. Second, to protect the watermark under JPEG compression, we propose a robust embedding mechanism to identify the best embedding value for each input image. Third, information on embedding points and values for an input image is used to easily and rapidly embed a watermark into the host image. To perform these tasks, we used QIM [11, 18, 19, 26-28] to obtain the relevant information. The QIM method is now briefly described [11, 12]. Chen and Wornell introduced a class of embedded methods referred to as dither modulation that they termed QIM [11]. This method embeds signal-dependent watermarks using a quantization technique. The watermark message represents an index for selection of a particular quantizer from a set of possible quantizers. The selected quantizer is applied to the host data to encode the watermark message. Fig. 1 shows the general watermarkembedding model. The QIM works on a sample-by-sample basis. Assume that one bit is to be embedded so that m ∈ {0, 1}; thus, we require two quantizers Qi(s), i = 0, 1. The embedded value determines the selection of quantizer Q(s) with step size Δ, which is quantized according to: Qi(s) = Q(s − di) + di, i = 0, 1,

(9)

Δ Δ ⎢s⎥ where Q( s ) = Δ ⎢ ⎥ , d0 = − , d1 = . 4 4 ⎣Δ⎦ Based on two quantizers Q0 and Q1, the watermarked signal is expressed as:

⎧Q ( s ) : m = 0 . x=⎨ 0 ⎩Q1 ( s ) : m = 1

(10)

Fig. 1. General watermark-embedding model. A message m is embedded in the host-signal vector x using some embedding function S(x, m).


168

The signal detected is y′ = y + n = (x + w) + n, where n represents the noise introduced by the channel (e.g., an attack) compared to the quantizers and the decoded bit mˆ corresponds to the minimum-distance decoder; the decoder needs to choose the nearest reconstruction point to the signal y′. The decoded message bit is defined as: mˆ = arg min ||y ′ − Qm ( y ′)||.

(11)

m∈(0,1)

The maximum error produced by QIM embedding is Δ/2. If the quantization errors are uniformly distributed over [− Δ/2, Δ/2], the average squared error distortion is Δ2/12. The QIM is independent of image content, and therefore the method may lead to serious degradation of image quality. However, in our system we modify the traditional QIM algorithm and thus maintain good image quality and avoid distortion.

3. PROPOSED METHOD Our proposed method comprises two processes: the identification of robust embedding values and points, and embedding of the watermark into the DCT coefficients for each segment of the host image. We also use a secure key to restore the embedded information. First, we individually analyze each host image and extract robust embedding information, including the robust embedding values and points. This yields an effective embedding message for each host image. This information is integrated and the embedding process is then carried out using an improvement to the traditional QIM method to achieve better performance. Fig. 2 shows a flowchart of the process. The proposed algorithm is described in detail in the following subsection.

Fig. 2. System flowchart.


169

3.1 Robust Embedding Process 3.1.1 Searching for robust embedding points

In general, JPEG compression can cause distortion of information in the host image. In addition, the compression ratio may exceed the limit and the original data will be returned as zero. For both of these situations, embedded watermark information may be lost. To solve this problem, we developed a method to search for robust embedding points [28], which are points that have good ability to resist JPEG compression for each 8 × 8 block in an image. This provides advantageous information for achieving high performance against JPEG compression. The method used to search for and identify embedding points is described as follows. Step 1: Use JPEG compression wit a QF of 1-100 to compress the host image Oi(x, y) corresponding to coordinates (x, y) (to obtain compression zero numbers in the compression process). Step 2: Translate the images to the DCT domain for each 8 × 8 block. If the image size N

N

is N1 × N2, then we have 81 × 82 blocks. Step 3: Calculate the compression zero numbers (denoted CZNumi(x, y), i = 1, 2, …, 100) for each block on JPEG compression for the different QF values. The compression zero number is defined as CZNumi(x, y) = 1, if DCTi (x, y) = 0, i = 1, 2, …, 100.

(12)

Step 4: Calculate the sum of CZNumi(x, y) corresponding to coordinates (x, y) in an 8 × 8 block: Sum of CZNumi ( x, y ) =

∑ CZNumi ( x, y ).

(13)

i, x, y

Step 5: Search the low-frequency positions for each 8 × 8 block. Step 6: Identify the robust embedding point as the point with minimum compression zero N

N

numbers for each 8 × 8 block. If the image size is N1 × N2, then we have 81 × 82 points. i.e., If the image size is 720 × 480, then we obtain 90 × 60 = 5400 robust embedding points. Step 7: Save these points in the secure key. Fig. 3 shows a flowchart of the search for robust embedding points. Next, we use an example to illustrate the above steps, as shown in Fig. 4. First, steps 1-4 are followed for an image of size 8 × 8 to calculate the sum of CZNumi(x, y). In step 5 we search for lowfrequency positions in this 8 × 8 block using a scanning direction similar to the zig-zag ordering illustrated in Fig. 4. Finally, we determine the minimum CZNumi(x, y) located in the low-frequency area. In this example, the robust embedding point is identified at (x, y) = (2, 1), which exhibits the minimum value in the low-frequency area. As we know, the low-frequency region in the DCT domain is remained the important information and

170


Fig. 3. Illustration of the robust embeddingpoints searching.

Fig. 4. The diagram of the robust embedding points searching.

can robust the lossy data compression. However, based on our experiments, the minimum CZNumi(x, y) for each block is almost located in the low-frequency or middle-frequency area. Hence, the embedded position generated the minimum CZNumi(x, y) can robust the compression attacks. For a host image of size 720 × 480, we use this approach to obtain a total of 90 × 60 = 5400 robust embedding points. In the next subsection we describe how to determine the robust embedding values. 3.1.2 Searching for robust embedding values

The JPEG compression ratio can cause serious distortion of the host image and generate errors in the extraction process, because an unsuitable embedding value may result in a failure in data extraction. To solve this problem, we designed a technique to identify robust embedding values and a critical rule to determine effective values among the embedding values identified. The previous steps yield the robust embedding point for each block and each of these robust points has a possibility of being adopted as a watermark embedding point. Then the corresponding embedding values for JPEG compression need to be calculated. In the following subsection we describe how to identify the best tolerance range that provides advantageous information when searching for robust embedding values. Fig. 5 shows a schematic diagram of this search.


171

Fig. 5. Robust embeddingvalues searching.

Step 1: Use JPEG compression with a QF of 1-100 to compress the host image (Oi(x, y)). Step 2: Translate the images to the DCT domain for each 8 × 8 block. If the image size is N

N

N1 × N2, then we have 81 × 82 blocks. Step 3: Calculate the distortion between the original image and compressed image in the DCT domain. If the reconstructed value is greater than the original value, we define this as positive distortion. If the reconstructed value is less than the original value, we define this as negative distortion. Then the maximum positive distortion (MPD(i)) and maximum negative distortion (MND(i)) can be determined. Step 4: Determine the maximum distortion range (MDR(i)), which is the best tolerance range for the embedding point, defined as: MDR(i) = max(|MPD(i)|, |MND(i)|).

(14)

Step 5: Save this range in the secure key. 3.2 Embedding and Extraction Processes 3.2.1 Embedding process

Our proposed method involves an improvement to the traditional QIM algorithm [28]. By combining the quantization distortion capacity and the robust embedding points and values, we propose an effective watermark embedding method that has a flexible embedding range and yields low distortion of the host image. To identify robust positions for watermark embedding, the embedding points (EWP(m, n)) and values (EWV(m, n)) must first be determined corresponding to the coordinates m, n in watermark. According to the previous discussion, robust embedding positions (OEP(p, q)) can be found that have tolerance to high JPEG compression ratios and that correspond to the pth row and qth column in the block. In other words, each OEP(p, q) has a related number of zero coefficients on compression. This information is then used to determine the embedding points (EWP(m, n)). The search rule is that the compression zero number of (EWP(m, n)) must be greater than a threshold value, and the search is carried out from the threshold to 100. In our experiments we set the threshold to 30, so that the search for EWP(m, n) was from 30 to 100. When the number of EWP(m, n) points is equal to the

172


number of watermarks to be embedded, the search is stopped. Each EWP(m, n) has a corresponding EWV(m, n) that can easily be obtained by means of the robust embedded value (OEV(p, q)). Note that our system does not search for EWP(m, n) from 0 to 100, because if the threshold is set to 0 this causes serious distortion of the watermark image. And the detail steps of our proposed embedding process are described as follows [29]. Step 1: Load the EWP((m, n) to identify the embedding point and then read the DCT value of the original host image (Oi(x, y)) corresponding to this point. Step 2: Determine the positive index range PIR(i) and negative index range NIR(i) for the ith point that will be used in the extraction process. This information also serves as the secure key.

PIR(i) = Oi(x, y) + MDR(i), NIR(i) = Oi(x, y) − MDR(i),

(15)

where Oi(x, y) is the DCT value for the original host image corresponding to the embedding point. Step 3: Define the index value. ⎧ 0, if NIR(i ) ≤ Wi ( x, y ) ≤ PIR(i ), Index = ⎨ ⎩1, if NIR(i ) ≥ Wi ( x, y ) or PIR(i ) ≤ Wi ( x, y ),

(16)

where Wi(x, y) is the DCT value of the embedding point for the watermarked image. Step 4: Embed the watermark into the host image. ⎧ Oi( x, y ) + 2 × EWV(i ), if the embedding index is 1, Wi ( x, y ) = ⎨ if the embedding index is 0. ⎩ Oi( x, y ),

(17)

If we want to embed index 0, we do not need to change its value. If we want to embed index 1, we need to change the original DCT value. Step 5: Repeat steps 1-4 until all of the watermarks are embedded. Fig. 6 presents an example to indicate our proposed embedding process. Assume that the embedded point Oi(x, y) = 106, we obtained the MDR(i) = 6 according to the previous process, and calculated PIR(i) = 112 and NIR(i) = 100. If we want to embed index 1, the

Fig. 6. An example of the proposed embedment process.


173

DCT value of the original host image will be changed, such as Wi(x, y) = 118. If we want to embed index 0, we don’t change its value. 3.2.2 Extraction process

Based on the embedding process, the contents of the secure key including all embedding points, maximum distortion range, and positive and negative index ranges can be obtained. In the extraction step, we use these secure keys information to detect the embedded watermark. The detection process can be represented as: ⎧ 0, if NIR(i ) ≤ Wi ( x, y ) ≤ PIR(i ), Index = ⎨ ⎩1, if NIR(i ) ≥ Wi ( x, y ) or PIR(i ) ≤ Wi ( x, y ),

(18)

where Wi(x, y) denotes the DCT value of the embedded point for the watermarked image.

4. EXPERIMENTAL RESULTS AND DISCUSSION In our experiments, test images of size 720 × 480 were used as host images, as shown in Fig. 7. To est for general conditions, we choose a watermark with nearly equal numbers of black and white dots. Permutation of the watermark of size 30 × 20 was carried out using a pseudo-random generator to disperse the spatial relationship, as shown in Fig. 8. Our experimental results are compared to those for the methods of Hsu and Wu [2] and Wu et al. [8], which exhibit high resistance to JPEG compression and good watermark performance in the DCT domain.

(a) Lena.

(b) Mandrill. Fig. 7. Testing images with size 720 × 480.

(c) Pepper.

Fig. 8. The watermark.

4.1 Performance Estimation

Robustness is an important factor in watermarking. A measure of the similarity (NC)


174

between the extracted and reference watermarks was used to evaluate the performance according to: m

n

∑∑w(i, j ) × wˆ (i, j ) NC =

i =0 j =0 m n

∑∑[ w(i, j )]

(19)

,

2

i =0 j =0

where w(i, j) and wˆ (i, j ) denote the original and extracted watermarks corresponding to the coordinates (i, j), respectively. Invisibility is another important factor in watermarking. We use the peak signal-tonoise ratio (PSNR) to indicate the degree of transparency as a measure of invisibility. The PSNR and mean square error (MSE) value are expressed as: 2552 , MSE

PSNR = 10 × log MSE =

1 H ×W

H W

∑∑ |S1 (i, j ) − S2 (i, j )|2 , j

(20)

i

where S1(i, j) and S2(i, j) are the grayscale values at (i, j) for the corrupted image S1 and the host image S2, and H and W denote the height and width of the image, respectively. 4.2 Results

For JPEG attacks, Figs. 9 (a) to 11 (a) show PSNR values for the embedded image for different JPEG compression ratios. The results for extracted watermarks at JPEG compression of QF = 90, 70, 50, and 30 are illustrated in Figs. 9 (b)-(e) to 11 (b)-(e) and Table 1.

(a) Embedded watermark image which PSNR = 45.4320dB.

(b) QF = 90, NC = 0.9983.

(c) QF = 70, NC = 0.9933. (d) QF = 50, NC = 0.9916. (e) QF = 30, NC = 0.9733. Fig. 9. Compression ratio and NC value for Lena image.



175

(b) QF = 90, NC = 1.0000.

(c) QF = 70, NC = 0.9900. (d) QF = 50, NC = 0.9933. (e) QF = 30, NC = 0.9883. Fig. 10. Compression ratio and NC value for Mandrill image.


(b) QF = 90, NC = 0.9983.

(c) QF = 70, NC = 0.9900. (d) QF = 50, NC = 0.9860. (e) QF = 30, NC = 0.9560. Fig. 11. Compression ratio and NC value for Pepper image.

Table 1. The NC values of the proposed method for the different quality factors (QF). Testing Image

10 Lena 0.89 Mandrill 0.95 Pepper 0.868

QF 20 0.956 0.97 0.955

30 0.973 0.988 0.957

40 0.98 0.997 0.975

50 0.991 0.993 0.987

60 0.99 0.997 0.988

70 0.993 0.99 0.99

80 0.996 0.998 0.996

90 0.998 1 0.998

100 1 1 1

The results demonstrate that even when the quality factor decreases, the NC value remains greater than 0.8. The robustness of our watermarking system is compared to that of Hsu and Wu and Wu et al. in Figs. 12-14. From these results it is evident that NC values for the proposed method under different JPEG compression ratios exhibit superior performance to

176


Fig. 12. All NC values of the extracted watermark under the different JPEG compression on Lena image.

Fig. 13. All NC values of the extracted watermark under the different JPEG compression on Mandrill image.

Fig. 14. All NC values of the extracted watermark under the different JPEG compression on Pepper image.


177

that of Wu et al. and Hsu and Wu. Hence, our approach effectively improves the robustness of digital watermarking against JPEG compression to achieve copyright protection, with robustness against JPEG compression up to a compression ratio of 33. 4.3 Discussion

The results presented demonstrate that NC values for our proposed system remain stable for different quality factors. Watermarked images retained the original watermark information to protect owner copyright. Figs. 12-14 show that the robustness of the proposed system is superior to that for the methods of Wu et al. and Hsu and Wu. Moreover, in terms of invisibility the proposed approach maintains good image quality under high compression ratios.

5. CONCLUSIONS We have proposed an approach for embedding a watermark into a still image to resist high JPEG compression based on the QIM method. This approach uses a robust embedding mechanism in which robust embedding points and values are identified for the embedding and detection processes. The former procedure effectively searches for robust embedding points and values in each image. This provides an important reference factor that yields good performance on JPEG compression. Using the robust embedding points and values, a watermark can be effectively inserted. However, one problem with the system is that the experimental result is restrained by the embedding capacity because robust embedding points are restricted when the size of the embedded watermark exceeds a specified boundary. Progress in modifying this limitation and resisting other prototype attacks will be the major focus of future work.

REFERENCES 1. C. Busch, W. Funk, and S. Wolthusen, “Digital watermarking: from concepts to realtime video applications,” IEEE Computer Graphics and Applications, Vol. 19, 1999, pp. 25-35. 2. C. T. Hsu and J. L. Wu, “Hidden digital watermarks in images,” IEEE Transactions on Image Processing, Vol. 8, 1999, pp. 58-68. 3. I. Cox, J. Kilian, F. T. Leighton, and T. Shamoon, “Secure spread spectrum watermarking for multimedia,” IEEE Transactions on Image Processing, Vol. 6, 1997, pp. 1673-1687. 4. P. H. W. Wong and O. C. Au, “A blind watermarking technique in JPEG compressed domain,” in Proceedings of IEEE International Conference on Image Processing, 2002, pp. 497-500. 5. R. Lancini, F. Mapelli, and S. Tubaro, “A robust video watermarking technique in the spatial domain,” in Proceedings of IEEE International Symposium on Video/Image Processing and Multimedia Communications, 2002, pp. 251-256. 6. P. V. K. Borges and J. Mayer, “Analysis of position based watermarking,” Pattern Analysis and Applications, Vol. 9, 2006, pp. 70-82.

178


7. H. Y. Huang, Y. R. Lin, and W. H. Hsu, “Robust technique for watermark embedding in a video stream based on block matching algorithm,” Optical Engineering, Vol. 47, 2008, pp. 037402-1-037402-14. 8. G. Z. Wu, Y. Y. Wang, and W. H. Hsu, “Robust watermark embedding/detection algorithm for H.264 video,” Jounal of Electronic Imaging, Vol. 14, 2005, pp. 13013-013013-9. 9. F. Y. Shih and S. Y. T. Wu, “Combinational image watermarking in the spatial and frequency domains,” Pattern Recognition, Vol. 36, 2003, pp. 969-975. 10. M. A. Suhail and M. S. Obaidat, “Digital watermarking-based DCT and JPEG model,” IEEE Transactions on Instrucmentation and Measurement, Vol. 52, 2003, pp. 16401647. 11. B. Chen and G. Wornell, “Quantization index modulation: A class of provably good methods for digital watermarking and information embedding,” IEEE Transactions on Information Theory, Vol. 47, 2001, pp. 1423-1443. 12. Y. S. Seo, W. G. Kim, Y. H. Suh, W. G. Oh, and C. J. Hwang, “QIM watermarking for image with two adaptive quantization step-sizes,” in Proceedings of the 9th International Conference on Advanced Communication Technology, 2007, pp. 997800. 13. F. Qurique, V. Licks, R. Jordan, and F. Perez-Gonzalez, “Angle QIM: A novel watermark embedding scheme robust against amplitude scaling distortions,” in Proceedings of IEEE International Conference on Accustics, Speech, and Signal Processing, 2005, pp. 797-800. 14. Q. Li and I. J. Cox, “Using perceptual models to improve fidelity and provide resistance to valumetric scaling for quantization index modulation watermarking,” IEEE Transactions on Information Forensices and Security, Vol. 2, 2007, pp. 127-139. 15. F. Deguillaume, S. Voloshynovskiy, and T. Pun, “Secure hybirid robust watermarking resistant against tampering and copy attack,” Signal Processing, Vol. 83, 2003, pp. 2133-2170. 16. T. Liu and P. Moulin, “Error exponents for one-bit watermarking,” in Proceedings of IEEE International Conference on Accustics, Speech, and Signal Processing, 2003, pp. 65-68. 17. P. Moulin and A. Briassouli, “A stochastic QIM algorithm for robust, undetectable image watermarking,” in Proceedings of IEEE International Conference on Image Processing, 2004, pp. 1173-1176. 18. P. H. W. Wong, A. Chang, and O. C. Au, “On improving the iterative watermark embedding technique for JPEG-to-JPEG watermarking,” in Proceedings of IEEE International Symposium on Circuits and Systems, 2004, pp. 161-164. 19. P. H. W. Wong and O. C. Au, “A capacity estimation technique for JPEG-to-JPEG image watermarking,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 13, 2003, pp. 746-752. 20. M. Berg, M. Kreveld, M. Overmars, and O. Schwarzkopf, Computational Geometry: Algorithms and Applications, 2nd ed., Springer-Verlag, New York, 2000. 21. R. Klein, Concrete and Abstract: Voronoi Diagrams, Lecture Notes in Computer Science, Vol. 400, Springer-Verlag, New York, 1989. 22. W. Guan and S. Ma, “A list-processing approach to compute Voronoi diagrams and the Euclidean distance transform,” IEEE Transactions on Pattern Analysis and Ma-


179

chine Intelligence, Vol. 20, 1998, pp. 757-761. 23. T. Tommasini, A. Fusiello, E. Trucco, and V. Roberto, “Marking good features track better,” in Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, 1998, pp. 178-183. 24. P. Wolfgang and E. Delp, “A watermark for digital images,” in Proceedings of IEEE International Conference on Image Processing, 1996, pp. 219-222. 25. R. C. Gonzales and R. E. Woods, Digital Image Processing, 2nd ed., Prentice Hall, New Jersey, 2002. 26. J. Eggers and B. Girod, Informed Watermarking, Kluwer Academic, Norwell, Massachusetts, 2002. 27. P. H. W. Wong, O. C. Au, and Y. M. Yeung, “Novel blind multiple watermarking technique for images,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 13, 2003, pp. 813-830. 28. H. Y. Huang, C. H. Fan, and W. H. Hsu, “An effective watermark embedding algorithm for high JPEG compression,” in Proceedings of International Conference on Machine Vision Applications, 2007, pp. 256-259. 29. C. H. Fan, “Optimal embedded positions in watermarking system for resisting high JPEG compression,” Master Thesis, Department of Electrical Engineering, National Tsing Hua University, 2006.

Chi-Hung Fan (范志鴻) received the B.S. degree in Electrical Engineering from National Chung Hsing University, Taiwan, in 2004, and the M.S. degree in Electrical Engineering from National Tsing Hua University, Taiwan, in 2006. His research interests include image/video coding, VLSI design, and signal processing.

Hui-Yu Huang (黃惠俞) received the B.S. degree in Electronic Engineering from Feng Chia University, Taiwan, in 1992, and the M.S. degree in Electrical and Computer Engineering from Yuan Ze University, and the Ph.D. degree in Electrical Engineering from National Tsing Hua University, Taiwan, in 1994 and 2002, respectively. Since 2005, she has been with the Department of Computer Science and Information Engineering at National Formosa University in Taiwan, where she is now an Assistant Professor. Her research interests include multimedia processing, neural networks, pattern recognition, and content-based image/video retrieval. Dr. Huang is an member of IEEE, the Chinese Association of Image Processing and Pattern Recognition.

180


Wen-Hsing Hsu (許文星) received the B.S. degree in Electrical Engineering from National Cheng Kung University, Taiwan, in 1972, and the M.E. and Ph.D. degrees in Electrical Engineering from Keio University, Japan, in 1978 and 1982, respectively. In 1982, he joined the Department of Electrical Engineering at National Tsing Hua University in Taiwan, where he is now a Professor. His research interests include image processing, biologic identification and network security. Dr. Hsu is a member of the Institute of Electronics, Information and Communication Engineers (IEICE), the Information Processing Society of Japan, and the Chinese Association of Image Processing and Pattern Recognition.