An Improved Digital Watermarking Scheme in the Domain of Vector Quantization

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An Improved Digital Watermarking Scheme in the Domain of Vector

Quantization

Chao-Chin Chang, Chin-Shiuh Shieh, Jeng-Shyang Pan, Bin-Yih Liao, Lin Hong,

and Hung-Jen Lee

Kaohsiung University of Applied Sciences, Taiwan, R.O.C.

csshieh@cc.kuas.edu.tw

Abstract

Digital watermarking had been well recognized as an effective measure for the copyright protection of multimedia data. Numerous schemes in the domain of vector quantization had been proposed to achieve the desired goal. A new scheme, also in the domain of vector quantization, is developed and presented in this article. The proposed approach use genetic algorithms to reassign the indices of code words. As a result, embedded information will be diffused more evenly across the image to be protected, and therefore possible security leakage can be avoided. Experimental results reveal that the proposed scheme is free from the potential limitations in previous approaches, while maintaining the robustness against various kinds of attacks.

1. Introduction

Data secrecy had become an important issue as communication networks getting commoditization and widely spread, especially with the blooming of the Internet. Among others technologies, digital watermarking had received considerable attention in recent years for their theoretical and practical significance.

Aimed at copyright protection, arbitration, and authentication, watermarking is the process of embedding extra information into a media clip. There are three main streams of proposed approaches, namely spatial domain, transform domain, and compression domain. There have been a vast number of established methods [1]. However, it is still far from trivial to make the embedded watermark robust. Various criteria are addressed in judging a watermarking technique, such as perceptibility, security, embedding rate, whether original clip is

required for extraction, robustness to common signal processing or intentional attack, and so on.

In order to reduce the bandwidth requirement for data transmission and space requirement for data storage, various data compression techniques had been developed [2]. Vector quantization (VQ) [3] is a widely adopted approach for lossy data compression. In applications regarding image, audio, and video, human sensory system is sophisticated enough to filter out limited data loose in the process of encoding and decoding. VQ and its descendents try to maintain high compression rate while retaining essential information carried in media clips.

2. Related works

Huang et al. [4] had pioneered a robust digital watermarking algorithm in the domain of vector quantization, from which various variants were developed. To facilitate our discussion, a brief review of their work is given here. In [4], the image X to be protected is an 8-bit gray-level image of sizeM× ,N and the watermark W is a binary image of size

W W N

M × . The X is discomposed into non-overlapping blocks of size

w

w N

N M

M _× _. _x

( )

_m_,_n _is used to denote the block located at

( )

m,n . For a given code book C , the set of indices Y can be obtained by regular search.

( )

X

{

VQ

(

x

( )

m n

)

}

{

y

( )

mn

}

VQ

Y = = , = , (1)

With y

( )

m,n , σ2

( )

m,n is defined to be the variance of index values of those 3× blocks centered3 around

( )

m,n . The polarity set P=

{

p

( )

m,n

}

can then be evaluated according to

(2)

5. Experimental results

A series of experiments was made to verify the feasibility and effectiveness of our approach. Key experiment setting is given below:

Test image: 512∗512 8-bit, gray-level Lena Watermark:128×128 binary Rose

Generation limit of GA: 10,000 generations Population size of GA: 20 chromosomes Crossover rate of GA: 70%

Mutation rate of GA: 0.1% Range of comparison s: 4 Size of neighborhood K: 6

We also implemented the algorithm by Huang et al. [4] for comparative study. Figure 2 reports the watermarks extracted using our scheme under various kinds of attacks. Table 1 summarizes the bit correct rate for the two approaches under consideration. We can see that there is comparable performance between them. We now turn our attention to the possible information leakage. Experiments were made to extract watermark from non-watermarked images, including Baboon, Goldhill, and Peppers. The result is given in Figure 3. It is clear that the potential problem with Huang et al. [4] ceases to exist with our scheme.

6. Conclusions

A new scheme, in the domain of vector quantization, is developed and presented in this article. The proposed approach use genetic algorithms to reassign the indices of code words. As a result, embedded information will be diffused more evenly across the image to be protected, and then possible security leakage can be avoided. Experimental results reveal that the proposed scheme is free from the potential limitations in previous approaches, while maintaining the robustness against various kinds of attacks.

7. References

[1] J.-S. Pan, H.-C. Huang, and L.C. Jain, (Eds.), Intelligent

Watermarking Techniques, World Scientific Publishing

Company, Singapore, 2004.

[2] K. Sayood, Introduction to Data Compression, 2nd Ed., Morgan Kaufmann, 2000.

[3] R.M. Gray, “Vector quantization,” IEEE ASSP Magazine, pp. 4-29, 1984.

[4] H.-C. Huang, F.-H. Wang, and J.-S. Pan, “Efficient and robust watermarking algorithm with vector quantisation,” Electronic Letters, vol. 37, no. 13, pp. 826-828, 2001. [5] Z.M. Lu, D.G. Xu, and S.H. Sun, “Multipurpose image watermarking algorithm based on multistage vector quantization”, IEEE Transactions on Image Processing, vol. 14, no. 6, pp. 822-831, 2005.

[6] D. Charalampidis, “Improved robust VQ-based watermarking,” Electronics Letters, vol. 41, no. 23, pp. 1272-1273, 2005.

[7] D.E. Goldberg, Genetic Algorithms in Search Optimization and Machine Learning, Addison Wesley, 1989.

Acknowledgement

This work is supported by the National Science Council, Taiwan, R.O.C., under the grant No. NSC 94-2213-E-151 -013.

(a) (b) (c)

(d) (e) (f)

Figure 2. Watermarks extracted under various kinds of attacks.

(a) (b) (c)

(d) (e) (f)

Figure 3. Watermarks extracted from non-watermarked images. (a)-(c): Huang et al. [4].

(d)-(f): our scheme.

Table 1. Comparison on bit correct rate

Huang et al. [4] Ours (a) No Attack 1.0 1.0 (b) JPEG 90% 0.9998 0.9993 (c) JPEG 60% 0.9913 0.9559 (d) Crop 25% 0.7600 0.8437 (e) LPF 0.9984 0.9294 (f) MPF 0.9982 0.9581