Elsevier

Expert Systems with Applications

Volume 36, Issue 9, November 2009, Pages 11509-11516
Expert Systems with Applications

A blind watermarking method using maximum wavelet coefficient quantization

https://doi.org/10.1016/j.eswa.2009.03.060Get rights and content

Abstract

This paper proposes a blind watermarking algorithm based on maximum wavelet coefficient quantization for copyright protection. The wavelet coefficients are grouped into different block size and blocks are randomly selected from different subbands. We add different energies to the maximum wavelet coefficient under the constraint that the maximum wavelet coefficient is always maximum in a block. The watermark is embedded the local maximum coefficient which can effectively resist attacks. Also, using the block-based watermarking, we can extract the watermark without using the original image or watermark. Experimental results show that the proposed method is quite robust under either non-geometry or geometry attacks.

Introduction

The rapid growth of the Internet and digital media manifests itself in widespread public forms such as the digital image, the MPEG, and so on, because digital media are easy to copy and transmit. Many researchers are aware of the issues of copyright protection, image authentication, proof of ownership, etc. Hence, there are many solutions that have been proposed. The watermarking technique is one of the solutions. This technique embeds information so that it is not easily perceptible; that is, the viewer cannot see any information embedded in the contents. A watermarking technique is referred to as blind if the original image and watermark are not needed during extraction (Chen et al., 2005, Dugad et al., 1998, Ganic and Eskicioglu, 2004, Inoue et al., 1998, Tsai et al., 2000). There are several important issues in the watermarking system. First, the embedded watermark should not degrade the quality of the image and should be perceptually invisible to maintain its protective secrecy. Second, the watermark must be robust enough to resist common image processing attacks and not be easily removable; only the owner of the image ought to be able to extract the watermark. Third, the blindness is necessary if it is difficult for us to obtain the original image and watermark.

Spread spectrum communication is robust against many types of interference and jamming (Pickholtz, Schilling, & Milstein, 1982). In the following paragraphs, we will briefly review some proposed watermarking approaches which are based on the frequency domain (discrete cosine transform (DCT) and discrete wavelet transform (DWT)). Cox et al., 1996, Cox et al., 1997 suggested inserting the watermark into the perceptually significant portion of the whole DCT-transformed image, wherein a predetermined range of low frequency components excludes the DC component. This watermarking scheme has been shown to be robust against common attacks, such as compression, filtering, cropping, etc. Kwon, Kim, and Park (1999) embedded the watermark in the variable DCT block size. The block size is determined according to the characteristics of the region in the spatial domain. Langelaar and Lagendijk (2001) proposed a blind watermarking approach called differential energy watermarking. A set of several 8 × 8 DCT blocks are composed and divided into two parts to embed a watermark bit. The high frequency DCT coefficients in the JPEG/MPEG stream are selectively discarded to produce energy differences in the two parts of the same set.

Although different watermarking approaches are proposed, the DWT approach remains one of the most effective and easy to implement techniques for image watermarking (Meerwald & Uhl, 2001). The most important issue in DWT-based image watermarking is how to choose the coefficients to be embedded. In Dugad et al., 1998, Kim and Moon, 1999, Kwon et al., 2001, Wang et al., 2000, Wang and Huo, 1997, and Wang, Su, and Kuo (1998), the watermark is embedded the significant coefficient which is selected from the wavelet coefficients. Wang et al. (1998) proposed a watermarking method, according to multi-threshold wavelet coding (MTWC) (Wang & Kuo, 1997); the successive subband quantization (SSQ) was adopted to search for the significant coefficients. The watermark is added by quantizing the significant coefficient in the significant band using different weights. Davoine (2000) proposed two watermarking methods. One is the watermark embedded by modifying the triplets of significant coefficients, according to a sequence of information bits. The other considers rectangular blocks of coefficients, and each block is used to embed one watermark bit.

Some researches embed a watermark using block-based DWT. Huang and Yang (2004) proposed a watermarking algorithm based on the DWT. The original image is separated into m blocks, with each of size n × n; then every block is decomposed into a wavelet domain. The watermark is embedded in the wavelet coefficients in the middle and lower subbands of a block in each image. Khelifi, Bouridane, and Kurugollu (2005) proposed an adaptive blind watermarking technique based on the DWT. The original image is separated into non-overlapping blocks classified as uniform or non-uniform blocks using a JND-based classifier. The watermark is embedded in the high subband of each block which is transformed into the DWT according to its classification. Zhang, Wang, and Wen (2004) divided the original image into n × n blocks and transformed these into a DWT domain. The watermark is embedded by using the mean and the variance of a subband to modify the wavelet coefficient of a block. Hsieh, Tseng, and Huang (2001) proposed a watermarking method based on the qualified significant wavelet tree (QSWT). Wang and Lin (2004) proposed a wavelet-based blind watermarking scheme and each watermark bit is embedded using two trees. One of the two trees is quantized with respect to a quantization index, and both trees exhibit a large statistical difference between the quantized tree and the unquantized tree; the difference can later be used for watermark extraction. Li, Liang, and Niu (2006) improved Wang and Lin’s (2004) method, adding the Human Visual System (HVS) to effectively resist geometric attack. Lien and Lin (2006) improved Wang and Lin’s (2004) method by using four trees to represent two watermark bits in order to improve visual quality. One of the four trees is quantized according to the binary value of the two embedded watermark bits. Wu and Huang (2007) improved Wang and Lin’s method using minimum mean to extract the watermark. Tsai and Lin (2007) proposed a structure based wavelet tree quantization. A super-tree which consists of four wavelet trees is divided into five subblocks. Each subblock is separated into two areas, namely up and low. According to a watermark bit, one of the two areas in each subblock is quantized. But these methods (Li et al., 2006, Lien and Lin, 2006, Tsai and Lin, 2007, Wang and Lin, 2004, Wu and Huang, 2007) cannot effectively resist low-pass filtering such as median filtering or Gaussian filtering.

Previous researches (Dugad et al., 1998, Hsieh et al., 2001, Temi et al., 2005, Wang et al., 2000, Wang et al., 1998, Wang and Huo, 1997) use significant coefficients which are selected from global coefficients are showed that can effectively resist attacks. The problem is that the order of extracting significant coefficients in the extraction process should be exactly the same as those in the embedding process. So, they were unsuitable for the blind watermarking, especially for the attacked watermarked image. For this reason, in this paper, we use the local maximum wavelet coefficient quantization; the wavelet coefficients of a host image are grouped into blocks of variable size. We embed a watermark in different subbands and every block will be used to embed either the watermark bit 0 or bit 1. By adjusting the local maximum wavelet coefficient and maintaining the maximum wavelet coefficient always maximum to present a binary watermark, we embed a watermark bit 1 by increasing the energy of the local maximum wavelet coefficient in a block and embed a watermark bit 0 by decreasing the energy of the local maximum wavelet coefficients in a block. In the extraction process, if we subtract the energy from the local maximum coefficient in a block, and the resulting value is still the maximum in the block, then the block has been embedded with a watermark bit 1; otherwise, the block has been embedded the watermark bit 0. Experimental results show that the proposed method decreases the distortion of the host image and is effectively robust against JPEG compression, low-pass filtering and Gaussian noise, and the PSNR value of a watermarked image is greater than 40 dB.

This paper is organized as follows: In Section 2, we briefly introduce the scan order of wavelet coefficients and the block base significant coefficient. The proposed method is described in Section 3. The experimental results and experimental analysis are given in Section 4. Finally, the conclusions are summed up in Section 5.

Section snippets

The scan order of wavelet coefficients

A host image of size IW by IH is transformed into wavelet coefficients using the L-level discrete wavelet transform (DWT). With an L-level decomposition, we have 3 × L + 1 subbands. The LLL frequency band is found to be unsuitable to be modified since it is a low frequency band which contains important information about an image and easily causes image distortions. Embedding a watermark in the HHL, HHL−1,…, HH1 subbands is also not suitable, since the subbands can easily be eliminated, for example

The preprocess

In the previous section, we have BlocksN blocks in LHL and HLL subbands. In order to decrease the probability that the watermark may be tampered with, each time we must select a block from either Block0,j or Block1,j to embed a watermark bit. We defineE=ej|ej{0,1},1jBlocksN,whereBlockQue=BlockQuej|Blockr,j,r=ej,1jBlocksN.If ej = 0, then Block0,j is selected; otherwise Block1,j is chosen. Hence, the selected blocks form a set of blocks called BlockQue. We shuffle the BlockQue blocks in a

Experimental results

The peak signal-to-noise ratio (PSNR) is used to evaluate the quality between an attacked image and the original image. For the sake of completeness, we list the PSNR formula as follows:PSNR=10×log10255×2551IH×IWx=0IH-1y=0IW-1fx,y-gx,y2dB,where IW and IH are the height and width of the image, respectively. f(x, y) and g(x, y) are the values located at coordinates (x, y) of the original image, and the attacked image, respectively.

After extracting the watermark, the normalized correlation

Conclusion

In this paper, a blind watermarking method based on the DWT using maximum wavelet coefficient quantization is proposed. The proposed method is different from the block-based or wavelet-tree based methods which are discussed in Section 1. In order to achieve the secrecy of embedding watermark, we use variable blocks size and embed a watermark bit using different subbands which are selected randomly from two subbands. As a result, in the proposed method the watermarked images look lossless in

Acknowledgement

This work was partially supported by National Science Council of Taiwan under the Contract Numbers NSC-97-2221-E-129-007 and NSC-97-2221-E-239-022.

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