Elsevier

Signal Processing

Volume 90, Issue 2, February 2010, Pages 467-479
Signal Processing

Watermarking via zero assigned filter banks

https://doi.org/10.1016/j.sigpro.2009.07.012Get rights and content

Abstract

In order to identify the owner and distributor of digital data, a watermarking scheme in frequency domain for multimedia files is proposed. The scheme satisfies the imperceptibility and persistence requirements and it is robust against additive noise. It consists of a few stages of wavelet decomposition of several subblocks of the original signal using special zero assigned filter banks. By assigning zeros to filters on the high frequency portion of the spectrum, filter banks with frequency selective response are obtained. The information is then inserted in the wavelet-decomposed and compressed signal. Several robustness tests are performed on male voice, female voice, and music files, color and gray level images. The algorithm is tested under white Gaussian noise and against JPEG compression and it is observed to be robust even when exposed to high levels of corruption.

Introduction

Due to the recent developments in Internet and multimedia services, digital data have become easily attainable through the World Wide Web. Properties such as error-free reproduction, efficient processing and storage, and a uniform format for digital applications, make digital technology popular. However, these advantages may present many complications for the owner of the multimedia data. Unrestricted access to intellectual property and the ease of copying digital files raise the problem of copyright protection.

In order to approve rightful ownership and prevent unauthorized copying and distribution of multimedia data, digital watermarking is employed and imperceptible data are embedded into digital media files. Watermarking makes it possible not only to identify the owner or distributor of digital files but also to track the creation or manipulation of audio, image or video signals. Moreover, by embedding a digital signature, one may provide different access levels to different users. There are several essential conditions that must be met by an effective watermarking algorithm.

  • (i)

    The signature of the author, the watermark, needs to be not only transparent to the user but also robust against attacks [8]. These attacks may include degradations resulting from a transmission channel, compression of the signal, rotation, filtering, permutations or quantization.

  • (ii)

    The watermarking procedure should be invertible. The watermark must be recovered from the marked data preferably without accessing the original signal.

  • (iii)

    The marking procedure must be able to resolve rightful ownership when multiple ownership claims are made. A pirate may modify the marked signal in a way that if his fake original signal is used in detection process, both claimers may gather equal evidence for ownership [13]. This situation is called the deadlock problem [8]. The importance of decoding without the original signal arises here.

  • (iv)

    The author should provide secret keys in order to obtain a more secure encryption technique that allows only the authorized detections of the watermark with the help of proper keys. Even if the exact algorithm is available to a pirate, he should not be able to extract or predict the watermark without accessing the security keys.

Since human auditory system (HAS) and human visual system (HVS) are imperfect detectors, the watermark can be made imperceptible via appropriate masking. In visual masking, watermark signal is usually embedded in the detail bands of the signal, which may make the watermark more fragile against certain attacks such as high frequency filtering. Imperceptibility should be counterbalanced against robustness. Wavelets and filter banks offer a great deal of advantage in terms of these requirements.

Watermarking may be performed in spatial domain or in frequency domain. In this paper we deal with watermarking in frequency domain via wavelets and zero assigned filter banks.

Previous works in frequency domain watermarking are addressed in [4], [8], [20]. Wang et al. discuss the practical requirements for watermarking systems [20]. For standardized algorithms storing watermarks, original or marked signals and secret keys may introduce excessive memory requirements and a great deal of financial burden for registration of all those by the legal authority. A good marking scheme should meet several requirements as explained in detail by Swanson et al. [8]. Embedding data must not violate the perceptual quality of the host signal. The mark should be easily detectable. It is also a desired property that the recovery of data does not use the original signal to decode the embedded watermark. Furthermore it must be robust against modifications and manipulations such as compression, filtering, and additive noise.

One of the early image watermarking techniques using wavelets was suggested by Xia et al. [21] (also see [7]), where a white noise with masking was added on top of the detail portions, i.e., High–Low (HL), Low–High (LH) or High–High (HH) bands of the discrete wavelet transform of the image. Since compression schemes degrade the HH band most, LH or HL band is preferred to obtain robustness against compression. The detection scheme of [21] consisted of computing the correlation of the extracted watermark with the original watermark signal so that one needs to store the embedded watermark and transmit it to the decoder side. Embedded zero-tree wavelets (EZW) have also been employed in watermarking applications for selecting the appropriate detail band coefficients for embedding the watermark [4], [15]. In 1993, Shapiro proposed an efficient low bit rate image coding algorithm based on the self-similarity of wavelet coefficients [14]. He found out that if the coefficients at a coarser scale are insignificant with respect to some amplitude threshold T, then the ones which correspond to the same spatial location at a finer scale are also likely to be insignificant with respect to T. A coefficient at a coarse scale satisfying this self-similarity condition is called the parent and the coefficients corresponding to the same spatial location at finer scales are called its children. Identifying the parents and their children which are insignificant with respect to T, one constructs a zero tree which helps in the detection of the perceptually inconsequential regions and embeds a signature there. Because of the spread spectrum handling of data offered by the multiresolution property of the filter banks, there is an opportunity to increase the robustness while keeping the degradations as small as possible [4]. In [15], in order to facilitate the decoding phase of the watermark, rather than erasing the insignificant coefficients, a nonzero number called the embedded intensity replaces these coefficients. In the decoding phase, the mean of the coefficients which are known to be on the zero tree is computed and the correct embedded bit is determined according to the sign of the mean value. In [6], another method based on the idea of EZW is proposed based on ‘qualified significant coefficients’ that are between two thresholds T1 and T2. In [10], Mıhçak et al. develop an algorithm based on deriving robust semi-global features in wavelet domain and quantizing them. They partition the DC subband into nonoverlapping rectangles and form a series composed of the averages of these rectangles. The watermark embedding is done byquantization of this series. Two different quantization functions are used in order to differentiate between the embedded bits. The authors state that this method is robust against several benchmark attacks and compression.

In [3], Tekalp et al. propose an alternative algorithm to solve the deadlock problem. The authors assume that the number of users of secret files are not many and they can embed a unique watermark into each file composed of a pseudonoise pattern which defines a particular user. Against collusion attacks, the authors propose to apply pre-warping on the host signal. In case of a collusion attack, the method ensures that there will be a perceptual degradation on the signal and the attack will be obvious.

Initial results on the watermarking method given here has been reported in [23], [24]. We then adapted the zero-assigned filter bank method of [23] to image watermarking while making the method more robust against attacks via employment of embedded zero-tree wavelets. While keeping the advantages of the earlier image watermarking techniques, we reduce the amount of key information needed in the detection phase. The proposed scheme embeds the watermark in the perceptually transparent parts of the image by replacing the zero-tree elements by an embedded intensity, +m or -m, which leads to a reduction in the amount of transmitted information. When the watermark is regarded as a noise introduced on the image, peak signal-to-noise ratio (PSNR) is a good measure of imperceptibility and it must be as high as possible. On the other hand, successful detection of the watermark under as low a PSNR as possible is a measure of the robustness of the method against distortion type of attacks. We illustrate in detail under white Gaussian noise and compression attacks that the method we propose improves both PSNR properties in comparison to the earlier techniques proposed in [11], [12], [2].

The outline of this paper is as follows. The design algorithm of perfect reconstruction (PR) zero assigned filter banks is discussed in Section 2. In Section 3, the application of zero assigned filter banks in audio and image watermarking is explained in detail and several experimental results in noise free, noisy and attacked media are presented in Section 4.

Section snippets

Zero assignment

In a PR, quadrature mirror (QM) filter bank, synthesis filters are completely determined by the analysis filters so that the construction of the filter bank reduces to the construction of the analysis filters [17]. The zero assignment in our method refers to the construction of FIR, QM, and minimal length analysis filters having assigned zeros at desired locations with respect to the unit circle (or at desired frequencies) [1]. We now summarize the PR, FIR, QM, and minimal length filter bank

Audio and image watermarking algorithms

In this section a new method for digital watermarking based on zero assigned filter banks is presented. The method proposed in [22] is improved by introducing wavelet decomposition into the watermarking scheme, without increasing the bandwidth requirement. Two filter banks with different assigned zeros around the stop band where each of them designates a bit ‘0’ or ‘1’ are used in computing the wavelet decomposition of the signal and a perceptually insignificant set of coefficients is selected

Experimental results

The algorithms defined in previous section need to be tested against several factors. First of all it must be verified that the techniques satisfy the perceptual transparency condition. It must be determined into which region the zeros can be placed without any significant artifact. Moreover, the effect of the relative positions of the zeros, i.e., the minimum distance between zeros which can be resolved must be detected. The algorithms need to also be tested against signal processing attacks,

Conclusion

We have presented the image and audio watermarking algorithms step by step by indicating the differences in implementation in Section 3 and pointed out the practical details about selections of several parameters such as frame size, decomposition stage number, L, and the threshold for zero tree condition in 4.1 Experimental results in audio watermarking, 4.2 Experimental results in image watermarking. The rest of this section is dedicated to the experimental results in noise free and noisy

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