A Robust Zero-Watermarking Algorithm for Audio Based on LPCC

Shu-Min Tsai Department of Communication Engineering

National Penghu Technology University Penghu, Taiwan

smtsai@npu.edu.tw

Abstract—The embedding of a digital watermark into digital data protects the owners of information. The development of the Internet has made the problem of unauthorized copying and distributed data potentially devastating to intellectual property rights. A zero-watermarking algorithm for audio based on the correlation of Linear Prediction Cepstrum Coefficients (LPCC) with adaptive factors, is proposed herein. Compared with LPCC watermarking, the proposed algorithm can retrieve the available embedded watermark back after various attacks. Simulation results reveal that the proposed algorithm has greater robustness and security than the LPCC scheme algorithm.

Index Terms—Intellectual Property Right, Information Security, Zero-Watermarking, Robustness, Linear Prediction Cepstrum Coefficients.

I. INTRODUCTION With With the fast development of digital media and the

application of Internet, digital watermarking technique is now playing an increasingly important role in the protection of copyright and the authentication of digital signals. To date, various watermarking schemes have been presented but they may distort slightly the original signal and can not provide a perfect balance between robustness and imperceptibility. The technique of zero-watermark was developed to keep well the robustness and imperceptibility in digital media [1].

Recently, zero-watermarking schemes have been proposed to analyze security for audio signals. [2] proposed a method for mapping the approximate coefficients of the wavelet transform of an audio segment into a binary matrix. A blind zero-watermark algorithm, based on discrete wavelet transform (DWT), has been used to construct secrete keys [3]. [4] used the lifting wavelet transform (LWT) technique on audio aggregation zero-watermark. The technologies of DWT and discrete cosine transformation (DCT) have been combined to generate the watermark sequences [5, 6]. [7] employed an audio zero-watermarking algorithm that combined DCT and Zernike moments. [8] discovered that the properties of entropy on different scales were depicted the audio's statistical features.

The concept of the zero-watermark based on LPCC was proposed to protect audio signals [9, 10]. This paper proposes a new zero-watermarking algorithm that is based on the relationship between LPCC with factors. Simulation results that this method is robust.

This paper proceeds as follows. Section II presents the proposed zero-watermarking scheme in detail. Section III presents experimental results concerning the proposed and existing methods. Finally, the conclusions about this paper are given in Section IV.

II. ZERO-WATERMARKING SCHEME The zero-watermarking scheme consists of three parts,

which are described as follows.

A. Preprocess First, a watermark image I = }1,1),,({ YjXijiI ≤≤≤≤

is used with YX × size. It needs to be converted to an one-dimensional signal V, V }1),({ YXkkv ×≤≤= for the host audio.

Second, a high-pass filter is applied before the embedding process. The high-pass filter eliminates undesired low-frequency components. After the audio signal is filtered through the high-pass filter, it is segmented by a Hamming window into segments YX × .

B. Embedding Process Figure 1 shows the block diagram of the watermarking

embedding scheme. The embedding steps are as follows. • Generate a random permutation which is used as a key

(denoted as random seed) in the watermark detecting process. Then V is scrambled into W. By this way, we can decrease the relationship of pixel space in the watermark image, and improve the security of the system.

• The zero-watering sequences should be perceptually significant features. In this paper, Linear Prediction Cepstrum Coefficients (LPCC) are chosen as the characteristic values of audio signal. Let S be the original audio signal, s(n) is the estimated value by linear prediction, s(n-1) ,..., s(n-p) are the previous audio samples, A1~Ap are the linear prediction coefficients, and p is an order number. Linear prediction function is given by

).(...)2()1()( 21 pnSAnSAnSAnS p −×++−×+−×= (1)

978-1-4673-5936-8/13/$31.00 ©2013 IEEE 63

To obtain the linear prediction coefficients (LPC), we use autocorrelation relationship and Durbin algorithm. Thus Linear Prediction Cepstrum Coefficients can be obtained as follows:

.,)/1(

;1,)/1(

,

1)(

1

1)(

11

pnifCAnmC

pnifCAnmAC

AC

p

mmnmn

n

mmnmnn

≥××−=

≤≤××−+=

=

∑

∑

=−

−

=−

(2)

• A binary pattern E, is defined as },1),({ YXkke ×≤≤=E (3)

Th(k) is the mean of the logarithm characteristic values of every frame.

,/})]([log{)(1 10 pkCkTh p

n n∑ == (4)

where k is a frame index. A new binary bit is obtained by comparing with TH, as follows.

⎩⎨⎧ >

=.,0

),()(,1)(

otherwisekThkCif

kno nn

(5)

The number, )(knotal is obtained by summation the new bits as

.)()()(1∑

==

p

nnntal knokwkno (6)

The factor nw is used to intensify the characteristic, Cn for large than the threshold. The element of the binary pattern E can be obtained by

⎩⎨⎧ ×>

=,,0

),()(,1)(

otherwiseptkno

ke tal (7)

t is set to 70% in this paper.

• The zero-watermarking ZW is generated as follows.

),()()( kwkekzw ⊗= (8) where ⊗ is the XOR operation. The zero-watermarking

image is shown in Figure 2.

Fig. 1. Block diagram of watermarking embedding scheme

Fig. 2. Zero-Watermarking image

C. Detecting Process The watermark can be recovered and Fig. 3 shows the

block diagram of watermarking detecting scheme. The extraction process is as follows.

• The test audio is divided into YX × frames. • Feature values (LPCC) of the test audio are calculated. • The same procedure that is described as an embedding

process in section B is performed to estimate the binary pattern E~ .

• The XOR operstion is applied between E~ and )(kzw , that is,

),()(~)(~ kzwkekw ⊗= (9)

where W~ are sequences of the scrambled image. • The random seed is used to get the extracted watermark

from W~ .

Fig. 3. Block diagram of watermarking detecting scheme

III. SIMULATION RESULTS In computer simulations, a 8888× bits binary image is used

as the watermark, as shown in Fig. 4.

Fig. 4. Watermark image A completed music is used as the original signal. The

detailed information is as follows. • Duration of music: 296s.

Tested Audio

Preprocess Characteristic Values

Encoding

XOR

Recovered Untidy- Picture

Zero-Watermark

Random Seed Extracted

Watermark

Keyes

Original Audio

Preprocess Characteristic Values

Encoding Time Label

XOR

Untidy Picture

Zero-Watermark

Random Seed

Original Picture

(Watermark)

Keyes

64

• Sampling frequency: 44.1k Hz. • Number of bits per sample: 16 bits. • Bit rate: 705k bps. • Number of channels: 1 (mono)

Table 1 presents several methods and tools of attack. • Re-sampling ： The sampling frequency is changed

from 44.1k Hz to 22.05k Hz, and then from 22.05k Hz to 44.1k Hz.

• Re-quantization: The number of bits is reduced from 16 bits to 8 bits, and then increased from 8 bits to 16 bits.

• Low-pass filtering: Tested music is filtered through a low-pass filter with a cut-off frequency of 11k Hz.

• MP3 compression: The signal is compressed in MP3 format.

• Additive white Gaussian noise (AWGN): The signal-to -noise ratios of WGN are 20 dB and 30 dB.

• Random Cropping: The signal is cropped by 10% and 20%.

TABLE 1. ATTACKED METHODS AND TOOLS

ATTACKED METHODS

TOOLS

Resampling 44.1→22.05→44.1 (kHz) GoldWave

Requantization 16→8→16 (bits) GoldWave

Low-pass filter (11 kHz) GoldWave

MP3 compression (24 kbps) Audacity

AWGN (20dB) Audacity

AWGN (30dB) Audacity

Random cropping (10%) GoldWave

Random cropping (20%) GoldWave

The bit error rate (BER), defined in Eq. (10), is used to

estimate the reliability of the schemes.

%,100×=MRBER (10)

where R denotes the number of error bits, and M is the total number of bits. As the order of LPCC is increases, the recovery image becomes more distinct, and the complexity increases [10]. Hence, the watermarking method is highly robust and the trade-off between the time of required for calculation and the quality of the digital watermark must be made. The proposed algorithm provides a resolution. Table 2 summarizes the results of simulation. When order number

10=p is chosen, the proposed algorithm is more reliable than that of [10], and the quality of the recovered image approaches that obtained using the algorithm of with 22=p of [10].

Therefore, the proposed method reduces the computational complexity and provides a robust watermark image.

IV. CONCLUSION We have presented a new algorithm for embedding and

detecting an audio watermark based on LPCC with adaptive factors. Simulation results demonstrate that the proposed zero-watermark scheme performs excellently in terms of security and robustness, and against cropping attacks.

ACKNOWLEDGMENT The author would like to thank the National Science

Council of the Republic of China, Taiwan, for financially supporting this research under Contract No. NSC 101-2221-E-346 -012.

REFERENCES [1] Q. Wen, T. F. Sun, and S. X. Wang, “Concept and application of

zero-watermark,” Acta Electronica Sinica, vol. 31, no. 2, pp.214-216, Feb. 2003.

[2] X. Li, and G. He, ”A new audio zero-watermark algorithm for copyright protection based on audio segmentation and wavelet coefficients mapping,” International Conference on Digital Content, Multimedia Technology and its Applications (IDCTA), pp. 22-26, 2011.

[3] X. Zhong, X. Tang, and H. Yuc, ”Zero-watermark scheme based on audio's statistical character,” International Symposium on Microwave, Antenna, Propagation, and EMC Technologies for Wireless Communications, pp. 1227-1230, 2007.

[4] J. Li, and R. Wang, “Audio aggregation zero-watermark algorithm based on (k, n/k=n),” International Conference on Signal Processing Systems (ICSPS), vol. 3, pp. 644-648, 2010.

[5] R. W. Ciptasari, F. A. Yulianto, A. Fajar, and K. Sakurai, ”An efficient key generation method in audio zero-Watermarking,” International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), pp. 336-339, 2011.

[6] H. L. Dai, and D. He, ”An efficient and robust zero-watermarking scheme for audio based on DWT and DCT,” Asia Pacific Conference on IEEE Microelectronics & Electronics, pp. 233-236, 2009.

[7] Y. Xiong, and R. Wang, ”An audio zero-watermark algorithm combined DCT with zernike moments,” International Conference on Cyberworlds, pp. 11-15, 2008.

[8] G. Chen, and Q. Hu, ”Zero-watermark of audio based on multi-scale entropy vectors,” IEEE International Conference on Computer Science and Information Technology (ICCSIT), pp. 131-135, 2010.

[9] Y. C. Lu, “Cepstral coefficients-based zero-waterwark scheme for digital audio,” Thesis for Master of Science Institute of Communication Engineering Tatung University, 2008.

[10] S. M. Tsai and S. T. Tu, “LPCC zero-watermark for audio signal,” Conference on Electronic Communication and Applications, 2011.

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TABLE 2. SIMULATION RESULTS

ATTACTED

METHODES

LPCC10[a] LPCC16[a] LPCC22[a] Proposed Scheme (LPCC10+factor)

BER watermark BER watermark BER watermark BER watermark

Re-Sampling

44.1→22.05→

44.1(kHz)

17.03%

5.19%

2.80%

5.1%

Re-Quantization

16→8→16

(bits)

10.78%

4.64%

3.58%

7.4%

Low-pass filter

(11 kHz) 15.69

%

4.24%

2.29%

0.8%

MP3 compression

(24 kbps) 18.69

%

6.16%

3.42%

1.0 %

AWGN

20dB 28.72

%

12.13

%

9.47%

1.65%

AWGN

30dB 33.70

%

15.75

%

11.11

%

2.1%

Random cropping

10%

2.66%

1.32%

0.88%

1.35%

Random cropping

20%

4.86%

2.04%

1.30%

1.75%

66