An adaptive audio watermarking based on the singular value decomposition in the wavelet domain



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AN ADAPTIVE AUDIO WATERMARKING BASED ON THE SINGULAR VALUE DECOMPOSITION IN THE WAVELET DOMAIN

Abstract

This paper presents a secure, robust, and blind adaptive audio watermarking algorithm based on singular value decomposition (SVD) in the discrete wavelet transform domain using synchronization code. In our algorithm, a watermark is embedded by applying a quantization-index-modulation process on the singular values in the SVD of the wavelet domain blocks. The watermarked signal is perceptually similar to the original audio signal and gives high quality output. Experimental results show that the hidden watermark data is robust to additive noise, re sampling, low-pass filtering, re quantization, MP3 compression, cropping, echo addition, and denoising. Performance analysis of the proposed scheme shows low error probability rates. The data embedding rate of the proposed scheme is 45.9 bps. The proposed scheme has high payload and superior performance against MP3 compression compared to the earlier audio watermarking schemes.



1. INTRODUCTION

Recent advances in Internet and digital multimedia technology have allowed transmission and distribution of digital multimedia (audio, image and video) easily and efficiently to distant places. However, this convenience allows unauthorized copying and distribution of multimedia data. Copyright protection of digital data has become an important issue. Digital watermarking technology has received great deal of attention to solve this problem. Digital watermarking is a process of embedding watermark data into the audio signal. This embedded data can later be detected or extracted from the audio signal for various applications. There are several applications of audio watermarking including copyright protection, copy protection, content authentication, fingerprinting and broadcast monitoring.



2. PROBLEM DEFINITION

Audio watermarking techniques have achieved significant progress, and several good algorithms have been developed. A detailed survey of audio watermarking algorithms. Most of the recent audio watermarking algorithms can be broadly classified into two categories: time-domain algorithms and frequency-domain algorithms. Time-domain algorithms directly insert the watermark into the audio signal, whereas frequency-domain algorithms embed the watermark based on modifying the frequency coefficients. Compared with frequency-domain algorithms, time-domain algorithms are relatively easier to implement and require less computational cost, but they are less robust to some audio signal-processing attacks. Some of the novel and popular audio watermarking algorithms use the patchwork method and spread spectrum techniques.

The main weaknesses of the existing algorithms are as follows: (i) The watermark embedding positions are not selected adaptively according to the characteristics of the audio signals, leading to significant reduction in imperceptibility. (ii) Synchronized code is embedded by modifying individual sample values, thereby reducing the resistance of the synchronized code against signal processing attacks, to a great extent .(iii) Low payload, for example 43 bps (time domain), 22 bps [13] (cepstrum domain), 16 bps (time domain), 10 bps(modified patchwork algorithm (MPA)), 8.5 bps(frequency domain), 5 bps (salient point), 4 bps (time domain), 0.83 bps (Fourier domain), and 0.5–1 bps (spread spectrum). (iv) Low robustness, like vulnerability to cropping.

CONCLUSION

This paper proposes an adaptive audio watermarking algorithm based on SVD in the DWT domain using synchronization code. The watermark data bits are embedded in the SVs of the wavelet blocks of the original audio signals based on QIM. Experiments demonstrate that the watermarked signals are indistinguishable from the original audio signals. Watermark detection is efficient and blind. Only quantization parameters are needed but not the original audio signal. Experimental results show that the proposed scheme is robust against MP3 compression, cropping, low-pass filtering, additive noise, resampling, requantization, echo addition, and denoising.

Our scheme has higher payload and better performance against MP3 compression compared to earlier audio watermarking schemes. The false positive and the false negative error probabilities are very low. The proposed algorithm is suitable for applications like copyright protection, where the embedded data is the information relevant to the owner of the digital audio media. Finally, the proposed algorithm involves only easy calculations and admits easy implementation, and is, therefore, practical for real-time applications.

REFERENCES

[1] I. Cox, M. Miller, J. Bloom, J. Fridrich, T. Kalke, Digital Watermarking and Steganography, second ed., Morgan Kaufmann, Burlington, MA, 2007.

[2] S. Katzenbeisser, F.A.P. Petitcolas, Information Hiding Techniques for Steganography and Digital Watermarking, Artech House, Boston, 2000.

[3] S. Bhattacharya, T. Chattopadhyay, A. Pal, A survey on different video watermarking techniques and comparative analysis with reference to H.264/AVC, in: IEEE 10th International Symposium on Consumer Electronics (ISCE’06), 2006, pp. 1–6.

[4] V.M. Potdar, S. Han, E. Chang, A survey of digital image watermarking techniques, in: 3rd IEEE International Conference on Industrial Informatics (INDIN’05), 2005, pp. 709–716.

[5] M.D. Swanson, B. Zhu, A.H. Tewfik, L. Boney, Robust audio watermarking using perceptual masking, Signal Process. 66 (3) (1998) 337–355.

[6] N. Cvejic, T. Seppanen, Digital Audio Watermarking Techniques and Technologies, Information Science Reference, USA, 2007.

[7] P. Basia, I. Pitas, N. Nikolaidis, Robust audio watermarking in the time domain, IEEE Trans. Multimedia 3 (2) (2001) 232–241.



[8] W.-N. Lie, L.-C. Chang, Robust high-quality time-domain audio watermarking based on low-frequency amplitude modification, IEEE Trans. Multimedia 8 (1) (2006) 46–59.

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