Speech authentication by semi-fragile speech watermarking utilizing analysis by synthesis and spectral distortion optimization

Yan, Bin; Guo, Yinjing

doi:10.1007/s11042-011-0861-7

Cited by 19 publications

(13 citation statements)

References 29 publications

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“…Linear predictive coding (LPC) or line spectrum pair (LSP) indirectly modifies or quantizes (AR) parameters for embedding the watermark (Gurijala and Deller, 2002;Hatada et al, 2002;Yan and Yin-Jing, 2011;Hofbauer and Kubin, 2006), then watermark data are embedded in the bit stream of the codec, e.g., ACELP (Geiser and vary, 2008), G.729 (Singh et al, 2009), G.711-PCMU (Aoki, 2008) and G.723.1 (Huang et al, 2011), for bypassing the speech compression attacks during or after speech compression.…”

Section: Watermarkingmentioning

confidence: 99%

“…It obtains the distance between two sets of speech signals for embedding the watermark data. This procedure is performed by using some statistical methods to change the variance, the energy, and the mean of the sets (Yan and Yin-Jing, 2011;Hofbauer and Kubin, 2006;Geiser and vary, 2008;Singh et al, 2009). The larger the distance obtained, the easier the watermarking embedding.…”

Section: Watermarkingmentioning

confidence: 99%

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Blind digital speech watermarking based on Eigen-value quantization in DWT

Nematollahi

Al-Haddad

Zarafshan

2015

Journal of King Saud University - Computer and Information Scie

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This paper presents a new blind digital speech watermarking technique based on Eigenvalue quantization in Discrete Wavelet Transform. Initially, each frame of the digital speech was transformed into the wavelet domain by applying Discrete Wavelet Transform. Then, the Eigenvalue of Approximation Coefficients was computed by using Singular Value Decomposition. Finally, the watermark bits were embedded by quantization of the Eigen-value. The experimental results show that this watermarking technique is robust against different attacks such as filtering, additive noise, resampling, and cropping. Applying new robust transforms, adaptive quantization steps and synchronization techniques can be the future trends in this field. ª 2014 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Section: Watermarkingmentioning

confidence: 99%

Section: Watermarkingmentioning

confidence: 99%

Blind digital speech watermarking based on Eigen-value quantization in DWT

Nematollahi

Al-Haddad

Zarafshan

2015

Journal of King Saud University - Computer and Information Scie

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“…Also, the sharpest and second sharpest bandwidths of the speech formants were manipulated to carry the 0 and 1 watermark bits, respectively. In addition, another semi-fragile speech watermarking approach based on quantization of the linear prediction (LP) parameters has been proposed [25]. For this approach, the LP coefficients were converted into inverse sine (IS) coefficients, in which the watermark bits were embedded using QIM.…”

Section: Literature Reviewmentioning

confidence: 99%

Semi-fragile digital speech watermarking for online speaker recognition

Nematollahi

Akhaee

Al-Haddad

et al. 2015

J AUDIO SPEECH MUSIC PROC.

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In this paper, a semi-fragile and blind digital speech watermarking technique for online speaker recognition systems based on the discrete wavelet packet transform (DWPT) and quantization index modulation (QIM) has been proposed that enables embedding of the watermark within an angle of the wavelet's sub-bands. To minimize the degradation effects of the watermark, these sub-bands were selected from frequency ranges where little speaker-specific information was available (500-3500 Hz and 6000-7000 Hz). Experimental results on the TIMIT, MIT, and MOBIO speech databases show that the degradation results for speaker verification and identification are 0.39 and 0.97 %, respectively, which are negligible. In addition, the proposed watermark technique can provide the appropriate fragility required for different signal processing operations.

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“…Chen and Zhu [9] achieved robust watermarking by embedding watermark bits into codebook indices, while applying multistage vector quantization (MSVQ) to the derived LP coefficients. Yan and Guo [10] converted the LP coefficients to reflection coefficients, which were then transformed to inverse sine (IS) parameters. Watermark embedding was achieved by modifying the IS parameters using odd-even modulation [11].…”

Section: Introductionmentioning

confidence: 99%

Effective blind speech watermarking via adaptive mean modulation and package synchronization in DWT domain

Lin

Hsu

2017

J AUDIO SPEECH MUSIC PROC.

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This paper outlines a package synchronization scheme for blind speech watermarking in the discrete wavelet transform (DWT) domain. Following two-level DWT decomposition, watermark bits and synchronization codes are embedded within selected frames in the second-level approximation and detail subbands, respectively. The embedded synchronization code is used for frame alignment and as a location indicator. Tagging voice active frames with sufficient intensity makes it possible to avoid ineffective watermarking during the silence segments commonly associated with speech utterances. We introduce a novel method referred to as adaptive mean modulation (AMM) to perform binary embedding of packaged information. The quantization steps used in mean modulation are recursively derived from previous DWT coefficients. The proposed formulation allows for the direct assignment of embedding strength. Experiment results show that the proposed DWT-AMM is able to preserve speech quality at a level comparable to that of two other DWT-based methods, which also operate at a payload capacity of 200 bits per second. DWT-AMM exhibits superior robustness in terms of bit error rates, as long as the recovery of adaptive quantization steps is secured.

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Speech authentication by semi-fragile speech watermarking utilizing analysis by synthesis and spectral distortion optimization

Cited by 19 publications

References 29 publications

Blind digital speech watermarking based on Eigen-value quantization in DWT

Blind digital speech watermarking based on Eigen-value quantization in DWT

Semi-fragile digital speech watermarking for online speaker recognition

Effective blind speech watermarking via adaptive mean modulation and package synchronization in DWT domain

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