Speech watermarking scheme based on singular-spectrum analysis for tampering detection and identification

Karnjana, Jessada; Galajit, Kasorn; Aimmanee, Pakinee; Wutiwiwatchai, Chai; Unoki, Masashi

doi:10.1109/apsipa.2017.8282027

Cited by 6 publications

(9 citation statements)

References 13 publications

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“…It is shown in our previous work that modifying high-order singular values distorts the host signal less but is sensitive to noise or attacks. Contrarily, modifying low-order singular values can improve robustness but causes sound quality to be poor [9,14]. Thus, there is the trade-off between robustness and sound quality.…”

Section: Singular Value Decomposition (Svd)mentioning

confidence: 99%

“…Sound-quality evaluations: proposed scheme versus other methods. PESQ score LSD (dB) SDR (dB) LSB-based method [21] 4.49 0.19 65.35 CD-based method [7] ∼3.1-4.3 ∼0.6-0.8 -FE-based method [8] ∼3.9 ∼0.4 -SSA-based method (fixed rule) [12] 3.64 0.69 30.96 SSA-based method (with ad hoc parameters) [9] 3 frames from each host signal. Therefore, there were 2400 segments in our training dataset.…”

Section: B) Generating High-quality Datasetmentioning

confidence: 99%

“…Legal issues regarding unauthorized speech-signal modification and tampering have risen in number and are of interest, especially when recorded speech signals contain vital information, for instance, a recorded voice used in court or in a criminal investigation. Speech watermarking can be a possible solution for such issues [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, we proposed a semi-fragile watermarking method based on singular-spectrum analysis (SSA) for detecting tampering in speech signals [9]. A watermark was embedded into a host signal by changing a part of the singular spectrum of the host signal with respect to the watermark bit.…”

Section: Introductionmentioning

confidence: 99%

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Semi-fragile speech watermarking based on singular-spectrum analysis with CNN-based parameter estimation for tampering detection

Galajit

Karnjana

Unoki

et al. 2019

SIP

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A semi-fragile watermarking scheme is proposed in this paper for detecting tampering in speech signals. The scheme can effectively identify whether or not original signals have been tampered with by embedding hidden information into them. It is based on singular-spectrum analysis, where watermark bits are embedded into speech signals by modifying a part of the singular spectrum of a host signal. Convolutional neural network (CNN)-based parameter estimation is deployed to quickly and properly select the part of the singular spectrum to be modified so that it meets inaudibility and robustness requirements. Evaluation results show that CNN-based parameter estimation reduces the computational time of the scheme and also makes the scheme blind, i.e. we require only a watermarked signal in order to extract a hidden watermark. In addition, a semi-fragility property, which allows us to detect tampering in speech signals, is achieved. Moreover, due to the time efficiency of the CNN-based parameter estimation, the proposed scheme can be practically used in real-time applications.

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Section: Singular Value Decomposition (Svd)mentioning

confidence: 99%

Section: B) Generating High-quality Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Semi-fragile speech watermarking based on singular-spectrum analysis with CNN-based parameter estimation for tampering detection

Galajit

Karnjana

Unoki

et al. 2019

SIP

Self Cite

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“…Stability of such features under low data rate compression makes the method effective for semifragile authentication. Karnjana et al [15,16] proposed a scheme based on singularspectrum analysis to detect the acoustic feature-based tampering. Wu and Jay Kuo [17] implemented a fragile speech watermarking based on odd/even modulation and exponential scale quantization.…”

Section: Introductionmentioning

confidence: 99%

Speech Watermarking for Tampering Detection Based on Modifications to LSFs

Chen

Yuan

Wang

et al. 2019

Mathematical Problems in Engineering

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There have been serious issues concerning the protection of speech signals from malicious tampering. Digital watermarking has been paid much attention in solving this problem. This paper proposes a tampering detection approach based on speech watermarking by modifying the line spectral frequencies (LSFs). Watermarks are embedded into LSFs that derived from linear prediction (LP) analysis with dither modulation-quantization index modulation (DM-QIM). Minor modifications to LSFs introduced by quantization not only enable the watermarks to be inaudible to human auditory system but also provide the possibility of robustness against meaningful processing and fragility against tampering. We evaluated the proposed approach with objective evaluations with respect to inaudibility, robustness, and fragility. The results indicated that the proposed approach for tampering detection not only satisfied inaudibility but also provided good robustness against meaningful processing and fragility against malicious tampering.

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Speech watermarking based tamper detection and recovery scheme with high tolerable tamper rate

Wang

Yuan

Zhang

et al. 2023

Multimed Tools Appl

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Speech watermarking scheme based on singular-spectrum analysis for tampering detection and identification

Cited by 6 publications

References 13 publications

Semi-fragile speech watermarking based on singular-spectrum analysis with CNN-based parameter estimation for tampering detection

Semi-fragile speech watermarking based on singular-spectrum analysis with CNN-based parameter estimation for tampering detection

Speech Watermarking for Tampering Detection Based on Modifications to LSFs

Speech watermarking based tamper detection and recovery scheme with high tolerable tamper rate

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