Spread spectrum audio watermarking based on perceptual characteristic aware extraction

Li, Rangkun; Xu, Shenheng; Yang, Huazhong

doi:10.1049/iet-spr.2014.0388

Cited by 29 publications

(8 citation statements)

References 25 publications

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“…Li et al [12] presented a Spread Spectrum (SS)-based watermarking method for audio using Perceptual Characteristic Aware (PCA) extraction. The perceptual analysis technique is used during watermark embedding and extraction stage.…”

Section: ) Transform Domain Audio Watermarking Methodsmentioning

confidence: 99%

A Mean Quantization Watermarking Scheme for Audio Signals Using Singular-Value Decomposition

Das

Lee

2019

IEEE Access

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Recently, research in audio watermarking technology makes it a promising tool for copyright protection of digital audio files in networked environments. In this paper, we propose a new mean quantization based watermarking mechanism for audio based on the technique due to Singular Value Decomposition (SVD). In this method, the host signal is divided into a number of two-dimensional matrix frames. The SVD is then employed to every frame, and also the Euclidean norm of the Singular Values (SVs) are calculated for every frame. The watermark is then hidden into an audio signal by quantization of the norm of the SVs. The watermark is extracted blindly using the inverse process. Both the subjective and objective tests demonstrate good imperceptibility of the watermark in the audio file. Furthermore, the experimental results show good robustness of the proposed scheme against various common audio attacks and Stirmark benchmark attacks. The false-negative error of the method is very near to zero against Stirmark and audio attacks. The proposed scheme has a high payload, and its performance is superior compared to other related watermarking methods for audio signals. Finally, the proposed method achieves better trade-offs between conflicting requirements of imperceptibility, payload, and robustness.INDEX TERMS Mean quantization, singular value decomposition, audio watermarking, security.

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Section: ) Transform Domain Audio Watermarking Methodsmentioning

confidence: 99%

A Mean Quantization Watermarking Scheme for Audio Signals Using Singular-Value Decomposition

Das

Lee

2019

IEEE Access

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“…There is no maximization of the robustness.  PCA extraction procedure Rangkun Li et al [2] wished-for PCA extraction-based watermarking with audio signals is used as an input and WSR is considering as a parameter. There is an optimal performance and the watermarking applications of great potential for practical audio are shown.…”

Section: F Comparison Of Existing Algorithms With Features and Limitmentioning

confidence: 99%

“…Based on time with the working and performance of watermarking algorithms parameters are computed. The watermarks are extracted as well as the number of time requirements is measured successfully [2].…”

Section: Performance Analysismentioning

confidence: 99%

Watermarking Schemes for High Security with Applications and Attacks: Research Challenges and Open Issues

kumar*,

Reddy,

Devadhas

2019

IJRTE

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Recently, the growth of the internet is increased day by day also the digital data such as videos, images and audio availability to the public get increased rapidly. The society required intellectual property protection. To protect the media from other attack intruders and avoid business loss is the requirement of digital media produced. Introducing watermarks can be useful to safeguard copyright. In this review an effort is made to explore various aspects of watermarking, algorithms used, and to carry out a comparative study of these techniques based on their classifications.

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“…Several methods proposed previously involved in inserting watermarks in the time domain [11–21] or frequency domain [3–10, 22–25]. In this study, we focus on the frequency domain especially the wavelet domain.…”

Section: Introductionmentioning

confidence: 99%

Digital audio signal watermarking using minimum‐energy scaling optimisation in the wavelet domain

Hsu

Tu²,

Yang³

et al. 2020

IET signal process.

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This work's contributions include three innovative concepts, an improved model, two‐stage Lagrange principle, and minimum‐energy scaling optimisation, for quantisation audio watermarking in the wavelet domain. First, discrete wavelet transform (DWT) multi‐coefficients quantisation, composed of arbitrary scaling on the lowest DWT coefficients, and the group‐based signal‐to‐noise ratio (SNR) of these coefficients is connected in a model. Then, the two‐stage Lagrange principle and minimum‐energy approach play two essential roles to obtain the optimal scaling factors. With the proposed scheme, the best fidelity and robustness of embedded audio can be attained and the perceptual evaluation of audio quality (PEAQ) test with an illustration of the relationship between SNR and PEAQ is also performed as well. Simulation results show that each watermarked audio by the proposed method attains a high SNR, good PEAQ, and a low bit error rate (BER). The SNR of most watermarked audios in their method is above 35 or even above 40 and the corresponding subjective difference grade of PEAQ is close to 0. In terms of comparing BER, most of their BER is as low as 2% or less indicating better robustness against many attacks, such as re‐sampling, amplitude scaling, and mp3 compression.

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Spread spectrum audio watermarking based on perceptual characteristic aware extraction

Cited by 29 publications

References 25 publications

A Mean Quantization Watermarking Scheme for Audio Signals Using Singular-Value Decomposition

A Mean Quantization Watermarking Scheme for Audio Signals Using Singular-Value Decomposition

Watermarking Schemes for High Security with Applications and Attacks: Research Challenges and Open Issues

Digital audio signal watermarking using minimum‐energy scaling optimisation in the wavelet domain

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