Wavelet‐domain audio watermarking using optimal modification on low‐frequency amplitude

Hsu, Chih‐Yu; Huang, Hunag-Nan

doi:10.1049/iet-spr.2013.0399

Cited by 29 publications

(16 citation statements)

References 24 publications

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“…Wavelet is a domain of a signal obtained by using a function of ) (x ψ . This function can generate values from the signal samples in the wavelet domain by scaling and shifting related parameters [5]. Signals that have been transformed to the wavelet domain will be divided into two new signals, ie signals at high frequencies and at low frequencies [6].…”

Section: B Wavelet Domainmentioning

confidence: 99%

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High Security Adaptive BCH Code Discrete Wavelet Transform Copyright Protection

Safitri

Ginanjar

Yunawan

2019

Proceedings of the 2018 International Conference on Industrial Enterprise and System Engineering (IcoIESE 2018)

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In this research, we analyze the audio watermarking system by combining adaptive wavelet with SVD in various BCH Codes. The host audio signal is embedded by a watermark signal in the form of an image signal accompanied by several attacks on the watermarking system. The results show that ODG and SNR values for DWT level 1 are higher than level 3, that can reach ODG for BCH code 15,11,1 level 1 = -0.58 and BCH code 15,7,2 level 1 = -0.68 on LSC attack type; SNR for BCH code 15,11,1 level 1 = 32.6 dB and BCH code 15,7,2 level 1 = 31 dB on LPF attack type. While for watermark image BER parameter, the result of the research shows that BER level 1 is worse than level 3, and BCH code 15,7,2 has better BER values than those of BCH code 15,11,1. In our system, we have high security watermark image at BCH 15,7,2 level 3 where the BER value is the lowest among other parameters.

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Section: B Wavelet Domainmentioning

confidence: 99%

“…Some research in audio watermarking has been conducted in various methods, such as time domain method audio watermarking [2], temporal domain [3], DCT domain [4], DWT domain [5][6][7][8], combination of DWT and SVD method [6][7]13], BCH code [9,13], and SVD method [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

High Security Adaptive BCH Code Discrete Wavelet Transform Copyright Protection

Safitri

Ginanjar

Yunawan

2019

Proceedings of the 2018 International Conference on Industrial Enterprise and System Engineering (IcoIESE 2018)

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“…An audio watermarking system usually consists of the embedding and extraction processes and satisfies three minimum requirements of audio watermarking standards set by the International Federation of Phonographic Industry (IFPI) requirements. According to the IFPI recommendation, every audio watermarking algorithm is set to meet the following specifications [1–10]: (i) audio watermark should be imperceptible of the original signal; (ii) signal‐to‐noise ratio (SNR) needs to be >20 dB and the embedding capacity should be >20 bits per second (bps); and (iii) watermark should be capable of resisting common attacks and audio processing operations.…”

Section: Introductionmentioning

confidence: 99%

“…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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“…Their algorithm is demonstrated to be robust against echo and noise addition, filtering, and compression, without significant perceptual distortion. Chen and others have presented an optimization‐based audio watermarking algorithm where embedding is achieved using the seventh level of discrete wavelet transform. The perceptual transparency and robustness of their algorithm have been evaluated at 1,000 bps and 2,000 bps.…”

Section: Introductionmentioning

confidence: 99%

High Embedding Capacity and Robust Audio Watermarking for Secure Transmission Using Tamper Detection

Kaur¹,

Dutta²

2018

ETRI Journal

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Robustness, payload, and imperceptibility of audio watermarking algorithms are contradictory design issues with high‐level security of the watermark. In this study, the major issue in achieving high payload along with adequate robustness against challenging signal‐processing attacks is addressed. Moreover, a security code has been strategically used for secure transmission of data, providing tamper detection at the receiver end. The high watermark payload in this work has been achieved by using the complementary features of third‐level detailed coefficients of discrete wavelet transform where the human auditory system is not sensitive to alterations in the audio signal. To counter the watermark loss under challenging attacks at high payload, Daubechies wavelets that have an orthogonal property and provide smoother frequencies have been used, which can protect the data from loss under signal‐processing attacks. Experimental results indicate that the proposed algorithm has demonstrated adequate robustness against signal processing attacks at 4,884.1 bps. Among the evaluators, 87% have rated the proposed algorithm to be remarkable in terms of transparency.

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Wavelet‐domain audio watermarking using optimal modification on low‐frequency amplitude

Cited by 29 publications

References 24 publications

High Security Adaptive BCH Code Discrete Wavelet Transform Copyright Protection

High Security Adaptive BCH Code Discrete Wavelet Transform Copyright Protection

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

High Embedding Capacity and Robust Audio Watermarking for Secure Transmission Using Tamper Detection

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