Chaos-based discrete fractional Sine transform domain audio watermarking scheme

Fan, Ming-Quan; Wang, Hongxia

doi:10.1016/j.compeleceng.2008.12.004

Cited by 67 publications

(29 citation statements)

References 12 publications

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“…[1] Evaluates distortion by mean opinion score (MOS), which is a subjective measurment, and achieves transparency between imperceptible and perceptible but not annoying, MOS = 4.7. [4,5] have a low capacity but are robust against most of common attacks. [6] proposes a high bit rate data hiding, but only considers MP3 compression attacks.…”

Section: Resultsmentioning

confidence: 99%

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High capacity audio watermarking using FFT amplitude interpolation

Fallahpour

Megías

2009

IEICE Electron. Express

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An audio watermarking technique in the frequency domain which takes advantage of interpolation is proposed. Interpolated FFT samples are used to generate imperceptible marks. The experimental results show that the suggested method has very high capacity (about 3 kbps), without significant perceptual distortion (ODG about −0.5) and provides robustness against common audio signal processing such as echo, add noise, filtering, resampling and MPEG compression (MP3). Depending on the specific application, the tuning parameters could be selected adaptively to achieve even more capacity and better transparency.

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Section: Resultsmentioning

confidence: 99%

“…Audio watermarking methods exploit the insensitivity of the human auditory system (HAS) in various techniques such as embedding algorithms based on low-bit coding, echo, rational dither modulation [1], patchwork [2], Fourier transform [3,5], wavelet transform [4] or spread spectrum and interpolation [6,7].…”

Section: Introductionmentioning

confidence: 99%

High capacity audio watermarking using FFT amplitude interpolation

Fallahpour

Megías

2009

IEICE Electron. Express

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“…To determine the watermark threshold T, the false alarm and rejection are usually taken into consideration. The performance of a watermarking system is generally characterized by two types of errors (Fan and Wang 2009), the false-positive error and false-negative error. The false-positive error is the probability that an un-watermarked biomedical signal declared as watermarked by the decoder, while false-negative error is the probability that a watermarked biomedical signal declared as un-watermarked by the decoder.…”

Section: Error Analysismentioning

confidence: 99%

Ownership protection of outsourced biomedical time series data based on optimal watermarking scheme in data mining

Pham

Tran

2017

AJIS

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In the biomedical and healthcare fields, the ownership protection of the outsourced data is becoming a challenging issue in sharing the data between data owners and data mining experts to extract hidden knowledge and patterns. Watermarking has been proved as a rightprotection mechanism that provides detectable evidence for the legal ownership of a shared dataset, without compromising its usability under a wide range of data mining for digital data in different formats such as audio, video, image, relational database, text and software. Time series biomedical data such as Electroencephalography (EEG) or Electrocardiography (ECG) is valuable and costly in healthcare, which need to have owner protection when sharing or transmission in data mining application. However, this issue related to kind of data has only been investigated in little previous research as its characteristics and requirements. This paper proposes an optimized watermarking scheme to protect ownership for biomedical and healthcare systems in data mining. To achieve the highest possible robustness without losing watermark transparency, Particle Swarm Optimization (PSO) technique is used to optimize quantization steps to find a suitable one. Experimental results on EEG data show that the proposed scheme provides good imperceptibility and more robust against various signal processing techniques and common attacks such as noise addition, low-pass filtering, and resampling.

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“…However, they are less robust than transform-domain techniques which employ the human perceptual properties and frequency masking characteristics of the human auditory system [13]. Popular transforms that have been widely used in digital watermarking include the discrete Fourier transform (DFT), the discrete cosine transform (DCT), the discrete wavelet transform (DWT), and the singular value decomposition (SVD) [14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

An imperceptible and robust audio watermarking algorithm

Al-Haj

2014

J AUDIO SPEECH MUSIC PROC.

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In this paper, we propose a semi-blind, imperceptible, and robust digital audio watermarking algorithm. The proposed algorithm is based on cascading two well-known transforms: the discrete wavelet transform and the singular value decomposition. The two transforms provide different, but complementary, levels of robustness against watermarking attacks. The uniqueness of the proposed algorithm is twofold: the distributed formation of the wavelet coefficient matrix and the selection of the off-diagonal positions of the singular value matrix for embedding watermark bits. Imperceptibility, robustness, and high data payload of the proposed algorithm are demonstrated using different musical clips.

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Chaos-based discrete fractional Sine transform domain audio watermarking scheme

Cited by 67 publications

References 12 publications

High capacity audio watermarking using FFT amplitude interpolation

High capacity audio watermarking using FFT amplitude interpolation

Ownership protection of outsourced biomedical time series data based on optimal watermarking scheme in data mining

An imperceptible and robust audio watermarking algorithm

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