An audio watermarking scheme based on automatic parameterized singular-spectrum analysis using differential evolution

Unoki

Journal of Electrical and Computer Engineering

et al. 2016

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This paper proposes a blind, inaudible, and robust audio watermarking scheme based on singular spectrum analysis (SSA) and the psychoacoustic model 1 (ISO/IEC 11172-3). In this work, SSA is used to analyze the host signals and to extract the singular spectra. A watermark is embedded into the host signals by modifying the singular spectra which are in the convex part of the singular spectrum curve so that this part becomes concave. This modification certainly affects the inaudibility and robustness properties of the watermarking scheme. To satisfy both properties, the modified part of the singular spectrum is determined by a novel parameter selection method based on the psychoacoustic model. The test results showed that the proposed scheme achieves not only inaudibility and robustness but also blindness. In addition, this work showed that the extraction process of a variant of the proposed scheme can extract the watermark without assuming to know the frame positions in advance and without embedding additional synchronization code into the audio content.

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Section: Introductionmentioning

confidence: 92%

Section: Proposed Schemementioning

confidence: 99%

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Audio Watermarking Scheme Based on Singular Spectrum Analysis and Psychoacoustic Model with Self-Synchronization

Unoki

Journal of Electrical and Computer Engineering

et al. 2016

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“…This model was partially-blind because it assumed that the parameters u and l were shared, somehow, between embedding and extraction processes. This model was similar to [28], but it also divided one frame into three equal subsegments and used the majority rule as well. The parameters determined by differential evolution are shown in Table 1.…”

Section: Evaluationsmentioning

confidence: 99%

“…3.1, is music-dependent. Our previous work [28] assumed that parameters u and l are known in the extraction process, and proposed using the second derivative of singular spectrum to automatically estimate them. However, further investigation shows that the proposed method works well only when u is the singularvalue index selected somewhere in the singular spectrum where the slope changes rapidly.…”

Section: Automatic Parameter Estimationmentioning

confidence: 99%

Singular-Spectrum Analysis for Digital Audio Watermarking with Automatic Parameterization and Parameter Estimation

Unoki

IEICE Trans. Inf. & Syst.

et al. 2016

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SUMMARYThis paper proposes a blind, inaudible, robust digitalaudio watermarking scheme based on singular-spectrum analysis, which relates to watermarking techniques based on singular value decomposition. We decompose a host signal into its oscillatory components and modify amplitudes of some of those components with respect to a watermark bit and embedding rule. To improve the sound quality of a watermarked signal and still maintain robustness, differential evolution is introduced to find optimal parameters of the proposed scheme. Test results show that, although a trade-off between inaudibility and robustness still persists, the difference in sound quality between the original and the watermarked one is considerably smaller. This improved scheme is robust against many attacks, such as MP3 and MP4 compression, and band-pass filtering. However, there is a drawback, i.e., some music-dependent parameters need to be shared between embedding and extraction processes. To overcome this drawback, we propose a method for automatic parameter estimation. By incorporating the estimation method into the framework, those parameters need not to be shared, and the test results show that it can blindly decode watermark bits with an accuracy of 99.99%. This paper not only proposes a new technique and scheme but also discusses the singular value and its physical interpretation.

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Digital Audio Watermarking Method Based on Singular Spectrum Analysis with Automatic Parameter Estimation Using a Convolutional Neural Network

Galajit

Recent Advances in Intelligent Information Hiding and Multimedia Signal Processing

et al. 2018