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

2015 ◽  
Author(s):  
Jessada Karnjana ◽  
Pakinee Aimmanee ◽  
Masashi Unoki ◽  
Chai Wutiwiwatchai
Keyword(s):  
Differential Evolution ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Audio Watermarking ◽  
Singular Spectrum ◽  
Watermarking Scheme

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

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Jessada Karnjana ◽  
Masashi Unoki ◽  
Pakinee Aimmanee ◽  
Chai Wutiwiwatchai
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Extraction Process ◽  
Audio Watermarking ◽  
Psychoacoustic Model ◽  
Singular Spectrum ◽  
Convex Part ◽  
Spectrum Curve ◽  
Watermarking Scheme ◽  
Host Signals

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.

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

2019 ◽  
Vol 8 ◽  
Author(s):  
Kasorn Galajit ◽  
Jessada Karnjana ◽  
Masashi Unoki ◽  
Pakinee Aimmanee
Keyword(s):  
Parameter Estimation ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Computational Time ◽  
Speech Signals ◽  
Hidden Information ◽  
Speech Watermarking ◽  
Singular Spectrum ◽  
Watermarking Scheme ◽  
Host Signal

AbstractA 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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