4.6 Article

Robust Blind Speech Watermarking via FFT-Based Perceptual Vector Norm Modulation With Frame Self-Synchronization

Journal

IEEE ACCESS
Volume 9, Issue -, Pages 9916-9925

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3049525

Keywords

Watermarking; Robustness; Synchronization; Indexes; Discrete wavelet transforms; Protocols; Licenses; Blind speech watermarking; FFT-based perceptual vector norm modulation; downward progressive quantization index modulation; boundary constrained iterative adjustment

Funding

  1. Ministry of Science and Technology, Taiwan, R.O.C. [MOST 108-2221-E-197-013, 109-2221-E-197-024]

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Watermarking is crucial for protecting proprietary digital multimedia data, and the proposed method achieves robust and imperceptible blind speech watermarking using two modules in the fast Fourier transform domain. Experimental results demonstrate the excellent performance of this watermarking method in terms of imperceptibility and robustness.
Watermarking is an important measure for protecting proprietary digital multimedia data. This paper presents a novel approach to achieving robust and imperceptible blind speech watermarking on a frame-by-frame basis. The proposed method employs two modules operating in the fast Fourier transform (FFT) domain. The first module is referred to as downward progressive quantization index modulation. It modulates the vector norms drawn from FFT coefficients according to a guideline deduced from human auditory masking properties. The second module is referred to as boundary-constrained iterative adjustment. It provides a smooth transition across frames in the resulting speech waveform. Experiment results confirm the imperceptibility of the proposed modulation scheme in terms of the mean opinion score - listening quality objective (MOS-LQO) based on the perceptual evaluation of speech quality (PESQ) metric. The proposed watermarking method matched and exceeded the performance of five state-of-the-art methods in terms of robustness against common speech processing attacks.

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