Blind audio watermarking using adaptive quantization and Lifting wavelet transform

Gupta, Abhishek Kumar; Agarwal, Archit; Singh, Arun; Vimal, Divay; Kumar, Dharmendra

doi:10.1109/spin.2018.8474250

Cited by 3 publications

(1 citation statement)

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“…The SNR of this method ranges between 11 dB to 27 dB, which is much lower than the basic requirement, and the bit error rate (BER) against some attacks is also quite high. Gupta et al [16] suggested a watermarking method using lifting wavelet transform (LWT) and adaptive quantization. Although this method is blind, the SNR and normalized correlation (NC) of this method is poor.…”

Section: Related Researchmentioning

confidence: 99%

Blind Audio Watermarking Based on Parametric Slant-Hadamard Transform and Hessenberg Decomposition

2020

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Digital watermarking has been widely utilized for ownership protection of multimedia contents. This paper introduces a blind symmetric audio watermarking algorithm based on parametric Slant-Hadamard transform (PSHT) and Hessenberg decomposition (HD). In our proposed algorithm, at first watermark image is preprocessed to enhance the security. Then, host signal is divided into non-overlapping frames and the samples of each frame are reshaped into a square matrix. Next, PSHT is performed on each square matrix individually and a part of this transformed matrix of size m×m is selected and HD is applied to it. Euclidean normalization is calculated from the 1st column of the Hessenberg matrix, which is further used for embedding and extracting the watermark. Simulation results ensure the imperceptibility of the proposed method for watermarked audios. Moreover, it is demonstrated that the proposed algorithm is highly robust against numerous attacks. Furthermore, comparative analysis substantiates its superiority among other state-of-the-art methods.

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Section: Related Researchmentioning

confidence: 99%