Localized &amp; self adaptive audio watermarking algorithm in the wavelet domain

Kaur, Arashdeep; Dutta, Malay Kishore; Soni, Krishan; Taneja, Nidhi

doi:10.1016/j.jisa.2016.12.003

Cited by 16 publications

(12 citation statements)

References 4 publications

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“…On the other hand, the transform domain technique is applied to coefficients obtained as the result of transformation of either a whole audio or the frame of the audio. Some well-known and conventional transform domain techniques are discrete wavelet transform (DWT) [4], discrete cosine transform (DCT) [5], and fast fourier transform (FFT) [6]. Pandey et al [3] presented a method that uses the pseudo-random gray sequence property.…”

Section: Related Researchmentioning

confidence: 99%

“…However, the imperceptibility result of this method is not quite high and robustness result is provided for very few attacks. Kaur et al [4] suggested a method based on a mathematical model by using features such as energy, short time energy, and zero cross means, but robustness against some attacks is quite low. Tsai et al [5] proposed a watermarking method based on energy averaging.…”

Section: Related Researchmentioning

confidence: 99%

“…Simulation results demonstrated that our proposed method is highly robust against numerous attacks. The BER of the proposed method varies from 0 to 6.54, whereas the BER of the recent methods [4][5][6][7][8][9][10][11][12] vary from 0 to 17.76. The PSNR of the proposed method varies from 43.81 to 47.75, whereas PSNR of the recent methods vary from 19.39 to 44.81.…”

Section: Related Researchmentioning

confidence: 99%

“…It is observed that all ODGs of our proposed algorithm range from −0.39 to −0.46, indicating that the original and watermarked audios are perceptual similar. Table 3 shows a comparative analysis between the proposed and several recent methods [4,12] in terms of SNR and MOS. From this comparison, it was observed that our proposed method shows better result in terms of SNR and MOS.…”

Section: Objective Analysismentioning

confidence: 99%

“…Method SNR MOS [4] Energy averaging 41.47 - [5] Localized and self-adaptive algorithm 31.40 3.7 [6] LRS-FFT 44.81 - [7] DCT-SVD-ELO 33.47 4.88 [8] SSA-PM 25.61 - [9] Multifunctional algorithm 23.33 - [10] DCT-SVD-LPT 37.20 4.85 [11] SVD-QIM 19.39 - [12] FS-AE 33.6 -Proposed PSHT-HD 46.56 4.95…”

Section: Referencementioning

confidence: 99%

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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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