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Temporal derivative-based spectrum and mel-cepstrum audio ...
https://www.academia.edu/4990788/Temporal_derivative_based_spectrum_and_mel_cepstrum_audio_steganalysis#:~:text=In%20comparison%20with%20the%20wavelet-based%20approach%2C%20the%20derivative-based,of%20audio%20steganograms%20with%20a%20certain%20information-hiding%20ratio.
Derivative-Based Audio Steganalysis
https://www.shsu.edu/~qxl005/New/Publications/TOMCCAP_audiosteganalysis.pdf
This article presents a second-order derivative-based audio steganalysis. First, Mel-cepstrum coefficients and Markov transi-tion features from the second-order derivative of the audio signal are extracted; a support vector machine is then applied to the features for discovering the existence of hidden data in digital audio streams.
Derivative-based audio steganalysis | ACM Transactions …
https://dl.acm.org/doi/10.1145/2000486.2000492
This article presents a second-order derivative-based audio steganalysis. First, Mel-cepstrum coefficients and Markov transition features from the second-order derivative of the audio signal are extracted; a support vector machine is then applied to the features for discovering the existence of hidden data in digital audio streams.
(PDF) Derivative-based audio steganalysis
https://www.researchgate.net/publication/220214526_Derivative-based_audio_steganalysis
a scheme of second-order derivative-based audio steganalysis, the details of which are described as follows. An audio signal is denoted f ( t )( t = 0 , 1 , 2 ,..., N − 1).
CiteSeerX — Derivative-Based Audio Steganalysis
https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.473.3660
This article presents a second-order derivative-based audio steganalysis. First, Mel-cepstrum coefficients and Markov transi-tion features from the second-order derivative of the audio signal are extracted; a support vector machine is then applied to the features for discovering the existence of hidden data in digital audio streams.
Derivative-based audio steganalysis, ACM Transactions on ...
https://www.deepdyve.com/lp/association-for-computing-machinery/derivative-based-audio-steganalysis-Fy3Z4JVg50
To improve Mel-cepstrum-based audio steganalysis, we formulate the second-order derivative-based MFCCs and FMFCCs, obtained by replacing the signal f in (12) and (13) with the second-order ¢ Q. Liu et al. derivative D2 ( ¢), the calculation is given by f d mel1 d mel2 Derivative MelCepstrum = FT (MT (FT (D2 ))) = f ... d melM (14) and df mel1 df mel2 Derivative …
Temporal Derivative-Based Spectrum and Mel-Cepstrum …
https://ieeexplore.ieee.org/abstract/document/5071232/
To improve a recently developed mel-cepstrum audio steganalysis method, we present in this paper a method based on Fourier spectrum statistics and mel-cepstrum coefficients, derived from the second-order derivative of the audio signal. Specifically, the statistics of the high-frequency spectrum and the mel-cepstrum coefficients of the second …
Temporal derivative-based spectrum and mel-cepstrum …
https://www.academia.edu/4990788/Temporal_derivative_based_spectrum_and_mel_cepstrum_audio_steganalysis
Results show that our derivative-based and wavelet-based methods are very promising and possess remarkable advantage over Kraetzer and Dittmann's work.The rest of the paper is organized as follows: Section II presents the second-order derivative for audio steganalysis and the Fourier analysis; Section III introduces Kraetzer and Dittmann's mel ...
Temporal Derivative-Based Spectrum and Mel-Cepstrum …
https://www.researchgate.net/publication/224503942_Temporal_Derivative-Based_Spectrum_and_Mel-Cepstrum_Audio_Steganalysis
For audio steganalysis, Liu et al. [13, 14] showed that the residuals based on the second-order derivative operation help to improve the SNR (stego signal to audio content). Mathematically, a ...
Universal audio steganalysis based on calibration and ...
https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-spr.2016.0690
Previous studies have shown that taking the second-order derivative of audio signal improves the performance of audio steganalysis [11, 12, 19]. Therefore, in the proposed method, the second-order derivative of signals is used for feature extraction.
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