Audio Recapture Detection With Convolutional Neural Networks

Lin, Xiaodan; Liu, Jingxian; Kang, Xiangui

doi:10.1109/tmm.2016.2571999

Cited by 44 publications

(15 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ENF is appied for audio recapture detection. Lin et al [26] takes ENF spectrogram as the convolutional neural network input for audio recapture detection. 3.…”

Section: Detection Methods Based On Deep Featuresmentioning

confidence: 99%

Shallow and Deep Feature Fusion for Digital Audio Tampering Detection

Zeng¹,

Yang²,

Wang³

et al. 2021

Preprint

View full text Add to dashboard Cite

Digital Audio tampering detection can be applied to verify the authenticity of digital audio. However, the current methods are mostly based on visual comparison analysis of the continuity of electronic network frequency (ENF) of digital audio with a standard ENF database. It is usually tricky to obtain the ENF database, and the feature expression of the visualization method is weak, which leads to low detection accuracy. In order to solve this problem, this paper proposed an audio tampering detection method based on the fusion of shallow and deep features. Firstly, the band-pass filtering process is performed on the audio signal to obtain the ENF components, and then the discrete Fourier transform and Hilbert transform are applied to obtain the phase and instantaneous frequency of the ENF components. Secondly, the shallow features are extracted by performing framing and fitting operations on the estimated phase and instantaneous frequency. Then, the designed convolutional neural network is used to obtain deep features, and the attention mechanism is applied to fuse shallow features and deep features. Finally, after dimensionality reduction through the fully connected layer, the Softmax layer is used for classification to detect the tampering audio. The method achieves 97.03% accuracy on three classic databases, which are Carioca 1, Carioca 2, and New Spanish. In addition, we have achieved an accuracy of 88.31% on the newly constructed database GAUDI-DI. Experimental results show that the proposed method is superior to the state-of-the-art method.

show abstract

“…ENF is appied for audio recapture detection. Lin et al [26] takes ENF spectrogram as the convolutional neural network input for audio recapture detection. 3.…”

Section: Detection Methods Based On Deep Featuresmentioning

confidence: 99%

Shallow and Deep Feature Fusion for Digital Audio Tampering Detection

Zeng¹,

Yang²,

Wang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Since many decades, machine learning and neural network methods have been successfully employed in a wide range of speech and audio processing applications, such as automatic speech recognition (ASR) [38]- [41], audio forensic [42], music information retrieval [43], [44], sound classification [45]. However, their use for the improvement or the new design of multichannel processing localization schemes has been explored only recently [25], [26], [46], [47].…”

Section: B Machine Learning Methods For Multichannel Processingmentioning

confidence: 99%

Exploiting CNNs for Improving Acoustic Source Localization in Noisy and Reverberant Conditions

Salvati

Drioli

Foresti

2018

IEEE Trans. Emerg. Top. Comput. Intell.

View full text Add to dashboard Cite

The paper discusses the application of convolutional neural networks (CNNs) to minimum variance distortionless response (MVDR) localization schemes. We investigate the direction of arrival (DOA) estimation problem in noisy and reverberant conditions using an uniform linear array (ULA). CNNs are used to process the multichannel data from the ULA and to improve the data fusion scheme which is performed in the steered response power (SRP) computation. CNNs improve the incoherent frequency fusion of the narrowband response power by weighting the components, reducing the deleterious effects of those components affected by artifacts due to noise and reverberation. The use of CNNs avoids the necessity of previously encoding the multichannel data into selected acoustic cues with the advantage to exploit its ability in recognizing geometrical pattern similarity. Experiments with both simulated and real acoustic data demonstrate the superior localization performance of the proposed SRP beamformer with respect to other state-ofthe-art techniques.

show abstract

“…CNNs can be exploited in order to learn features emerging from ENF audio recordings. A CNN-based system using spectrograms for audio recapture detection was proposed in [45].…”

Section: Related Workmentioning

confidence: 99%

Blackman–Tukey spectral estimation and electric network frequency matching from power mains and speech recordings

Karantaidis

Kotropoulos

2021

IET Signal Processing

View full text Add to dashboard Cite

Forensic applications exploit electric network frequency (ENF) as a fingerprint to determine multimedia content authenticity, as well as the time and region of multimedia recording. ENF is present at a nominal frequency of 50/60 Hz and its harmonics. Strong interference due to speech content deteriorates ENF estimation accuracy. Herein, the authors propose a non-parametric approach for ENF estimation, which incorporates a customised lag window design into the Blackman-Tukey spectral estimation method. Leakage reduction is formulated as a problem of energy maximisation within the main lobe of the spectral window. The proposed approach is compared to state-of-the-art methods for ENF estimation. Maximum correlation coefficient and minimum standard deviation of errors are employed to measure ENF estimation accuracy. Hypothesis testing is performed to determine whether the improvements in ENF estimation accuracy of the proposed approach over the state-of-the-art methods are statistically significant. Experimental results and statistical tests indicate that the proposed approach improves ENF estimation against many state-of-the-art methods.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Audio Recapture Detection With Convolutional Neural Networks

Cited by 44 publications

References 22 publications

Shallow and Deep Feature Fusion for Digital Audio Tampering Detection

Shallow and Deep Feature Fusion for Digital Audio Tampering Detection

Exploiting CNNs for Improving Acoustic Source Localization in Noisy and Reverberant Conditions

Blackman–Tukey spectral estimation and electric network frequency matching from power mains and speech recordings

Contact Info

Product

Resources

About