Digital Audio Tampering Detection Based on ENF Consistency

Wang, Zhifeng; Wang, Jing; Zeng, Chunyan; Min, Qiusha; Tian, Yuan; Zuo, Mingzhang

doi:10.1109/icwapr.2018.8521378

Cited by 21 publications

(26 citation statements)

References 8 publications

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“…In this section, we conducted five sets of experiments. In 1. the comparison experiments between the proposed method and the machine learning method, we concluded that the proposed method is better than the method in the literature [16], and the model generalization ability is significantly better than the machine learning method; 2. the validation of the fitting coefficient features, we concluded that for the duration of the audio to be measured is 9 35s, the global information compensation as the deep features In 3. feature matrix validation and 4. deep feature validation experiments, we verify the validity of the deep features proposed in this paper. In 5. attention mechanism validation, we verify the effectiveness of feature selection by attention mechanism in this paper.…”

Section: Discussionmentioning

confidence: 86%

“…The feature fusion with attention is better than the mechanism without attention. The model generalization ability of the proposed method is significantly better than that of the method in [16].…”

Section: Validation Of Deep Featuresmentioning

confidence: 85%

“…3). Electronic network frequency in audio: Electronic network frequency (ENF) is widely used in audio forensics [16]. ENF is the transmission frequency with a nominal value of 50 or 60hz in the power grid.…”

Section: Detection Methods Based On Shallow Featuresmentioning

confidence: 99%

“…The proposed method is compared with the machine learning method. This part compares the shallow and deep feature fusion methods proposed in this paper with those in literature [16], as shown in Fig. 3.…”

Section: 2mentioning

confidence: 99%

“…3. The average value of the change of ENF phase and instantaneous frequency used in the literature [16] was taken as feature F 01f . SVM, random forest, and XGboost machine learning methods were used to implement the F01f experiment.…”

Section: 2mentioning

confidence: 99%

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Shallow and Deep Feature Fusion for Digital Audio Tampering Detection

Zeng¹,

Yang²,

Wang³

et al. 2021

Preprint

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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.

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Section: Discussionmentioning

confidence: 86%

Section: Validation Of Deep Featuresmentioning

confidence: 85%

Section: Detection Methods Based On Shallow Featuresmentioning

confidence: 99%

Section: 2mentioning

confidence: 99%

Section: 2mentioning

confidence: 99%

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Shallow and Deep Feature Fusion for Digital Audio Tampering Detection

Zeng¹,

Yang²,

Wang³

et al. 2021

Preprint

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Electric Network Frequency Based Audio Forensics Using Convolutional Neural Networks

Mao

Xiao

Kang

et al. 2020

Advances in Digital Forensics XVI

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Digital media forensics can exploit the electric network frequency of audio signals to detect tampering. However, current electric network based audio forensic schemes are limited by their inability to obtain concurrent electric network frequency reference datasets from power grids. In addition, most forensic algorithms do not provide high detection precision in adverse signal-to-noise conditions.This chapter proposes an automated electric network frequency based audio forensic scheme that monitors abrupt mutations of tampered frames and discontinuities in the variations of electric network frequency features. Specifically, the scheme utilizes the multiple signal classification, Hilbert linear prediction and Welch algorithms to extract electric network frequency features from audio signals; the extracted features are passed to a convolutional neural network classifier to detect audio tampering. The negative effects of low signal-to-noise ratios on electric network frequency extraction are addressed by employing extra low-rank filtering that removes voice activity and noise interference. Simulation results demonstrate that the proposed scheme provides better audio tampering detection accuracy compared with a benchmark method, especially under adverse signal-to-noise conditions.

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