Edit Detection in Speech Recordings via Instantaneous Electric Network Frequency Variations

Esquef, Paulo A. A.; Apolinario, J.A.; Biscainho, Luiz W. P.

doi:10.1109/tifs.2014.2363524

Cited by 51 publications

(31 citation statements)

References 15 publications

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“…Most tampering operations cause the ENF to change suddenly at the tampering point. Esquef et al [22] uses Hilbert transform to calculate the instantaneous frequency of ENF and proposes TPSW (two-pass Split Window) method to estimate the change degree ENF background to achieve tampering detection. Rodriguez et al [23,24] detects audio tampering by extracting ENF signals and detecting ENF phase changes' consistency.…”

Section: Detection Methods Based On Shallow Featuresmentioning

confidence: 99%

“…According to the method in literature [22,23], to obtain the phase and instantaneous frequency of ENF, we performed subsampling and bandpass filtering on the audio. Firstly, the subsampling frequency is set as 1000Hz and 1200Hz according to the ENF nominal frequency of 50 or 60Hz.…”

Section: Enf Phase and Frequency Feature Extractionmentioning

confidence: 99%

“…Hilbert transformation [22] was performed on the filtered ENF signal X EN F C [n] to obtain the ENF instantaneous frequency f [n]. So first we get the analytic function of…”

Section: The Hilbert Transform Captures the Instantaneous Frequencymentioning

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: Detection Methods Based On Shallow Featuresmentioning

confidence: 99%

Section: Enf Phase and Frequency Feature Extractionmentioning

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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“…For completeness, tamper detection is discussed. ENF variations can be exploited to facilitate edit detection in multimedia recordings, as proposed in [48]. Phase discontinuities are created due to insertions and deletions.…”

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

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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.

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“…The ENF can also be exploited to detect tampering in multimedia recordings. An edit detection approach taking advantage of the time-varying nature of ENF was proposed in [19]. Multimedia authentication was formulated as a problem of phase change analysis employing the Fourier Transform in [20].…”

Section: Introductionmentioning

confidence: 99%

An Automated Approach for Electric Network Frequency Estimation in Static and Non-Static Digital Video Recordings

Karantaidis

Kotropoulos

2021

J. Imaging

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Electric Network Frequency (ENF) is embedded in multimedia recordings if the recordings are captured with a device connected to power mains or placed near the power mains. It is exploited as a tool for multimedia authentication. ENF fluctuates stochastically around its nominal frequency at 50/60 Hz. In indoor environments, luminance variations captured by video recordings can also be exploited for ENF estimation. However, the various textures and different levels of shadow and luminance hinder ENF estimation in static and non-static video, making it a non-trivial problem. To address this problem, a novel automated approach is proposed for ENF estimation in static and non-static digital video recordings. The proposed approach is based on the exploitation of areas with similar characteristics in each video frame. These areas, called superpixels, have a mean intensity that exceeds a specific threshold. The performance of the proposed approach is tested on various videos of real-life scenarios that resemble surveillance from security cameras. These videos are of escalating difficulty and span recordings from static ones to recordings, which exhibit continuous motion. The maximum correlation coefficient is employed to measure the accuracy of ENF estimation against the ground truth signal. Experimental results show that the proposed approach improves ENF estimation against the state-of-the-art, yielding statistically significant accuracy improvements.

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Edit Detection in Speech Recordings via Instantaneous Electric Network Frequency Variations

Cited by 51 publications

References 15 publications

Shallow and Deep Feature Fusion for Digital Audio Tampering Detection

Shallow and Deep Feature Fusion for Digital Audio Tampering Detection

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

An Automated Approach for Electric Network Frequency Estimation in Static and Non-Static Digital Video Recordings

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