ENF Detection in Audio Recordings via Multi-Harmonic Combining

Liao, Han; Hua, Guang; Zhang, Haijian

doi:10.1109/lsp.2021.3109773

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…The detectors showed a credible performance for short audio clips, enabling reliable ENF detection. In [44], a multi-tone time-frequency detector was developed by utilizing a multiharmonic combination to determine if valid ENF traces were present in a recording as well as to offer information on the overall ENF quality and the number of accessible harmonic components. In [23], a LAD-Likelihood Ratio Test ENF detector was proposed that improved the accuracy and robustness of ENF detection in short-length recordings compared to the state-of-the-art detectors.…”

Section: Enf Detectionmentioning

confidence: 99%

Leveraging Electric Network Frequency Estimation for Audio Authentication

Korgialas,

Kotropoulos,

Plataniotis

2024

IEEE Access

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The Electric Network Frequency (ENF) serves as a simple means to verify the authenticity of audio recordings. ENF variations contain crucial information, acting as a distinctive "fingerprint" when electronic devices are connected or located near power mains. A novel framework for ENF estimation is proposed. This approach alternates between the Least Absolute Deviation (LAD) regression for determining regression weights and objective function minimization with respect to frequency, adapting them within the context of the ℓ 1 norm or the sum of ℓ 1 norms of the approximation error. This framework is a direct consequence of Laplacian distributed noise. Goodness-of-fit tests are reported, indicating that the Laplacian noise hypothesis is more appropriate than the hypothesis of Gaussian noise in the benchmark ENF-WHU dataset. Extensive evaluation using audio recordings from the aforementioned dataset demonstrates the exceptional performance of the proposed framework outperforming state-of-the-art ENF estimation schemes. These findings provide compelling evidence for the efficacy of the proposed ENF estimation schemes as reliable prerequisites for detecting audio forgeries.

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Section: Enf Detectionmentioning

confidence: 99%

Leveraging Electric Network Frequency Estimation for Audio Authentication

Korgialas,

Kotropoulos,

Plataniotis

2024

IEEE Access

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“…Liao et al [64] suggested a multi-tone harmonic combining (MHC) approach that exploits the harmonic components of the ENF to improve the single-tone TF domain ENF detector presented in [64]. Their approach first eliminated contaminated harmonics that may interfere with ENF detection using an enhanced sub-band SNR estimator.…”

Section: Detecting Enf Presence In Digital Multimedia Recordingmentioning

confidence: 99%

ENF Based Digital Multimedia Forensics: Survey, Application, Challenges and Future Work

Ngharamike,

Ang,

Seng

et al. 2023

IEEE Access

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The electric network frequency (ENF) represents the transmission frequency of the electrical grid and fluctuates constantly around 50 Hz or 60 Hz, subject to the region. This constant fluctuation, caused by the continuous mismatch in power demand and supply, makes the ENF a unique signature, which can be utilized for several applications. According to studies, the ENF may be intrinsically implanted in audio recordings captured by digital audio recorders (e.g., microphone-based voice recorder) plugged into the mains supply or are situated close to sources of power and power transmission cable due to electromagnetic field interference originated from power source or acoustic hum and mechanical vibrations emitted by electrically operated devices such as regular household appliances. Recent studies further observed that video recordings made under an illumination source powered by main power can pick up the ENF signal. Following this discovery, several research efforts have been invested towards successful and accurate extraction of the ENF signal, and utilizing the ENF signal retrieved for several applications, including time stamp verification, audio/video authentication, location of recording estimation, power grid identification, estimation of camera read-out time, and video record synchronization. To the best of our knowledge, there has been no comprehensive survey on ENF-based multimedia forensics. Thus, in this paper, we present a comprehensive survey of studies conducted in this field, identifying several application specifics, current challenges, and future research directions.INDEX TERMS Electric network frequency (ENF), ENF detection, ENF estimation, audio and video forensic, ENF applications

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“…Iterative dynamic programming and adaptive trace compensation were employed to identify the frequency components. A multi-tone model for ENF detection applied prior to ENF estimation was presented in [18].…”

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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ENF Detection in Audio Recordings via Multi-Harmonic Combining

Cited by 9 publications

References 14 publications

Leveraging Electric Network Frequency Estimation for Audio Authentication

Leveraging Electric Network Frequency Estimation for Audio Authentication

ENF Based Digital Multimedia Forensics: Survey, Application, Challenges and Future Work

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

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