面向多媒体取证的电网频率建模

doi:10.3969/j.issn.0255-8297.2022.03.011

Abstract

Abstract: Electric network frequency (ENF) modeling, which aims to use mathematical models to describe its random fluctuation properties and establish feature extraction, statistical analysis, and trajectory prediction capabilities, has been an important research subtopic in ENF-based digital forensics. In this paper, we first illustrate the limitations of the existing autoregressive (AR) model and then conduct a comprehensive statistical analysis based on practically recorded ENF data from Central China Grid. Specifically, we apply the autoregressive integrated moving average model (ARIMA) and two Markov chain based models respectively in ENF modeling for solving corresponding model parameters. Through the comparative analysis, we reveal that the ARMA(2,4) model is theoretically the best choice for ENF modeling, whereas with the consideration of the frequency resolution limitation in practical situations, the Markov chain model is more suitable to model the estimated ENF from a testing file.

Key words: multimedia forensics, electric network frequency (ENF), autoregressive (AR) model, autoregressive integrated moving average (ARIMA) model, Markov chain

CLC Number:

P751.1

WANG Qingyi, HUA Guang, ZHANG Haijian. Electric Network Frequency Modeling for Multimedia Forensics[J]. Journal of Applied Sciences, 2022, 40(3): 477-492.

References

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Electric Network Frequency Modeling for Multimedia Forensics

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