面向多媒体取证的电网频率建模

电网频率建模旨在对其随机波动特性建立数学模型，形成特征描述、统计分析以及轨迹预测能力，是基于电网频率判据的数字取证中一项重要的研究子课题。该文首先揭示了现有自回归模型的不足，并基于该模型对实际观测得到的电网频率参考信号数据集进行统计分析，分别用差分整合移动平均自回归（autoregressive integrated moving average,ARIMA）模型和两种基于马尔科夫链的模型进行建模，进而求解模型各项参数。对比实验表明： ARMA (2,4)模型在理论上对华中电网频率具备最优的描述性能，而马尔科夫链模型因考虑到实际场景下频率分辨率的局限性，更适用于模拟从待测文件中提取的电网频率。

Electric Network Frequency Modeling for Multimedia Forensics

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.