Factors affecting forensic electric network frequency matching – A comprehensive study

Abstract

The power system frequency fluctuations could be captured by digital recordings and extracted to compare with a reference database for forensic timestamp verification. It is known as the Electric Network Frequency (ENF) criterion, enabled by the properties of random fluctuations and intra-grid consistency. In essence, this is a task of matching a short random sequence within a long reference, whose accuracy is mainly concerned with whether this match could be uniquely correct. In this paper, we comprehensively analyze the factors affecting the reliability of ENF matching, including the length of test recording, length of reference, temporal resolution, and Signal-to-Noise Ratio (SNR). For synthetic analysis, we incorporate the first-order AutoRegressive (AR) ENF model and propose an efficient Time-Frequency Domain noisy ENF synthesis method. Then, the reliability analysis schemes for both synthetic and real-world data are respectively proposed. Through a comprehensive study, we quantitatively reveal that while the SNR is an important external factor to determine whether timestamp verification is viable, the length of test recording is the most important inherent factor, followed by the length of reference. However, the temporal resolution has little impact on performance. Finally, a practical workflow of the ENF-based audio timestamp verification system is proposed, incorporating the discovered results.