# Application of Electrical Network Frequency of Digital Recordings for Location-Stamp Verification
## 2 Enf Extraction and Database Formation Based on Location Specific Recordings
### Classification Model

Table 10: Training and Testing Accuracies (%) for 60 Hz and 50 Hz Power and Audio Recordings. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Predicted Labels**} \\ \hline \multicolumn{6}{c}{**A**} & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** & **I** & **N** \\ \hline \multirow{6}{*}{True Labels} & A & 100 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & B & 0 & 100 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & C & 0 & 0 & 100 & 0 & 0 & 0 & 0 & 0 & 0 \\  & D & 0 & 0 & 0 & 100 & 0 & 0 & 0 & 0 & 0 \\  & E & 0 & 0 & 0 & 16.67 & 83.33 & 0 & 0 & 0 & 0 \\  & F & 0 & 0 & 0 & 0 & 83.33 & 0 & 0 & 0 & 16.67 \\  & G & 0 & 0 & 0 & 0 & 0 & 100 & 0 & 0 & 0 \\  & H & 0 & 0 & 0 & 0 & 0 & 16.67 & 83.33 & 0 & 0 \\  & I & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 100 & 0 \\  & N & 20 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 80 \\ \hline \hline \end{tabular} _A, B, C, D, E, F, G, H and I are the 9 grids and \(N\) denotes ‘none of the grids’._

\end{table} 
Table 11: Confusion Matrix Calculated for Power + Audio ENF Testing Data–Accuracies (%). Figure 6: Proposed hierarchical structure for classifying 50 Hz audio data. ## 4 Conclusions

Location forensics analysis is an important tool for security applications in the modern world. Different types of criminal and anti-social activities can be prevented and prosecuted by using location-stamp information of digital recordings. Therefore, novel and reliable location authenticity verification methods to investigate power and multimedia signals are significant. In this article, a novel and efficacious location-stamp verification method based on ENF signals of power and audio recordings captured in different grid locations is presented. The extracted ENF signals are analyzed and decomposed into two segments based on the fluctuation trends from the base ENF values. The decomposed ENF segments are referred to as low outliers frequency (LOF) and high outliers frequency (HOF) signals. Potential feature components are extracted from these segments of the ENF sequences. Then a multi-class SVM classifier is applied using the extracted features to locate the regions of recordings. Custom training and testing algorithms for the SVM classifier are developed in this work. However, the obtained locations are verified with ground truths of the testing samples and the performance assessments affirm the efficiency of the proposed work. [PERSON] extracted the ENF sequences, developed the classification model and revised the manuscript. [PERSON] determined the feature vectors, tested the classification model and documented the manuscript. [PERSON] analyzed the ENF signals and edited the manuscript. [PERSON] conceptualized and supervised the work. This research received no external funding. The authors declare no conflict of interest. ## References

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