Digital media forensics can exploit the electric network frequency of audio signals to detect tampering. However, current electric network based audio forensic schemes are limited by their inability to obtain concurrent electric network frequency reference datasets from power grids. In addition, most forensic algorithms do not provide high detection precision in adverse signal-to-noise conditions. This chapter proposes an automated electric network frequency based audio forensic scheme that monitors abrupt mutations of tampered frames and discontinuities in the variations of electric network frequency features. Specifically, the scheme utilizes the multiple signal classification, Hilbert linear prediction and Welch algorithms to extract electric network frequency features from audio signals; the extracted features are passed to a convolutional neural network classifier to detect audio tampering. The negative effects of low signal-to-noise ratios on electric network frequency extraction are addressed by employing extra low-rank filtering that removes voice activity and noise interference. Simulation results demonstrate that the proposed scheme provides better audio tampering detection accuracy compared with a benchmark method, especially under adverse signal-to-noise conditions.