Digital Audio Tampering Detection Based on Deep Temporal–Spatial Features of Electrical Network Frequency

Abstract

In recent years, digital audio tampering detection methods by extracting audio electrical network frequency (ENF) features have been widely applied. However, most digital audio tampering detection methods based on ENF have the problems of focusing on spatial features only, without effective representation of temporal features, and do not fully exploit the effective information in the shallow ENF features, which leads to low accuracy of audio tamper detection. Therefore, this paper proposes a new method for digital audio tampering detection based on the deep temporal–spatial feature of ENF. To extract the temporal and spatial features of the ENF, firstly, a highly accurate ENF phase sequence is extracted using the first-order Discrete Fourier Transform (DFT), and secondly, different frame processing methods are used to extract the ENF shallow temporal and spatial features for the temporal and spatial information contained in the ENF phase. To fully exploit the effective information in the shallow ENF features, we construct a parallel RDTCN-CNN network model to extract the deep temporal and spatial information by using the processing ability of Residual Dense Temporal Convolutional Network (RDTCN) and Convolutional Neural Network (CNN) for temporal and spatial information, and use the branch attention mechanism to adaptively assign weights to the deep temporal and spatial features to obtain the temporal–spatial feature with greater representational capacity, and finally, adjudicate whether the audio is tampered with by the MLP network. The experimental results show that the method in this paper outperforms the four baseline methods in terms of accuracy and F1-score.