A Robust Deep Audio Splicing Detection Method via Singularity Detection Feature

There are many methods for detecting forged audio produced by conversion and synthesis. However, as a simpler method of forgery, splicing has not attracted widespread attention. Based on the characteristic that the tampering operation will cause singularities at high-frequency components, we propose a high-frequency singularity detection feature obtained by wavelet transform. The proposed feature can explicitly show the location of the tampering operation on the waveform. Moreover, the long short-term memory (LSTM) is introduced to the CNN-architecture LCNN to ensure that the sequence information can be fully learned. The proposed feature is sent to the improved RNN-architecture LCNN together with the widely used linear frequency cepstral coefficients (LFCC) to learn forgery characteristics where the LFCC is used as a supplement. Systematic evaluation and comparison show that the proposed method has greatly improved the accuracy and generalization.