A Robust Deep Audio Splicing Detection Method via Singularity Detection Feature
Kanghao Zhang, Shan Liang, Shuai Nie, Shulin He, Jiahui Pan, Xueliang Zhang, Haoxin Ma, Jiangyan Yi
Abstract
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.
BibTeX
@inproceedings{icassp2022_arobustdeepaudio,
title = {A Robust Deep Audio Splicing Detection Method via Singularity Detection Feature},
author = {Kanghao Zhang and Shan Liang and Shuai Nie and Shulin He and Jiahui Pan and Xueliang Zhang and Haoxin Ma and Jiangyan Yi},
booktitle = {ICASSP 2022},
year = {2022}
}