Detecting splicing in digital audios using local noise level estimation

Xunyu Pan, Xing Zhang, Siwei Lyu

Abstract: One common form of tampering in digital audio signals is known as splicing, where sections from one audio is inserted to another audio. In this paper, we propose an effective splicing detection method for audios. Our method achieves this by detecting abnormal differences in the local noise levels in an audio signal. This estimation of local noise levels is based on an observed property of audio signals that they tend to have kurtosis close to a constant in the band-pass filtered domain. We demonstrate the efficacy and robustness of the proposed method using both synthetic and realistic audio splicing forgeries.