Audio Splicing Detection using Convolutional Neural Network
Shital Jadhav, Rashmika Patole, Priti P. Rege · 2019
In an audio forensics scenario includes audio authentication in which major investigation topic is audio tampering detection. In this paper, we present a novel method of splicing detection using a convolutional neural network. As high-level features of audio are effectively estimated by convolutional neural network, the frequency spectrogram of audio is directly fed as an input to the convolutional neural network. The proposed work uses 10-13 sec of an anechoic audio signal of an audio slice of 1sec, 2sec, and 3sec insertion at the middle of the audio. Results show that the insertion of 3 sec part gives better accuracy than the other two parts. Whereas slice_1 part insertion gives 82.80% accuracy, slice_2 part gives 87.54% accuracy, and slice_3 part gives 96.67% accuracy. Also, the proposed method is robust to the audio compression as well as to the Additive White Gaussian Noise.