Supervised audio tampering detection using an autoregressive model

Splicing, cutting and insertion are the most common operations imposed on audio files when the adversary intends to modify or fabricate the content. The detection of such kinds of tampering is still challenging in real-world applications. In this paper, a generic approach for the detection of audio tampering is proposed via the analysis of electric network frequency (ENF). Based on the fact that tampering with an audio leads to anomalous variations of the underlying ENF signal, a wavelet-filtered ENF signal is generated to highlight the abnormal ENF variations. An autoregressive (AR) model is then fitted to the detail part of the ENF signal and the resulting AR coefficients are employed to train the classifier under a supervised-learning framework. Experimental results show that our proposed method significantly outperforms the state-of-art methods in the context where moderate or high levels of noise are present. Moreover, robustness against MP3 compression can be achieved.