  Digital audio tampering detection can be used to verify the authenticity of
digital audio. However, most current methods use standard electronic network
frequency (ENF) databases for visual comparison analysis of ENF continuity of
digital audio or perform feature extraction for classification by machine
learning methods. ENF databases are usually tricky to obtain, visual methods
have weak feature representation, and machine learning methods have more
information loss in features, resulting in low detection accuracy. This paper
proposes a fusion method of shallow and deep features to fully use ENF
information by exploiting the complementary nature of features at different
levels to more accurately describe the changes in inconsistency produced by
tampering operations to raw digital audio. The method achieves 97.03% accuracy
on three classic databases: Carioca 1, Carioca 2, and New Spanish. In addition,
we have achieved an accuracy of 88.31% on the newly constructed database
GAUDI-DI. Experimental results show that the proposed method is superior to the
state-of-the-art method.
