An Integrated Speech Tampering Detection Framework With Deep Neural Networks

Summary

Researchers present an integrated detection framework (IDF) based on deep neural networks for blind speech tampering detection, covering copy-move, deletion, and splicing manipulations. The framework combines an encoder-decoder network producing a preliminary localisation mask from Mel spectrogram features, an adaptive localisation refinement algorithm, and a classifier for tampering type. Trained on a new 55,500-pair dataset from Chinese and English corpora, it reports average F-scores of 95.32% (Chinese), 96.02% (English), 88.81% (Spanish) and 95.75% (spoofed samples), with localisation error below 0.1 seconds.

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