Audio Splicing Detection Using Self-Supervised Representations

Audio splicing is the manipulation of a recording by inserting, deleting, or rearranging segments to alter its content or context with the goal of misrepresenting the original message, fabricating evidence, or creating a misleading narrative. Audio splicing detection has become increasingly vital in the realms of security, digital forensics, and fact-checking, as manipulated audio can be used to spread misinformation or falsify evidence. This paper presents a novel approach to detecting audio splicing by leveraging a self-supervised front end, wav2vec2, in combination with a simple logistic regression classifier. Unlike conventional methods that require handcrafted features or complex models, our method utilizes the rich, context-aware representations produced by wav2vec2 to train a linear classifier that effectively distinguishes between authentic and spliced audio segments. We demonstrate that even with a basic classifier, the wav2vec2 representations capture sufficient discriminative information to achieve good performance on our benchmark dataset.