An Integrated Speech Tampering Detection Framework With Deep Neural Networks

Malicious actors can now manipulate recorded speech using AI tools through techniques like copy-move forgery and splicing (inserting audio segments), creating fake voices and spreading misinformation. Researchers developed an integrated detection framework (IDF), a system combining multiple deep neural networks (computer models inspired by how brains learn) to both detect when speech has been tampered with and identify what type of tampering was used, achieving over 95% accuracy across multiple languages.