What is it about?

This research focuses on attributing audio deepfakes to their source, rather than simply detecting whether they are real or fake. It introduces a two-level framework that first identifies the generation technology used to create a synthetic voice and then recognizes the specific AI model responsible. By analyzing subtle acoustic patterns using a shared neural encoder and attention mechanisms, the framework improves forensic reliability under real-world conditions, including unfamiliar or unseen attacks. This structured approach helps investigators and researchers trace the origins of fake audio with high accuracy.

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Why is it important?

Audio deepfakes can be used for scams, disinformation, or to undermine trust in digital communications. While detecting whether audio is real or fake is important, understanding where a fake comes from is crucial for forensic analysis, accountability, and policy responses. By attributing synthetic audio to both the generation technology and the specific AI model behind it, this work supports more reliable investigations, helps expose malicious actors, and contributes to building transparent and trustworthy media ecosystems.

Perspectives

Future work will explore extending the framework to new types of generative models and family-level attribution, enabling broader coverage of emerging technologies. We also plan to combine audio and visual modalities for multi-modal deepfake attribution, and to integrate the framework into real-world forensic workflows. These developments could support law enforcement, journalists, and regulators in tracing and understanding increasingly sophisticated synthetic media.

Andrea Di Pierno
IMT School for Advanced Studies

Read the Original

This page is a summary of: Towards Reliable Audio Deepfake Attribution and Model Recognition: A Multi-Level Autoencoder-Based Framework, October 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3746265.3759668.
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