Unveiling Audio Deepfake Origins: A Deep Metric learning And Conformer Network Approach With Ensemble Fusion

TL;DR

This paper introduces a novel audio source tracing system for deepfakes that combines deep metric learning, Conformer networks, and ensemble fusion, achieving superior performance in identifying the origin of fake audio.

Contribution

It presents a new multi-component system integrating N-pair loss, Conformer architecture, and ensemble fusion for improved audio source tracing in deepfake detection.

Findings

Superior performance over baseline in source tracing accuracy.

Effective differentiation between real and fake speech patterns.

Robustness across in-domain and out-of-domain scenarios.

Abstract

Audio deepfakes are acquiring an unprecedented level of realism with advanced AI. While current research focuses on discerning real speech from spoofed speech, tracing the source system is equally crucial. This work proposes a novel audio source tracing system combining deep metric multi-class N-pair loss with Real Emphasis and Fake Dispersion framework, a Conformer classification network, and ensemble score-embedding fusion. The N-pair loss improves discriminative ability, while Real Emphasis and Fake Dispersion enhance robustness by focusing on differentiating real and fake speech patterns. The Conformer network captures both global and local dependencies in the audio signal, crucial for source tracing. The proposed ensemble score-embedding fusion shows an optimal trade-off between in-domain and out-of-domain source tracing scenarios. We evaluate our method using Frechet Distance and standard metrics, demonstrating superior performance in source tracing over the baseline system.