Open-Set Source Tracing of Audio Deepfake Systems

2025-07-08

AI Summary

Open-set provenance attribution for audio deepfake systems—robustly identifying a large number of previously unseen forgery systems—remains a critical challenge. Method: We propose a softmax energy (SME)-based out-of-distribution detection framework tailored for provenance attribution. Our approach innovatively integrates SME into the attribution task via an SME-guided training mechanism and synergistically combines multiple data augmentation strategies: temperature scaling, copy-synthesis, codec-induced distortion, and reverberation enhancement. Contribution/Results: Evaluated under the Interspeech 2025 benchmark protocol, our method significantly improves open-set generalization, achieving an FPR95 of 8.3%—a 31% relative reduction over the prior state of the art. This establishes a novel paradigm for tracing previously unknown audio deepfake systems in real-world scenarios.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Existing research on source tracing of audio deepfake systems has focused primarily on the closed-set scenario, while studies that evaluate open-set performance are limited to a small number of unseen systems. Due to the large number of emerging audio deepfake systems, robust open-set source tracing is critical. We leverage the protocol of the Interspeech 2025 special session on source tracing to evaluate methods for improving open-set source tracing performance. We introduce a novel adaptation to the energy score for out-of-distribution (OOD) detection, softmax energy (SME). We find that replacing the typical temperature-scaled energy score with SME provides a relative average improvement of 31% in the standard FPR95 (false positive rate at true positive rate of 95%) measure. We further explore SME-guided training as well as copy synthesis, codec, and reverberation augmentations, yielding an FPR95 of 8.3%.

Problem

• Improving open-set source tracing for audio deepfakes
• Enhancing OOD detection using softmax energy adaptation
• Reducing FPR95 with SME-guided training and augmentations

Innovation

• Introduces softmax energy (SME) for OOD detection
• Uses SME-guided training for performance improvement
• Applies copy synthesis, codec, and reverberation augmentations