# Open-Set Source Tracing of Audio Deepfake Systems Source: [Open-Set Source Tracing of Audio Deepfake Systems]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:57:18.888Z Authors: Nicholas Klein, Hemlata Tak, Elie Khoury Published: 2025-07-09T01:03:36.000Z ## 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%. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Audio Source Tracing on MLAAD (eval)](): Audio Source Tracing; MLAAD (eval); ID Accuracy: 95.8. - [Closed-set Attribution on MLAAD v5](): Closed-set Attribution; MLAAD v5; Accuracy: 95.8. - [OOD Detection on MLAAD v5](): OOD Detection; MLAAD v5; Accuracy: not recorded. ## Original sources and implementation [Original paper]() [PDF]()