Multilingual Source Tracing of Speech Deepfakes: a First Benchmark

Recent progress in generative AI has made it increasingly easy to create natural-sounding deepfake speech from just a few seconds of audio. While these tools support helpful applications, they also raise serious concerns by making it possible to generate convincing fake speech in many languages...

Frame-Level Temporal Difference Learning for Partial Deepfake Speech Detection

Detecting partial deepfake speech is essential due to its potential for subtle misinformation. However, existing methods depend on costly frame-level annotations during training, limiting real-world scalability. Also, they focus on detecting transition artifacts between bonafide and deepfake...

STOPA: a Database of Systematic VariaTion of DeePfake Audio for Open-Set Source Tracing and Attribution

A key research area in deepfake speech detection is source tracing - determining the origin of synthesised utterances. The approaches may involve identifying the acoustic model AM, vocoder model VM, or other generation-specific parameters. However, progress is limited by the lack of a dedicated,...