6 citations · 31 across the 23 of their papers we have counts for
5 papers · 2 filters
Towards Zero-Shot Attribution of Synthetic Speech via Audio-Text Contrastive Retrieval
Cristian-Teodor Neamtu, Serban Mihalache, Stefan Smeu +3
Audio deepfake forensics is moving beyond a simple real-or-fake verdict toward attribution: which system generated the audio clip? Most source-attribution methods cast this as clos…
Detecting Audio Deepfakes on the Edge:Lightweight SSL-Based Detection in a Browser Plugin
Octavian Pascu, Dan Oneata, Horia Cucu +1
Audio deepfakes are a growing challenge for the general public, as well as for journalists and fact-checkers. The latter need reliable tools to verify the authenticity of their sou…
Anchoring the Unknown: Open-Set Model Attribution via Proxy-Anchor Learning
Cristian-Teodor Neamtu, Serban Mihalache, Stefan Smeu +3
The proliferation of text-to-speech (TTS) systems capable of generating realistic synthetic speech poses growing challenges for audio forensics. While binary deepfake detection has…
How Open is Open TTS? A Practical Evaluation of Open Source TTS Tools
Teodora Răgman, Adrian Bogdan Stânea, Horia Cucu +1
Open-source text-to-speech (TTS) frameworks have emerged as highly adaptable platforms for developing speech synthesis systems across a wide range of languages. However, their appl…
Understanding the strengths and weaknesses of SSL models for audio deepfake model attribution
Gabriel Pîrlogeanu, Adriana Stan, Horia Cucu
Audio deepfake model attribution aims to mitigate the misuse of synthetic speech by identifying the source model responsible for generating a given audio sample, enabling accountab…