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From the 1 of 16 linked papers with an AI index.

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20242026
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eess.AS2026

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…

eess.AS2025

Unmasking real-world audio deepfakes: A data-centric approach

David Combei, Adriana Stan, Dan Oneata +2

The growing prevalence of real-world deepfakes presents a critical challenge for existing detection systems, which are often evaluated on datasets collected just for scientific pur…

eess.AS2025

TADA: Training-free Attribution and Out-of-Domain Detection of Audio Deepfakes

Adriana Stan, David Combei, Dan Oneata +1

Deepfake detection has gained significant attention across audio, text, and image modalities, with high accuracy in distinguishing real from fake. However, identifying the exact so…

eess.AS2024

Easy, Interpretable, Effective: openSMILE for voice deepfake detection

Octavian Pascu, Dan Oneata, Horia Cucu +1

In this paper, we demonstrate that attacks in the latest ASVspoof5 dataset -- a de facto standard in the field of voice authenticity and deepfake detection -- can be identified wit…

eess.AS2024

WavLM model ensemble for audio deepfake detection

David Combei, Adriana Stan, Dan Oneata +1

Audio deepfake detection has become a pivotal task over the last couple of years, as many recent speech synthesis and voice cloning systems generate highly realistic speech samples…

eess.AS2024

Towards generalisable and calibrated synthetic speech detection with self-supervised representations

Octavian Pascu, Adriana Stan, Dan Oneata +2

Generalisation -- the ability of a model to perform well on unseen data -- is crucial for building reliable deepfake detectors. However, recent studies have shown that the current…