4 accepted papers
Partially deepfake audio localization is important in audio forensics. However, existing localization models for partially deepfake audio face two major challenges: distribution shifts between training and testing data as well as insufficient utilization of information from both manipulated regions…
Partially deepfake audio attacks have attracted the attention recently, and the demand for locating the manipulation regions of partially deepfake audio arises accordingly. However, existing methods are usually proposed based on frame-level authenticity detection or splicing boundaries detection, ne…
Rapid advancements in speech synthesis and voice conversion bring convenience but also new security risks, creating an urgent need for effective audio deepfake detection. Although current models perform well, their effectiveness diminishes when confronted with the diverse and evolving nature of real…
The rapid evolution of speech synthesis and voice conversion has raised substantial concerns due to the potential misuse of such technology, prompting a pressing need for effective audio deepfake detection mechanisms. Existing detection models have shown remarkable success in discriminating known de…