Papers
1–5 of 5Do Compact SSL Backbones Matter for Audio Deepfake Detection? A Controlled Study with RAPTOR
Self-supervised learning (SSL) underpins modern audio deepfake detection, yet most prior work centers on a single large wav2vec2-XLSR backbone, leaving compact under studied. We present RAPTOR, Repres...
HyperPotter: Spell the Charm of High-Order Interactions in Audio Deepfake Detection
Advances in AIGC technologies have enabled the synthesis of highly realistic audio deepfakes capable of deceiving human auditory perception. Although numerous audio deepfake detection (ADD) methods ha...
Audio Deepfake Detection in the Age of Advanced Text-to-Speech models
Recent advances in Text-to-Speech (TTS) systems have substantially increased the realism of synthetic speech, raising new challenges for audio deepfake detection. This work presents a comparative eval...
Investigating the Impact of Speech Enhancement on Audio Deepfake Detection in Noisy Environments
Logical Access (LA) attacks, also known as audio deepfake attacks, use Text-to-Speech (TTS) or Voice Conversion (VC) methods to generate spoofed speech data. This can represent a serious threat to Aut...
Gender Fairness in Audio Deepfake Detection: Performance and Disparity Analysis
Audio deepfake detection aims to detect real human voices from those generated by Artificial Intelligence (AI) and has emerged as a significant problem in the field of voice biometrics systems. With t...