3 papers
eess.AS2026
Benchmarking Audio Deepfake Detection Robustness in Real-world Communication Scenarios
Haohan Shi, Xiyu Shi, Safak Dogan +3
Existing Audio Deepfake Detection (ADD) systems often struggle to generalise effectively due to the significantly degraded audio quality caused by audio codec compression and chann…
eess.AS2026
Audio Deepfake Detection at the First Greeting: "Hi!"
Haohan Shi, Xiyu Shi, Safak Dogan +2
This paper focuses on audio deepfake detection under real-world communication degradations, with an emphasis on ultra-short inputs (0.5-2.0s), targeting the capability to detect sy…
eess.AS2025
Multi-Granularity Adaptive Time-Frequency Attention Framework for Audio Deepfake Detection under Real-World Communication Degradations
Haohan Shi, Xiyu Shi, Safak Dogan +2
The rise of highly convincing synthetic speech poses a growing threat to audio communications. Although existing Audio Deepfake Detection (ADD) methods have demonstrated good perfo…