4 citations · 4 across the 6 of their papers we have counts for
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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…
eess.AS2025★ 4 cited
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…