3 papers
cs.CV2025
Real-Aware Residual Model Merging for Deepfake Detection
Jinhee Park, Guisik Kim, Choongsang Cho +1
Deepfake generators evolve quickly, making exhaustive data collection and repeated retraining impractical. We argue that model merging is a natural fit for deepfake detection: unli…
eess.AS2025
Instance-Specific Test-Time Training for Speech Editing in the Wild
Taewoo Kim, Uijong Lee, Hayoung Park +3
Speech editing systems aim to naturally modify speech content while preserving acoustic consistency and speaker identity. However, previous studies often struggle to adapt to unsee…
eess.AS2025
Naturalness-Aware Curriculum Learning with Dynamic Temperature for Speech Deepfake Detection
Taewoo Kim, Guisik Kim, Choongsang Cho +1
Recent advances in speech deepfake detection (SDD) have significantly improved artifacts-based detection in spoofed speech. However, most models overlook speech naturalness, a cruc…