Author

Wanying Ge

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Preprint Oct 2026

What Do Deepfake Audio Detectors Hear? From Self-Supervised Models to Audio LLMs

Real-world audio recordings for deepfake detection rarely consist solely of speech; they often are mixtures of speech and background events that themselves may also be synthetic. When processing these mixtures, it remains unclear whether detectors attend to the speech or the background event, and where their spectral e...

Wan-Ying Ge, Xin Wang, Junichi Yamagishi · 0 citations

#artificial intelligence Preprint Aug 2026

A Training-Free Proactive Defense Against Partial Speech Manipulation via Self-Embedding Steganography

This paper revisits audio steganography from a new perspective and proposes a self-embedding strategy in which a clean speech signal embeds a compressed representation of itself, enabling post-hoc extraction of reference content.

Yigitcan Özer, Zhe Zhang, Wan-Ying Ge et al. · 1 citation

Preprint Aug 2026

Textual Acoustic Grounding for Generalizable LLM-Based Deepfake Voice Detection

Deepfake voice detection suffers from poor generalization across unseen domains. While Audio Large Language Models (ALLMs) show promise, the modality gap between continuous audio embeddings which capture the subtle acoustic details necessary for deepfake detection and the semantic space of LLMs remains a critical, unde...

Y. El Kheir, Xin Wang, Wan-Ying Ge et al. · 1 citation