AT-ADD: All-Type Audio Deepfake Detection Challenge Evaluation Plan
The rapid advancement of Audio Large Language Models (ALLMs) has enabled cost-effective, high-fidelity generation and manipulation of both speech and non-speech audio, including sound effects, singing voices, and music. While these capabilities foster creativity and content production, they also introduce significant security and trust challenges, as realistic audio deepfakes can now be generated and disseminated at scale. Existing audio deepfake detection (ADD) countermeasures (CMs) and benchmarks, however, remain largely speech-centric, often relying on speech-specific artifacts and exhibiting limited robustness to real-world distortions, as well as restricted generalization to heteroge...
| Authors | Yuankun Xie, Haonan Cheng, Jiayi Zhou et al. |
|---|---|
| Primary category | cs.SD |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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