Adaptive Compression and Targeted Perturbation: A Unified Framework for Generalized Audio Deepfake Detection
Existing audio deepfake detection (ADD) methods frequently exhibit limited generalization, as fixed bottleneck architectures fail to adapt to the heterogeneous information densities inherent in diverse spoofing artifacts. Moreover, standard augmentation often leads to a pathological robustness-accuracy trade-off. In this paper, we propose a unified ADD framework to address these challenges. Our approach integrates: (1) an Adaptive Audio Feature Integration (AAFI) module that dynamically selects optimal latent dimensions via a compression pool and a stochastic exploration mechanism, ensuring flexible representations across multiple types of dynamic attacks; and (2) a Mel-Band Adversarial P...
| Authors | Jianqiao Cui, Haoxian Zhu, Hanbiao Meng et al. |
|---|---|
| Primary category | cs.SD |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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