Enhancing Generalization in Audio Deepfake Detection: A Neural Collapse based Sampling and Training Approach
<|reference_start|>Enhancing Generalization in Audio Deepfake Detection: A Neural Collapse based Sampling and Training Approach: Generalization in audio deepfake detection presents a significant challenge, with models trained on specific datasets often struggling to detect deepfakes generated under varying conditions and unknown algorithms. While collectively training a model using diverse datasets can enhance its generalization ability, it comes with high computational costs. To address this, we propose a neural collapse-based sampling approach applied to pre-trained models trained on distinct datasets to create a new training database. Using ASVspoof 2019 dataset as a proof-of-concept, we implement pre-trained models with Resnet and ConvNext architectures. Our approach demonstrates comparable generalization on unseen data while being computationally efficient, requiring less training data. Evaluation is conducted using the In-the-wild dataset.<|reference_end|>
@article{yousif2024enhancing,
    title={Enhancing Generalization in Audio Deepfake Detection: A Neural Collapse
  based Sampling and Training Approach},
    author={Mohammed Yousif, Jonat John Mathew, Huzaifa Pallan, Agamjeet Singh
  Padda, Syed Daniyal Shah, Sara Adamski, Madhu Reddiboina, Arjun Pankajakshan},
    journal={arXiv preprint arXiv:2404.13008},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13008},
    primaryClass={cs.SD eess.AS}
}