Paper ID: 2404.13008

Enhancing Generalization in Audio Deepfake Detection: A Neural Collapse based Sampling and Training Approach

Mohammed Yousif, Jonat John Mathew, Huzaifa Pallan, Agamjeet Singh Padda, Syed Daniyal Shah, Sara Adamski, Madhu Reddiboina, Arjun Pankajakshan

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.

Submitted: Apr 19, 2024