Adaptive Fake Audio Detection with Low-Rank Model Squeezing

TL;DR

This paper presents a low-rank adaptation approach for fake audio detection that efficiently adapts to new spoofing algorithms while maintaining accuracy on known types, reducing computational costs and memory usage.

Contribution

It introduces low-rank adaptation matrices for fake audio detection, enabling effective detection of new spoofing types without extensive finetuning.

Findings

Preserves accuracy on known fake audio types.

Reduces storage memory requirements.

Achieves lower equal error rates on specific spoofing algorithms.

Abstract

The rapid advancement of spoofing algorithms necessitates the development of robust detection methods capable of accurately identifying emerging fake audio. Traditional approaches, such as finetuning on new datasets containing these novel spoofing algorithms, are computationally intensive and pose a risk of impairing the acquired knowledge of known fake audio types. To address these challenges, this paper proposes an innovative approach that mitigates the limitations associated with finetuning. We introduce the concept of training low-rank adaptation matrices tailored specifically to the newly emerging fake audio types. During the inference stage, these adaptation matrices are combined with the existing model to generate the final prediction output. Extensive experimentation is conducted to evaluate the efficacy of the proposed method. The results demonstrate that our approach effectively preserves the prediction accuracy of the existing model for known fake audio types. Furthermore, our approach offers several advantages, including reduced storage memory requirements and lower equal error rates compared to conventional finetuning methods, particularly on specific spoofing algorithms.