  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.
