  The rise of advanced large language models such as GPT-4, GPT-4o, and the
Claude family has made fake audio detection increasingly challenging.
Traditional fine-tuning methods struggle to keep pace with the evolving
landscape of synthetic speech, necessitating continual learning approaches that
can adapt to new audio while retaining the ability to detect older types.
Continual learning, which acts as an effective tool for detecting newly emerged
deepfake audio while maintaining performance on older types, lacks a
well-constructed and user-friendly evaluation framework. To address this gap,
we introduce EVDA, a benchmark for evaluating continual learning methods in
deepfake audio detection. EVDA includes classic datasets from the Anti-Spoofing
Voice series, Chinese fake audio detection series, and newly generated deepfake
audio from models like GPT-4 and GPT-4o. It supports various continual learning
techniques, such as Elastic Weight Consolidation (EWC), Learning without
Forgetting (LwF), and recent methods like Regularized Adaptive Weight
Modification (RAWM) and Radian Weight Modification (RWM). Additionally, EVDA
facilitates the development of robust algorithms by providing an open interface
for integrating new continual learning methods
