Does Current Deepfake Audio Detection Model Effectively Detect ALM-Based Deepfake Audio?

Currently, Audio Language Models (ALMs) are rapidly advancing due to the developments in large language models and audio neural codecs. These ALMs have significantly lowered the bar-rier to creating deepfake audio, generating highly realistic and diverse types of deepfake audio, which pose severe threats to society. Consequently, effective audio deepfake detection tech-nologies to detect ALM-based audio have become increasingly critical. This paper investigate the effectiveness of current coun-termeasure (CM) against ALM-based audio. Specifically, we collect 12 types of the latest ALM-based deepfake audio and utilizing the latest CMs to evaluate. Our findings reveal that the latest codec-trained CM can effectively detect ALM-based audio, achieving 0% equal error rate under most ALM test conditions' which exceeded our expectations. This indicates promising directions for future research in ALM-based deepfake audio detection.