Year: 2024
Type: conference-paper
Abstract: 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.
Institutions: State Key Laboratory of Media Convergence and Communication, Communication University of China, Shandong University, Chinese Academy of Sciences, Institute of Automation, University of Chinese Academy of Sciences, Beijing National Research Center for Information Science and Technology, Tsinghua University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4405709464