The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio
<|reference_start|>The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio: With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for generalized detection methods. ALM-based deepfake audio currently exhibits widespread, high deception, and type versatility, posing a significant challenge to current audio deepfake detection (ADD) models trained solely on vocoded data. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially construct the Codecfake dataset, an open-source large-scale dataset, including 2 languages, over 1M audio samples, and various test conditions, focus on ALM-based audio detection. As countermeasure, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original SAM, we propose the CSAM strategy to learn a domain balanced and generalized minima. In our experiments, we first demonstrate that ADD model training with the Codecfake dataset can effectively detects ALM-based audio. Furthermore, our proposed generalization countermeasure yields the lowest average Equal Error Rate (EER) of 0.616% across all test conditions compared to baseline models. The dataset and associated code are available online.<|reference_end|>
@article{xie2024the,
    title={The Codecfake Dataset and Countermeasures for the Universally Detection
  of Deepfake Audio},
    author={Yuankun Xie, Yi Lu, Ruibo Fu, Zhengqi Wen, Zhiyong Wang, Jianhua Tao,
  Xin Qi, Xiaopeng Wang, Yukun Liu, Haonan Cheng, Long Ye, Yi Sun},
    journal={arXiv preprint arXiv:2405.04880},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04880},
    primaryClass={cs.SD cs.AI eess.AS}
}