Multi-Scale Permutation Entropy for Audio Deepfake Detection
Chenglong Wang1; He JY(何佳毅)1; Jiangyan Yi1; Jianhua Tao2; Chu Yuan Zhang1; Xiaohui Zhang1
Conference NameICASSP 2024
Conference Date2024-4-14
Conference Place韩国首尔

With the widespread application of Automatic Speaker Verification (ASV) technology in security authentication, the threat of fake audio attacks looms as a malicious means compromising system security. In this study, we employ the multi-scale permutation entropy (MPE) in audio deepfake detection, which could help measure the complexity and detect the dynamic characteristics of audio signals at different scales. Experimental results indicate that MPE can ffectively improve the performance of LFCC. For example, on the ASVspoof2019 LA test set, it successfully achieves an equal error rate (EER) of less than 2%, which is around 50% lower than that of LFCC. Notably, MPE exhibits extraordinary generalization perfor mance when applied to the In-the-Wild dataset, as its performance of EER is comparable to that of Wav2vec, without requiring pretrain ing. Therefore, we believe that MPE holds promising prospects in voice biometric recognition for anti-spoofing applications. Our code is available at deepfake-detection

Sub direction classification语音识别与合成
planning direction of the national heavy laboratory语音语言处理
Paper associated data
Document Type会议论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Chenglong Wang,He JY,Jiangyan Yi,et al. Multi-Scale Permutation Entropy for Audio Deepfake Detection[C],2024.
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