One-class Learning Towards Synthetic Voice Spoofing Detection

Abstract

Human voices can be used to authenticate the identity of the speaker, but the automatic speaker verification (ASV) systems are vulnerable to voice spoofing attacks, such as impersonation, replay, text-to-speech, and voice conversion. Recently, researchers developed anti-spoofing techniques to improve the reliability of ASV systems against spoofing attacks. However, most methods encounter difficulties in detecting unknown attacks in practical use, which often have different statistical distributions from known attacks. Especially, the fast development of synthetic voice spoofing algorithms is generating increasingly powerful attacks, putting the ASV systems at risk of unseen attacks. In this work, we propose an anti-spoofing system to detect unknown synthetic voice spoofing attacks (i.e., text-to-speech or voice conversion) using one-class learning. The key idea is to compact the bona fide speech representation and inject an angular margin to separate the spoofing attacks in the embedding space. Without resorting to any data augmentation methods, our proposed system achieves an equal error rate (EER) of 2.19% on the evaluation set of ASVspoof 2019 Challenge logical access scenario, outperforming all existing single systems (i.e., those without model ensemble).

References in corpus (1)

• Audio Spoofing Verification using Deep Convolutional Neural Networks by Transfer Learning

Cited by in corpus (11)

• A Survey on Speech Deepfake Detection
• SingFake: Singing Voice Deepfake Detection
• Spatial Reconstructed Local Attention Res2Net with F0 Subband for Fake Speech Detection
• Training-Free Deepfake Voice Recognition by Leveraging Large-Scale Pre-Trained Models
• VSMask: Defending Against Voice Synthesis Attack via Real-Time Predictive Perturbation
• Faked Speech Detection with Zero Prior Knowledge
• Generalizing Speaker Verification for Spoof Awareness in the Embedding Space
• Toward Improving Synthetic Audio Spoofing Detection Robustness via Meta-Learning and Disentangled Training With Adversarial Examples
• Is Audio Spoof Detection Robust to Laundering Attacks?
• What Affects the Performance of Fake Audio Detection? Analyzing Factors in a Continual Learning Setting
• FADEL: Uncertainty-aware Fake Audio Detection with Evidential Deep Learning