One-Class Learning Towards Synthetic Voice Spoofing Detection

By You Zhang, Fei Jiang, Zhiyao Duan. IEEE Signal Processing Letters, 2020

306 citations recorded by the source corpus.

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).

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/62551437d1cf90485dfeb6beb00ddef0a24237ef

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Citations in the corpus

Does Audio Deepfake Detection Generalize? (2022, methodology): “Residual Networks were ﬁrst used for audio deepfake detection by [17], and continue to be employed [18, 19].”
Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward (2021, methodology): “[251] proposed a DL-based approach using ResNet-18 and one-”
Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation (2022, background): “Motivated by (i) its proven application to the learning of general neural representations for a range of different tasks [11– 19] (ii) evidence that fine-tuning with modest quantities of labelled data leads to state-of-the-art results, (iii) encouraging, previously reported results for anti-spoofing”