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Toward Improving Synthetic Audio Spoofing Detection Robustness via
  Meta-Learning and Disentangled Training With Adversarial Examples

Toward Improving Synthetic Audio Spoofing Detection Robustness via Meta-Learning and Disentangled Training With Adversarial Examples

23 August 2024
Zhenyu Wang
John H. L. Hansen
    AAML
ArXivPDFHTML

Papers citing "Toward Improving Synthetic Audio Spoofing Detection Robustness via Meta-Learning and Disentangled Training With Adversarial Examples"

3 / 3 papers shown
Title
AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph
  Attention Networks
AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks
Jee-weon Jung
Hee-Soo Heo
Hemlata Tak
Hye-jin Shim
Joon Son Chung
Bong-Jin Lee
Ha-Jin Yu
Nicholas W. D. Evans
153
281
0
04 Oct 2021
End-to-end spoofing detection with raw waveform CLDNNs
End-to-end spoofing detection with raw waveform CLDNNs
Heinrich Dinkel
Nanxin Chen
Y. Qian
Kai Yu
46
78
0
26 Jul 2020
Adversarial Machine Learning at Scale
Adversarial Machine Learning at Scale
Alexey Kurakin
Ian Goodfellow
Samy Bengio
AAML
273
3,110
0
04 Nov 2016
1