RuASD is a comprehensive Russian speech anti-spoofing dataset featuring 37 synthesis systems and a robustness evaluation pipeline for real-world channel distortions.
Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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DeePen demonstrates that both production and academic audio deepfake detectors can be reliably deceived by simple signal processing attacks such as time-stretching or echo addition, with some attacks resistible via retraining and others remaining effective.
MLAAD provides a large-scale multi-language synthetic audio dataset for training and evaluating audio anti-spoofing models, showing better training performance than InTheWild and FakeOrReal and alternating superiority with ASVspoof 2019 across eight test sets.
citing papers explorer
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Evaluating Generalization and Robustness in Russian Anti-Spoofing: The RuASD Initiative
RuASD is a comprehensive Russian speech anti-spoofing dataset featuring 37 synthesis systems and a robustness evaluation pipeline for real-world channel distortions.
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DeePen: Penetration Testing for Audio Deepfake Detection
DeePen demonstrates that both production and academic audio deepfake detectors can be reliably deceived by simple signal processing attacks such as time-stretching or echo addition, with some attacks resistible via retraining and others remaining effective.
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MLAAD: The Multi-Language Audio Anti-Spoofing Dataset
MLAAD provides a large-scale multi-language synthetic audio dataset for training and evaluating audio anti-spoofing models, showing better training performance than InTheWild and FakeOrReal and alternating superiority with ASVspoof 2019 across eight test sets.