mlsanityData to 7 Oct 2026

SZU-AFS Antispoofing System for the ASVspoof 5 Challenge

arXiv 2408.09933 ↗First posted 19 Aug 2024cs.SD cs.AI eess.AScited by 4

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Abstract (arXiv)

This paper presents the SZU-AFS anti-spoofing system, designed for Track 1 of the ASVspoof 5 Challenge under open conditions. The system is built with four stages: selecting a baseline model, exploring effective data augmentation (DA) methods for fine-tuning, applying a co-enhancement strategy based on gradient norm aware minimization (GAM) for secondary fine-tuning, and fusing logits scores from the two best-performing fine-tuned models. The system utilizes the Wav2Vec2 front-end feature extractor and the AASIST back-end classifier as the baseline model. During model fine-tuning, three distinct DA policies have been investigated: single-DA, random-DA, and cascade-DA. Moreover, the employed GAM-based co-enhancement strategy, designed to fine-tune the augmented model at both data and optimizer levels, helps the Adam optimizer find flatter minima, thereby boosting model generalization. Overall, the final fusion system achieves a minDCF of 0.115 and an EER of 4.04% on the evaluation set.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 25 of its 35 references that are in the map. First posted on arXiv 19 Aug 2024.

Builds on

25 of 35 references.

References · 25

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  3. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  4. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,301
  5. 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,609
  6. 2020Sharpness-Aware Minimization for Efficiently Improving Generalizationcited by 1,404
  7. 2020MLS: A Large-Scale Multilingual Dataset for Speech Researchcited by 467
  8. 2021ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning of Deep Neural Networkscited by 293
  9. 2019ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detectioncited by 173
  10. 2021AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networkscited by 173
  11. 2020End-to-end anti-spoofing with RawNet2cited by 160
  12. 2021ASVspoof 2021: accelerating progress in spoofed and deepfake speech detectioncited by 157
13 more
  1. 2024ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scalecited by 88
  2. 2021RawBoost: A Raw Data Boosting and Augmentation Method applied to Automatic Speaker Verification Anti-Spoofingcited by 64
  3. 2020One-class Learning Towards Synthetic Voice Spoofing Detectioncited by 59
  4. 2023Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalizationcited by 57
  5. 2021A Comparative Study on Recent Neural Spoofing Countermeasures for Synthetic Speech Detectioncited by 56
  6. 2023Audio Deepfake Detection: A Surveycited by 49
  7. 2022The Vicomtech Audio Deepfake Detection System based on Wav2Vec2 for the 2022 ADD Challengecited by 33
  8. 2022Spoofed training data for speech spoofing countermeasure can be efficiently created using neural vocoderscited by 19
  9. 2024A robust audio deepfake detection system via multi-view featurecited by 19
  10. 2022Representation Selective Self-distillation and wav2vec 2.0 Feature Exploration for Spoof-aware Speaker Verificationcited by 9
  11. 2023Multi-Dataset Co-Training with Sharpness-Aware Optimization for Audio Anti-spoofingcited by 9
  12. 2024Enhancing Partially Spoofed Audio Localization with Boundary-aware Attention Mechanismcited by 9
  13. 2024a-DCF: an architecture agnostic metric with application to spoofing-robust speaker verificationcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Evolutionary Multi-Objective Fusion of Deepfake Speech Detectorscited by 3
  2. 2026RAT: Reference-Augmented Training for ASV Anti-Spoofingcited by 0
  3. 2026What Do Deepfake Speech Detectors Actually Hear?cited by 0
  4. 2026Supervised Post-training of Speech Foundation Models for Robust Adaptation in Speech Deepfake Detectioncited by 0