mlsanityData to 19 Oct 2026

Bridging the Spoof Gap: A Unified Parallel Aggregation Network for Voice Presentation Attacks

arXiv 2309.10560 ↗First posted 19 Sep 2023cs.SD eess.AScited by 1

On the map: Speech and audio language models → Anti-spoofing and adversarial audio attacks

Abstract (arXiv)

Automatic Speaker Verification (ASV) systems are increasingly used in voice bio-metrics for user authentication but are susceptible to logical and physical spoofing attacks, posing security risks. Existing research mainly tackles logical or physical attacks separately, leading to a gap in unified spoofing detection. Moreover, when existing systems attempt to handle both types of attacks, they often exhibit significant disparities in the Equal Error Rate (EER). To bridge this gap, we present a Parallel Stacked Aggregation Network that processes raw audio. Our approach employs a split-transform-aggregation technique, dividing utterances into convolved representations, applying transformations, and aggregating the results to identify logical (LA) and physical (PA) spoofing attacks. Evaluation of the ASVspoof-2019 and VSDC datasets shows the effectiveness of the proposed system. It outperforms state-of-the-art solutions, displaying reduced EER disparities and superior performance in detecting spoofing attacks. This highlights the proposed method's generalizability and superiority. In a world increasingly reliant on voice-based security, our unified spoofing detection system provides a robust defense against a spectrum of voice spoofing attacks, safeguarding ASVs and user data effectively.

Key facts. Cited by 1 paper in the map (data through 19 Oct 2026). Placed in Anti-spoofing and adversarial audio attacks, one of 11 directions in Speech and audio language models. Builds on 21 of its 43 references that are in the map. First posted on arXiv 19 Sep 2023.

Builds on

21 of 43 references.

Closest ancestors · 3

Same direction, newest first

  1. 2022Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation24 Feb
  2. 2021RW-Resnet: A Novel Speech Anti-Spoofing Model Using Raw Waveform12 Aug
  3. 2021End-to-End Spectro-Temporal Graph Attention Networks for Speaker Verification Anti-Spoofing and Speech Deepfake Detection27 Jul

Other references · 18

  1. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,653
  2. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,554
  3. 2017Deep Layer Aggregationcited by 439
  4. 2021AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networkscited by 238
  5. 2019ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detectioncited by 235
  6. 2020One-class Learning Towards Synthetic Voice Spoofing Detectioncited by 76
  7. 2019STC Antispoofing Systems for the ASVspoof2019 Challengecited by 72
  8. 2021A Comparative Study on Recent Neural Spoofing Countermeasures for Synthetic Speech Detectioncited by 71
  9. 2020Replay and Synthetic Speech Detection with Res2net Architecturecited by 47
  10. 2019ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworkscited by 33
  11. 2020Light Convolutional Neural Network with Feature Genuinization for Detection of Synthetic Speech Attackscited by 29
  12. 2019Ensemble Models for Spoofing Detection in Automatic Speaker Verificationcited by 13
6 more
  1. 2019Introduction to Voice Presentation Attack Detection and Recent Advancescited by 12
  2. 2019Detecting Spoofing Attacks Using VGG and SincNet: BUT-Omilia Submission to ASVspoof 2019 Challengecited by 12
  3. 2020Spoofing Attack Detection using the Non-linear Fusion of Sub-band Classifierscited by 12
  4. 2022Voice Spoofing Countermeasures: Taxonomy, State-of-the-art, experimental analysis of generalizability, open challenges, and the way forwardcited by 7
  5. 2019Voice Spoofing Detection Corpus for Single and Multi-order Audio Replayscited by 4
  6. 2020Audio Spoofing Verification using Deep Convolutional Neural Networks by Transfer Learningcited by 4

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025SHIELD: A Secure and Highly Enhanced Integrated Learning for Robust Deepfake Detection against Adversarial Attackscited by 3

Similar, not cited

Close in wording, no citation either way

  1. 2024Generalizing Speaker Verification for Spoof Awareness in the Embedding Space
  2. 2022Tackling Spoofing-Aware Speaker Verification with Multi-Model Fusion
  3. 2024Spoofing-Robust Speaker Verification Using Parallel Embedding Fusion: BTU Speech Group's Approach for ASVspoof5 Challenge
  4. 2026Spoofing-Aware Speaker Verification via Wavelet Prompt Tuning and Multi-Model Ensembles
  5. 2022Baseline Systems for the First Spoofing-Aware Speaker Verification Challenge: Score and Embedding Fusion
  6. 2023Frame-to-Utterance Convergence: A Spectra-Temporal Approach for Unified Spoofing Detection
  7. 2022A Probabilistic Fusion Framework for Spoofing Aware Speaker Verification
  8. 2024Toward Improving Synthetic Audio Spoofing Detection Robustness via Meta-Learning and Disentangled Training With Adversarial Examples