mlsanityData to 14 Oct 2026

The Attacker's Perspective on Automatic Speaker Verification: An Overview

arXiv 2004.08849 ↗First posted 19 Apr 2020eess.AS cs.CRcited by 19

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Abstract (arXiv)

Security of automatic speaker verification (ASV) systems is compromised by various spoofing attacks. While many types of non-proactive attacks (and their defenses) have been studied in the past, attacker's perspective on ASV, represents a far less explored direction. It can potentially help to identify the weakest parts of ASV systems and be used to develop attacker-aware systems. We present an overview on this emerging research area by focusing on potential threats of adversarial attacks on ASV, spoofing countermeasures, or both. We conclude the study with discussion on selected attacks and leveraging from such knowledge to improve defense mechanisms against adversarial attacks.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Apr 2020.

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Citing papers per year: 2021: 7; 2022: 6; 2023: 3; 2024: 2; 2025: 1.

19 papers.

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  1. 2024XLSR-Mamba: A Dual-Column Bidirectional State Space Model for Spoofing Attack Detectioncited by 30
  2. 2025Nes2Net: A Lightweight Nested Architecture for Foundation Model Driven Speech Anti-spoofingcited by 19
  3. 2021Channel-wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attackscited by 18
  4. 2021Adversarial defense for automatic speaker verification by cascaded self-supervised learning modelscited by 16
  5. 2021Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learningcited by 16
  6. 2021Voting for the right answer: Adversarial defense for speaker verificationcited by 12
  7. 2022Deepfake audio detection by speaker verificationcited by 11
  8. 2024How Do Neural Spoofing Countermeasures Detect Partially Spoofed Audio?cited by 10
  9. 2023The defender's perspective on automatic speaker verification: An overviewcited by 9
  10. 2021Adversarial Sample Detection for Speaker Verification by Neural Vocoderscited by 8
  11. 2021Practical Attacks on Voice Spoofing Countermeasurescited by 7
  12. 2022On the Detection of Adaptive Adversarial Attacks in Speaker Verification Systemscited by 3
7 more
  1. 2022Push-Pull: Characterizing the Adversarial Robustness for Audio-Visual Active Speaker Detectioncited by 3
  2. 2022Spoofing-Aware Speaker Verification by Multi-Level Fusioncited by 2
  3. 2022Tackling Spoofing-Aware Speaker Verification with Multi-Model Fusioncited by 1
  4. 2022Symmetric Saliency-based Adversarial Attack To Speaker Identificationcited by 1
  5. 2021Impact of Channel Variation on One-Class Learning for Spoof Detectioncited by 0
  6. 2023Interpretable Spectrum Transformation Attacks to Speaker Recognitioncited by 0
  7. 2023Scalable Ensemble-based Detection Method against Adversarial Attacks for speaker verificationcited by 0