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

arXiv: https://arxiv.org/abs/2004.08849 · HTML: https://mlsanity.com/paper/2004.08849/

## Key facts

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

## Built on by (most cited first, 19 of 19)

- [XLSR-Mamba: A Dual-Column Bidirectional State Space Model for Spoofing Attack Detection](https://mlsanity.com/paper/2411.10027/) (2024)
- [Nes2Net: A Lightweight Nested Architecture for Foundation Model Driven Speech Anti-spoofing](https://mlsanity.com/paper/2504.05657/) (2025)
- [Channel-wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attacks](https://mlsanity.com/paper/2107.08803/) (2021)
- [Adversarial defense for automatic speaker verification by cascaded self-supervised learning models](https://mlsanity.com/paper/2102.07047/) (2021)
- [Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning](https://mlsanity.com/paper/2106.00273/) (2021)
- [Voting for the right answer: Adversarial defense for speaker verification](https://mlsanity.com/paper/2106.07868/) (2021)
- [Deepfake audio detection by speaker verification](https://mlsanity.com/paper/2209.14098/) (2022)
- [How Do Neural Spoofing Countermeasures Detect Partially Spoofed Audio?](https://mlsanity.com/paper/2406.02483/) (2024)
- [The defender's perspective on automatic speaker verification: An overview](https://mlsanity.com/paper/2305.12804/) (2023)
- [Adversarial Sample Detection for Speaker Verification by Neural Vocoders](https://mlsanity.com/paper/2107.00309/) (2021)
- [Practical Attacks on Voice Spoofing Countermeasures](https://mlsanity.com/paper/2107.14642/) (2021)
- [On the Detection of Adaptive Adversarial Attacks in Speaker Verification Systems](https://mlsanity.com/paper/2202.05725/) (2022)
- [Push-Pull: Characterizing the Adversarial Robustness for Audio-Visual Active Speaker Detection](https://mlsanity.com/paper/2210.00753/) (2022)
- [Spoofing-Aware Speaker Verification by Multi-Level Fusion](https://mlsanity.com/paper/2203.15377/) (2022)
- [Tackling Spoofing-Aware Speaker Verification with Multi-Model Fusion](https://mlsanity.com/paper/2206.09131/) (2022)
- [Symmetric Saliency-based Adversarial Attack To Speaker Identification](https://mlsanity.com/paper/2210.16777/) (2022)
- [Impact of Channel Variation on One-Class Learning for Spoof Detection](https://mlsanity.com/paper/2109.14900/) (2021)
- [Interpretable Spectrum Transformation Attacks to Speaker Recognition](https://mlsanity.com/paper/2302.10686/) (2023)
- [Scalable Ensemble-based Detection Method against Adversarial Attacks for speaker verification](https://mlsanity.com/paper/2312.08622/) (2023)

## 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.