# The defender's perspective on automatic speaker verification: An overview

arXiv: https://arxiv.org/abs/2305.12804 · HTML: https://mlsanity.com/paper/2305.12804/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 48 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 30 of 33)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models](https://mlsanity.com/paper/1805.06605/) (2018)
- [ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection](https://mlsanity.com/paper/1904.05441/) (2019)
- [ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection](https://mlsanity.com/paper/2109.00537/) (2021)
- [ADD 2022: the First Audio Deep Synthesis Detection Challenge](https://mlsanity.com/paper/2202.08433/) (2022)
- [Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems](https://mlsanity.com/paper/1911.01840/) (2019)
- [The PartialSpoof Database and Countermeasures for the Detection of Short Fake Speech Segments Embedded in an Utterance](https://mlsanity.com/paper/2204.05177/) (2022)
- [SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems](https://mlsanity.com/paper/2007.06622/) (2020)
- [Half-Truth: A Partially Fake Audio Detection Dataset](https://mlsanity.com/paper/2104.03617/) (2021)
- [Fooling End-to-end Speaker Verification by Adversarial Examples](https://mlsanity.com/paper/1801.03339/) (2018)
- [SirenAttack: Generating Adversarial Audio for End-to-End Acoustic Systems](https://mlsanity.com/paper/1901.07846/) (2019)
- [An Initial Investigation for Detecting Partially Spoofed Audio](https://mlsanity.com/paper/2104.02518/) (2021)
- [Adversarial Attacks on Spoofing Countermeasures of automatic speaker verification](https://mlsanity.com/paper/1910.08716/) (2019)
- [Adversarial Attacks on GMM i-vector based Speaker Verification Systems](https://mlsanity.com/paper/1911.03078/) (2019)
- [Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition](https://mlsanity.com/paper/2206.03393/) (2022)
- [Adversarial Attack and Defense Strategies for Deep Speaker Recognition Systems](https://mlsanity.com/paper/2008.07685/) (2020)
- [Defense against adversarial attacks on spoofing countermeasures of ASV](https://mlsanity.com/paper/2003.03065/) (2020)
- [The Attacker's Perspective on Automatic Speaker Verification: An Overview](https://mlsanity.com/paper/2004.08849/) (2020)
- [Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning](https://mlsanity.com/paper/2106.00273/) (2021)
- [Multi-Task Learning in Utterance-Level and Segmental-Level Spoof Detection](https://mlsanity.com/paper/2107.14132/) (2021)
- [Waveform Boundary Detection for Partially Spoofed Audio](https://mlsanity.com/paper/2211.00226/) (2022)
- [Investigating Robustness of Adversarial Samples Detection for Automatic Speaker Verification](https://mlsanity.com/paper/2006.06186/) (2020)
- [Partially Fake Audio Detection by Self-attention-based Fake Span Discovery](https://mlsanity.com/paper/2202.06684/) (2022)
- [Voting for the right answer: Adversarial defense for speaker verification](https://mlsanity.com/paper/2106.07868/) (2021)
- [Adversarial Sample Detection for Speaker Verification by Neural Vocoders](https://mlsanity.com/paper/2107.00309/) (2021)
- [AdvEst: Adversarial Perturbation Estimation to Classify and Detect Adversarial Attacks against Speaker Identification](https://mlsanity.com/paper/2204.03848/) (2022)

## Built on by (most cited first, 9 of 9)

- [Temporal-Channel Modeling in Multi-head Self-Attention for Synthetic Speech Detection](https://mlsanity.com/paper/2406.17376/) (2024)
- [DFADD: The Diffusion and Flow-Matching Based Audio Deepfake Dataset](https://mlsanity.com/paper/2409.08731/) (2024)
- [Coarse-to-Fine Proposal Refinement Framework for Audio Temporal Forgery Detection and Localization](https://mlsanity.com/paper/2407.16554/) (2024)
- [Scalable Ensemble-based Detection Method against Adversarial Attacks for speaker verification](https://mlsanity.com/paper/2312.08622/) (2023)
- [On the Generation and Removal of Speaker Adversarial Perturbation for Voice-Privacy Protection](https://mlsanity.com/paper/2412.09195/) (2024)
- [Towards Generalized Source Tracing for Codec-Based Deepfake Speech](https://mlsanity.com/paper/2506.07294/) (2025)
- [3-Tracer: A Tri-level Temporal-Aware Framework for Audio Forgery Detection and Localization](https://mlsanity.com/paper/2511.21237/) (2025)
- [Degrading Voice: A Comprehensive Overview of Robust Voice Conversion Through Input Manipulation](https://mlsanity.com/paper/2512.06304/) (2025)
- [Adaptive Compression and Targeted Perturbation: A Unified Framework for Generalized Audio Deepfake Detection](https://mlsanity.com/paper/2610.16049/) (2026)

## Abstract (arXiv)

Automatic speaker verification (ASV) plays a critical role in security-sensitive environments. Regrettably, the reliability of ASV has been undermined by the emergence of spoofing attacks, such as replay and synthetic speech, as well as adversarial attacks and the relatively new partially fake speech. While there are several review papers that cover replay and synthetic speech, and adversarial attacks, there is a notable gap in a comprehensive review that addresses defense against adversarial attacks and the recently emerged partially fake speech. Thus, the aim of this paper is to provide a thorough and systematic overview of the defense methods used against these types of attacks.