# Adversarial Sample Detection for Speaker Verification by Neural Vocoders

arXiv: https://arxiv.org/abs/2107.00309 · HTML: https://mlsanity.com/paper/2107.00309/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 33 references that are in the map.
- First posted on arXiv 1 Jul 2021.

## Builds on (most cited first, 22 of 22)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram](https://mlsanity.com/paper/1910.11480/) (2019)
- [Audio Adversarial Examples: Targeted Attacks on Speech-to-Text](https://mlsanity.com/paper/1801.01944/) (2018)
- [ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection](https://mlsanity.com/paper/1904.05441/) (2019)
- [In defence of metric learning for speaker recognition](https://mlsanity.com/paper/2003.11982/) (2020)
- [Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition](https://mlsanity.com/paper/1903.10346/) (2019)
- [Fooling End-to-end Speaker Verification by Adversarial Examples](https://mlsanity.com/paper/1801.03339/) (2018)
- [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)
- [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)
- [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)
- [Investigating Robustness of Adversarial Samples Detection for Automatic Speaker Verification](https://mlsanity.com/paper/2006.06186/) (2020)
- [Inaudible Adversarial Perturbations for Targeted Attack in Speaker Recognition](https://mlsanity.com/paper/2005.10637/) (2020)
- [Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning](https://mlsanity.com/paper/2006.03214/) (2020)
- [Voting for the right answer: Adversarial defense for speaker verification](https://mlsanity.com/paper/2106.07868/) (2021)
- [Towards Robust Neural Vocoding for Speech Generation: A Survey](https://mlsanity.com/paper/1912.02461/) (2019)

## Built on by (most cited first, 8 of 8)

- [The defender's perspective on automatic speaker verification: An overview](https://mlsanity.com/paper/2305.12804/) (2023)
- [Push-Pull: Characterizing the Adversarial Robustness for Audio-Visual Active Speaker Detection](https://mlsanity.com/paper/2210.00753/) (2022)
- [LMD: A Learnable Mask Network to Detect Adversarial Examples for Speaker Verification](https://mlsanity.com/paper/2211.00825/) (2022)
- [Scores Know Bobs Voice: Speaker Impersonation Attack](https://mlsanity.com/paper/2603.02781/) (2026)
- [Unraveling Adversarial Examples against Speaker Identification -- Techniques for Attack Detection and Victim Model Classification](https://mlsanity.com/paper/2402.19355/) (2024)
- [Scalable Ensemble-based Detection Method against Adversarial Attacks for speaker verification](https://mlsanity.com/paper/2312.08622/) (2023)
- [Towards Generalized Source Tracing for Codec-Based Deepfake Speech](https://mlsanity.com/paper/2506.07294/) (2025)
- [NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples](https://mlsanity.com/paper/2507.21483/) (2025)

## Abstract (arXiv)

Automatic speaker verification (ASV), one of the most important technology for biometric identification, has been widely adopted in security-critical applications. However, ASV is seriously vulnerable to recently emerged adversarial attacks, yet effective countermeasures against them are limited. In this paper, we adopt neural vocoders to spot adversarial samples for ASV. We use the neural vocoder to re-synthesize audio and find that the difference between the ASV scores for the original and re-synthesized audio is a good indicator for discrimination between genuine and adversarial samples. This effort is, to the best of our knowledge, among the first to pursue such a technical direction for detecting time-domain adversarial samples for ASV, and hence there is a lack of established baselines for comparison. Consequently, we implement the Griffin-Lim algorithm as the detection baseline. The proposed approach achieves effective detection performance that outperforms the baselines in all the settings. We also show that the neural vocoder adopted in the detection framework is dataset-independent. Our codes will be made open-source for future works to do fair comparison.