# Investigating Robustness of Adversarial Samples Detection for Automatic Speaker Verification

arXiv: https://arxiv.org/abs/2006.06186 · HTML: https://mlsanity.com/paper/2006.06186/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 14 of 14)

- [Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition](https://mlsanity.com/paper/2206.03393/) (2022)
- [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)
- [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)
- [Spoofing-Aware Speaker Verification by Multi-Level Fusion](https://mlsanity.com/paper/2203.15377/) (2022)
- [LMD: A Learnable Mask Network to Detect Adversarial Examples for Speaker Verification](https://mlsanity.com/paper/2211.00825/) (2022)
- [Tackling Spoofing-Aware Speaker Verification with Multi-Model Fusion](https://mlsanity.com/paper/2206.09131/) (2022)
- [Unraveling Adversarial Examples against Speaker Identification -- Techniques for Attack Detection and Victim Model Classification](https://mlsanity.com/paper/2402.19355/) (2024)
- [Resilience and Security of Deep Neural Networks Against Intentional and Unintentional Perturbations: Survey and Research Challenges](https://mlsanity.com/paper/2408.00193/) (2024)

## Abstract (arXiv)

Recently adversarial attacks on automatic speaker verification (ASV) systems attracted widespread attention as they pose severe threats to ASV systems. However, methods to defend against such attacks are limited. Existing approaches mainly focus on retraining ASV systems with adversarial data augmentation. Also, countermeasure robustness against different attack settings are insufficiently investigated. Orthogonal to prior approaches, this work proposes to defend ASV systems against adversarial attacks with a separate detection network, rather than augmenting adversarial data into ASV training. A VGG-like binary classification detector is introduced and demonstrated to be effective on detecting adversarial samples. To investigate detector robustness in a realistic defense scenario where unseen attack settings may exist, we analyze various kinds of unseen attack settings' impact and observe that the detector is robust (6.27% EERdet degradation in the worst case) against unseen substitute ASV systems, but it has weak robustness (50.37% EERdet degradation in the worst case) against unseen perturbation methods. The weak robustness against unseen perturbation methods shows a direction for developing stronger countermeasures.