# Spoofing-Aware Speaker Verification by Multi-Level Fusion

arXiv: https://arxiv.org/abs/2203.15377 · HTML: https://mlsanity.com/paper/2203.15377/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 46 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks](https://mlsanity.com/paper/2110.01200/) (2021)
- [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)
- [Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation](https://mlsanity.com/paper/2202.12233/) (2022)
- [Attentive Statistics Pooling for Deep Speaker Embedding](https://mlsanity.com/paper/1803.10963/) (2018)
- [End-to-End Spectro-Temporal Graph Attention Networks for Speaker Verification Anti-Spoofing and Speech Deepfake Detection](https://mlsanity.com/paper/2107.12710/) (2021)
- [One-class Learning Towards Synthetic Voice Spoofing Detection](https://mlsanity.com/paper/2010.13995/) (2020)
- [A Comparative Study on Recent Neural Spoofing Countermeasures for Synthetic Speech Detection](https://mlsanity.com/paper/2103.11326/) (2021)
- [Fooling End-to-end Speaker Verification by Adversarial Examples](https://mlsanity.com/paper/1801.03339/) (2018)
- [Replay and Synthetic Speech Detection with Res2net Architecture](https://mlsanity.com/paper/2010.15006/) (2020)
- [The Attacker's Perspective on Automatic Speaker Verification: An Overview](https://mlsanity.com/paper/2004.08849/) (2020)
- [Graph Attention Networks for Anti-Spoofing](https://mlsanity.com/paper/2104.03654/) (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)
- [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)
- [Spoofing Attack Detection using the Non-linear Fusion of Sub-band Classifiers](https://mlsanity.com/paper/2005.10393/) (2020)
- [Integrated Replay Spoofing-aware Text-independent Speaker Verification](https://mlsanity.com/paper/2006.05599/) (2020)
- [Replay attack detection with complementary high-resolution information using end-to-end DNN for the ASVspoof 2019 Challenge](https://mlsanity.com/paper/1904.10134/) (2019)
- [SASV Challenge 2022: A Spoofing Aware Speaker Verification Challenge Evaluation Plan](https://mlsanity.com/paper/2201.10283/) (2022)
- [Self-supervised pre-training with acoustic configurations for replay spoofing detection](https://mlsanity.com/paper/1910.09778/) (2019)
- [A study on the role of subsidiary information in replay attack spoofing detection](https://mlsanity.com/paper/2001.11688/) (2020)

## Built on by (most cited first, 2 of 2)

- [One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training](https://mlsanity.com/paper/2308.07934/) (2023)
- [Generalizing Speaker Verification for Spoof Awareness in the Embedding Space](https://mlsanity.com/paper/2401.11156/) (2024)

## Abstract (arXiv)

Recently, many novel techniques have been introduced to deal with spoofing attacks, and achieve promising countermeasure (CM) performances. However, these works only take the stand-alone CM models into account. Nowadays, a spoofing aware speaker verification (SASV) challenge which aims to facilitate the research of integrated CM and ASV models, arguing that jointly optimizing CM and ASV models will lead to better performance, is taking place. In this paper, we propose a novel multi-model and multi-level fusion strategy to tackle the SASV task. Compared with purely scoring fusion and embedding fusion methods, this framework first utilizes embeddings from CM models, propagating CM embeddings into a CM block to obtain a CM score. In the second-level fusion, the CM score and ASV scores directly from ASV systems will be concatenated into a prediction block for the final decision. As a result, the best single fusion system has achieved the SASV-EER of 0.97% on the evaluation set. Then by ensembling the top-5 fusion systems, the final SASV-EER reached 0.89%.