# Anti-Spoofing Using Transfer Learning with Variational Information Bottleneck

arXiv: https://arxiv.org/abs/2204.01387 · HTML: https://mlsanity.com/paper/2204.01387/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 32 references that are in the map.
- First posted on arXiv 4 Apr 2022.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks](https://mlsanity.com/paper/2110.01200/) (2021)
- [End-to-end anti-spoofing with RawNet2](https://mlsanity.com/paper/2011.01108/) (2020)
- [Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings](https://mlsanity.com/paper/2104.03502/) (2021)
- [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)
- [Exploring wav2vec 2.0 on speaker verification and language identification](https://mlsanity.com/paper/2012.06185/) (2020)
- [End-to-End Spectro-Temporal Graph Attention Networks for Speaker Verification Anti-Spoofing and Speech Deepfake Detection](https://mlsanity.com/paper/2107.12710/) (2021)
- [A Comparative Study on Recent Neural Spoofing Countermeasures for Synthetic Speech Detection](https://mlsanity.com/paper/2103.11326/) (2021)
- [Variational Information Bottleneck for Effective Low-Resource Fine-Tuning](https://mlsanity.com/paper/2106.05469/) (2021)
- [Investigating self-supervised front ends for speech spoofing countermeasures](https://mlsanity.com/paper/2111.07725/) (2021)
- [Towards End-to-End Synthetic Speech Detection](https://mlsanity.com/paper/2106.06341/) (2021)
- [Raw Differentiable Architecture Search for Speech Deepfake and Spoofing Detection](https://mlsanity.com/paper/2107.12212/) (2021)
- [t-DCF: a Detection Cost Function for the Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification](https://mlsanity.com/paper/1804.09618/) (2018)
- [RW-Resnet: A Novel Speech Anti-Spoofing Model Using Raw Waveform](https://mlsanity.com/paper/2108.05684/) (2021)

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

- [The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio](https://mlsanity.com/paper/2405.04880/) (2024)
- [FSD: An Initial Chinese Dataset for Fake Song Detection](https://mlsanity.com/paper/2309.02232/) (2023)
- [Malafide: a novel adversarial convolutive noise attack against deepfake and spoofing detection systems](https://mlsanity.com/paper/2306.07655/) (2023)
- [Codecfake: An Initial Dataset for Detecting LLM-based Deepfake Audio](https://mlsanity.com/paper/2406.08112/) (2024)
- [Toward Improving Synthetic Audio Spoofing Detection Robustness via Meta-Learning and Disentangled Training With Adversarial Examples](https://mlsanity.com/paper/2408.13341/) (2024)
- [Towards Attention-based Contrastive Learning for Audio Spoof Detection](https://mlsanity.com/paper/2407.03514/) (2024)
- [HyperPotter: Spell the Charm of High-Order Interactions in Audio Deepfake Detection](https://mlsanity.com/paper/2602.05670/) (2026)
- [Supervised Post-training of Speech Foundation Models for Robust Adaptation in Speech Deepfake Detection](https://mlsanity.com/paper/2606.25328/) (2026)

## Abstract (arXiv)

Recent advances in sophisticated synthetic speech generated from text-to-speech (TTS) or voice conversion (VC) systems cause threats to the existing automatic speaker verification (ASV) systems. Since such synthetic speech is generated from diverse algorithms, generalization ability with using limited training data is indispensable for a robust anti-spoofing system. In this work, we propose a transfer learning scheme based on the wav2vec 2.0 pretrained model with variational information bottleneck (VIB) for speech anti-spoofing task. Evaluation on the ASVspoof 2019 logical access (LA) database shows that our method improves the performance of distinguishing unseen spoofed and genuine speech, outperforming current state-of-the-art anti-spoofing systems. Furthermore, we show that the proposed system improves performance in low-resource and cross-dataset settings of anti-spoofing task significantly, demonstrating that our system is also robust in terms of data size and data distribution.