# Channel-wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attacks

arXiv: https://arxiv.org/abs/2107.08803 · HTML: https://mlsanity.com/paper/2107.08803/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 34 references that are in the map.
- First posted on arXiv 19 Jul 2021.

## Builds on (most cited first, 23 of 23)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection](https://mlsanity.com/paper/1904.05441/) (2019)
- [End-to-end anti-spoofing with RawNet2](https://mlsanity.com/paper/2011.01108/) (2020)
- [One-class Learning Towards Synthetic Voice Spoofing Detection](https://mlsanity.com/paper/2010.13995/) (2020)
- [ASVspoof 2019: spoofing countermeasures for the detection of synthesized, converted and replayed speech](https://mlsanity.com/paper/2102.05889/) (2021)
- [STC Antispoofing Systems for the ASVspoof2019 Challenge](https://mlsanity.com/paper/1904.05576/) (2019)
- [Replay and Synthetic Speech Detection with Res2net Architecture](https://mlsanity.com/paper/2010.15006/) (2020)
- [Deep Residual Neural Networks for Audio Spoofing Detection](https://mlsanity.com/paper/1907.00501/) (2019)
- [ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworks](https://mlsanity.com/paper/1904.01120/) (2019)
- [Adversarial Attacks on GMM i-vector based Speaker Verification Systems](https://mlsanity.com/paper/1911.03078/) (2019)
- [Light Convolutional Neural Network with Feature Genuinization for Detection of Synthetic Speech Attacks](https://mlsanity.com/paper/2009.09637/) (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)
- [Voice Mimicry Attacks Assisted by Automatic Speaker Verification](https://mlsanity.com/paper/1906.01454/) (2019)
- [Data Augmentation with Signal Companding for Detection of Logical Access Attacks](https://mlsanity.com/paper/2102.06332/) (2021)
- [The DKU Replay Detection System for the ASVspoof 2019 Challenge: On Data Augmentation, Feature Representation, Classification, and Fusion](https://mlsanity.com/paper/1907.02663/) (2019)
- [Can We Use Speaker Recognition Technology to Attack Itself? Enhancing Mimicry Attacks Using Automatic Target Speaker Selection](https://mlsanity.com/paper/1811.03790/) (2018)
- [STC Anti-spoofing Systems for the ASVspoof 2015 Challenge](https://mlsanity.com/paper/1507.08074/) (2015)
- [A Study On Convolutional Neural Network Based End-To-End Replay Anti-Spoofing](https://mlsanity.com/paper/1805.09164/) (2018)

## Built on by (most cited first, 18 of 18)

- [AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks](https://mlsanity.com/paper/2110.01200/) (2021)
- [Temporal-Channel Modeling in Multi-head Self-Attention for Synthetic Speech Detection](https://mlsanity.com/paper/2406.17376/) (2024)
- [Nes2Net: A Lightweight Nested Architecture for Foundation Model Driven Speech Anti-spoofing](https://mlsanity.com/paper/2504.05657/) (2025)
- [Partially Fake Audio Detection by Self-attention-based Fake Span Discovery](https://mlsanity.com/paper/2202.06684/) (2022)
- [Learning From Yourself: A Self-Distillation Method for Fake Speech Detection](https://mlsanity.com/paper/2303.01211/) (2023)
- [Towards Quantifying and Reducing Language Mismatch Effects in Cross-Lingual Speech Anti-Spoofing](https://mlsanity.com/paper/2409.08346/) (2024)
- [WaveSP-Net: Learnable Wavelet-Domain Sparse Prompt Tuning for Speech Deepfake Detection](https://mlsanity.com/paper/2510.05305/) (2025)
- [Robust Audio Anti-Spoofing with Fusion-Reconstruction Learning on Multi-Order Spectrograms](https://mlsanity.com/paper/2308.09302/) (2023)
- [Analyzing the Impact of Splicing Artifacts in Partially Fake Speech Signals](https://mlsanity.com/paper/2408.13784/) (2024)
- [Phase-Aware Spoof Speech Detection Based on Res2Net with Phase Network](https://mlsanity.com/paper/2203.10793/) (2022)
- [Audio Anti-spoofing Using a Simple Attention Module and Joint Optimization Based on Additive Angular Margin Loss and Meta-learning](https://mlsanity.com/paper/2211.09898/) (2022)
- [Targeted Augmented Data for Audio Deepfake Detection](https://mlsanity.com/paper/2407.07598/) (2024)
- [ConvNeXt Based Neural Network for Audio Anti-Spoofing](https://mlsanity.com/paper/2209.06434/) (2022)
- [Synthetic Voice Detection and Audio Splicing Detection using SE-Res2Net-Conformer Architecture](https://mlsanity.com/paper/2210.03581/) (2022)
- [Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo Conversion](https://mlsanity.com/paper/2305.16353/) (2023)
- [Multi-perspective Information Fusion Res2Net with RandomSpecmix for Fake Speech Detection](https://mlsanity.com/paper/2306.15389/) (2023)
- [Toward Improving Synthetic Audio Spoofing Detection Robustness via Meta-Learning and Disentangled Training With Adversarial Examples](https://mlsanity.com/paper/2408.13341/) (2024)
- [Unraveling Hidden Representations: A Multi-Modal Layer Analysis for Better Synthetic Content Forensics](https://mlsanity.com/paper/2508.00784/) (2025)

## Abstract (arXiv)

Existing approaches for anti-spoofing in automatic speaker verification (ASV) still lack generalizability to unseen attacks. The Res2Net approach designs a residual-like connection between feature groups within one block, which increases the possible receptive fields and improves the system's detection generalizability. However, such a residual-like connection is performed by a direct addition between feature groups without channel-wise priority. We argue that the information across channels may not contribute to spoofing cues equally, and the less relevant channels are expected to be suppressed before adding onto the next feature group, so that the system can generalize better to unseen attacks. This argument motivates the current work that presents a novel, channel-wise gated Res2Net (CG-Res2Net), which modifies Res2Net to enable a channel-wise gating mechanism in the connection between feature groups. This gating mechanism dynamically selects channel-wise features based on the input, to suppress the less relevant channels and enhance the detection generalizability. Three gating mechanisms with different structures are proposed and integrated into Res2Net. Experimental results conducted on ASVspoof 2019 logical access (LA) demonstrate that the proposed CG-Res2Net significantly outperforms Res2Net on both the overall LA evaluation set and individual difficult unseen attacks, which also outperforms other state-of-the-art single systems, depicting the effectiveness of our method.