# ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworks

arXiv: https://arxiv.org/abs/1904.01120 · HTML: https://mlsanity.com/paper/1904.01120/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2019.

## Built on by (most cited first, 23 of 23)

- [Channel-wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attacks](https://mlsanity.com/paper/2107.08803/) (2021)
- [Attentive Merging of Hidden Embeddings from Pre-trained Speech Model for Anti-spoofing Detection](https://mlsanity.com/paper/2406.10283/) (2024)
- [Partially Fake Audio Detection by Self-attention-based Fake Span Discovery](https://mlsanity.com/paper/2202.06684/) (2022)
- [Fully Automated End-to-End Fake Audio Detection](https://mlsanity.com/paper/2208.09618/) (2022)
- [Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection](https://mlsanity.com/paper/2104.03123/) (2021)
- [Multi-Dataset Co-Training with Sharpness-Aware Optimization for Audio Anti-spoofing](https://mlsanity.com/paper/2305.19953/) (2023)
- [AASIST3: KAN-Enhanced AASIST Speech Deepfake Detection using SSL Features and Additional Regularization for the ASVspoof 2024 Challenge](https://mlsanity.com/paper/2408.17352/) (2024)
- [Deepfake CAPTCHA: A Method for Preventing Fake Calls](https://mlsanity.com/paper/2301.03064/) (2023)
- [Speech Foundation Model Ensembles for the Controlled Singing Voice Deepfake Detection (CtrSVDD) Challenge 2024](https://mlsanity.com/paper/2409.02302/) (2024)
- [DSVAE: Interpretable Disentangled Representation for Synthetic Speech Detection](https://mlsanity.com/paper/2304.03323/) (2023)
- [Can Emotion Fool Anti-spoofing?](https://mlsanity.com/paper/2505.23962/) (2025)
- [Phase-Aware Spoof Speech Detection Based on Res2Net with Phase Network](https://mlsanity.com/paper/2203.10793/) (2022)
- [Lightweight Model Attribution and Detection of Synthetic Speech via Audio Residual Fingerprints](https://mlsanity.com/paper/2411.14013/) (2024)
- [Adversarial Speaker Distillation for Countermeasure Model on Automatic Speaker Verification](https://mlsanity.com/paper/2203.17031/) (2022)
- [Efficient Attention Branch Network with Combined Loss Function for Automatic Speaker Verification Spoof Detection](https://mlsanity.com/paper/2109.02051/) (2021)
- [Synthetic Voice Detection and Audio Splicing Detection using SE-Res2Net-Conformer Architecture](https://mlsanity.com/paper/2210.03581/) (2022)
- [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)
- [Deep Learning Technology for Face Forgery Detection: A Survey](https://mlsanity.com/paper/2409.14289/) (2024)
- [HuLA: Prosody-Aware Anti-Spoofing with Multi-Task Learning for Expressive and Emotional Synthetic Speech](https://mlsanity.com/paper/2509.21676/) (2025)
- [Zero-Shot to Zero-Lies: Detecting Bengali Deepfake Audio through Transfer Learning](https://mlsanity.com/paper/2512.21702/) (2025)
- [Quantum Vision Theory Applied to Audio Classification for Deepfake Speech Detection](https://mlsanity.com/paper/2604.08104/) (2026)
- [Deep-Fake CAPTCHA: Mitigating Next-Generation Social Engineering Attacks](https://mlsanity.com/paper/2609.11404/) (2026)

## Abstract (arXiv)

We present JHU's system submission to the ASVspoof 2019 Challenge: Anti-Spoofing with Squeeze-Excitation and Residual neTworks (ASSERT). Anti-spoofing has gathered more and more attention since the inauguration of the ASVspoof Challenges, and ASVspoof 2019 dedicates to address attacks from all three major types: text-to-speech, voice conversion, and replay. Built upon previous research work on Deep Neural Network (DNN), ASSERT is a pipeline for DNN-based approach to anti-spoofing. ASSERT has four components: feature engineering, DNN models, network optimization and system combination, where the DNN models are variants of squeeze-excitation and residual networks. We conducted an ablation study of the effectiveness of each component on the ASVspoof 2019 corpus, and experimental results showed that ASSERT obtained more than 93% and 17% relative improvements over the baseline systems in the two sub-challenges in ASVspooof 2019, ranking ASSERT one of the top performing systems. Code and pretrained models will be made publicly available.