# Deep Residual Neural Networks for Audio Spoofing Detection

arXiv: https://arxiv.org/abs/1907.00501 · HTML: https://mlsanity.com/paper/1907.00501/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2019.

## Built on by (most cited first, 30 of 33)

- [Does Audio Deepfake Detection Generalize?](https://mlsanity.com/paper/2203.16263/) (2022)
- [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)
- [Human Perception of Audio Deepfakes](https://mlsanity.com/paper/2107.09667/) (2021)
- [I Can Hear You: Selective Robust Training for Deepfake Audio Detection](https://mlsanity.com/paper/2411.00121/) (2024)
- [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)
- [FairSSD: Understanding Bias in Synthetic Speech Detectors](https://mlsanity.com/paper/2404.10989/) (2024)
- [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)
- [Forensic deepfake audio detection using segmental speech features](https://mlsanity.com/paper/2505.13847/) (2025)
- [Trusted Media Challenge Dataset and User Study](https://mlsanity.com/paper/2201.04788/) (2022)
- [Deepfake CAPTCHA: A Method for Preventing Fake Calls](https://mlsanity.com/paper/2301.03064/) (2023)
- [Complex-valued neural networks for voice anti-spoofing](https://mlsanity.com/paper/2308.11800/) (2023)
- [FMFCC-A: A Challenging Mandarin Dataset for Synthetic Speech Detection](https://mlsanity.com/paper/2110.09441/) (2021)
- [How Deep Are the Fakes? Focusing on Audio Deepfake: A Survey](https://mlsanity.com/paper/2111.14203/) (2021)
- [Attacker Attribution of Audio Deepfakes](https://mlsanity.com/paper/2203.15563/) (2022)
- [Combining Automatic Speaker Verification and Prosody Analysis for Synthetic Speech Detection](https://mlsanity.com/paper/2210.17222/) (2022)
- [Speech-Forensics: Towards Comprehensive Synthetic Speech Dataset Establishment and Analysis](https://mlsanity.com/paper/2412.09032/) (2024)
- [RW-Resnet: A Novel Speech Anti-Spoofing Model Using Raw Waveform](https://mlsanity.com/paper/2108.05684/) (2021)
- [DiffSSD: A Diffusion-Based Dataset For Speech Forensics](https://mlsanity.com/paper/2409.13049/) (2024)
- [Passive Deepfake Detection Across Multi-modalities: A Comprehensive Survey](https://mlsanity.com/paper/2411.17911/) (2024)
- [Phase-Aware Spoof Speech Detection Based on Res2Net with Phase Network](https://mlsanity.com/paper/2203.10793/) (2022)
- [All-for-One and One-For-All: Deep learning-based feature fusion for Synthetic Speech Detection](https://mlsanity.com/paper/2307.15555/) (2023)
- [Lightweight Model Attribution and Detection of Synthetic Speech via Audio Residual Fingerprints](https://mlsanity.com/paper/2411.14013/) (2024)
- [Efficient Attention Branch Network with Combined Loss Function for Automatic Speaker Verification Spoof Detection](https://mlsanity.com/paper/2109.02051/) (2021)
- [ConvNeXt Based Neural Network for Audio Anti-Spoofing](https://mlsanity.com/paper/2209.06434/) (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)
- [DeePen: Penetration Testing for Audio Deepfake Detection](https://mlsanity.com/paper/2502.20427/) (2025)
- [Towards the Development of a Real-Time Deepfake Audio Detection System in Communication Platforms](https://mlsanity.com/paper/2403.11778/) (2024)

## Abstract (arXiv)

The state-of-art models for speech synthesis and voice conversion are capable of generating synthetic speech that is perceptually indistinguishable from bonafide human speech. These methods represent a threat to the automatic speaker verification (ASV) systems. Additionally, replay attacks where the attacker uses a speaker to replay a previously recorded genuine human speech are also possible. We present our solution for the ASVSpoof2019 competition, which aims to develop countermeasure systems that distinguish between spoofing attacks and genuine speeches. Our model is inspired by the success of residual convolutional networks in many classification tasks. We build three variants of a residual convolutional neural network that accept different feature representations (MFCC, Log-magnitude STFT, and CQCC) of input. We compare the performance achieved by our model variants and the competition baseline models. In the logical access scenario, the fusion of our models has zero t-DCF cost and zero equal error rate (EER), as evaluated on the development set. On the evaluation set, our model fusion improves the t-DCF and EER by 25% compared to the baseline algorithms. Against physical access replay attacks, our model fusion improves the baseline algorithms t-DCF and EER scores by 71% and 75% on the evaluation set, respectively.