# ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech

arXiv: https://arxiv.org/abs/2502.08857 · HTML: https://mlsanity.com/paper/2502.08857/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2025.

## Built on by (most cited first, 26 of 26)

- [Speech DF Arena: A Leaderboard for Speech DeepFake Detection Models](https://mlsanity.com/paper/2509.02859/) (2025)
- [Nes2Net: A Lightweight Nested Architecture for Foundation Model Driven Speech Anti-spoofing](https://mlsanity.com/paper/2504.05657/) (2025)
- [Forensic deepfake audio detection using segmental speech features](https://mlsanity.com/paper/2505.13847/) (2025)
- [STOPA: A Database of Systematic VariaTion Of DeePfake Audio for Open-Set Source Tracing and Attribution](https://mlsanity.com/paper/2505.19644/) (2025)
- [WaveScat: Wavelet Scattering Front-Ends with Self-Supervised Features for Speech Deepfake Detection](https://mlsanity.com/paper/2602.02980/) (2026)
- [Investigating Stochastic Methods for Prosody Modeling in Speech Synthesis](https://mlsanity.com/paper/2507.00227/) (2025)
- [Attention-based Mixture of Experts for Robust Speech Deepfake Detection](https://mlsanity.com/paper/2509.17585/) (2025)
- [AUDDT: A Unified Benchmark Toolkit for Audio and Speech Deepfake Detectors](https://mlsanity.com/paper/2509.21597/) (2025)
- [Discrete Optimal Transport and Voice Conversion](https://mlsanity.com/paper/2505.04382/) (2025)
- [On Deepfake Voice Detection -- It's All in the Presentation](https://mlsanity.com/paper/2509.26471/) (2025)
- [An Intervention-Based Framework for Shortcut Diagnosis in Spoofing Countermeasures](https://mlsanity.com/paper/2607.03150/) (2026)
- [Discrete optimal transport is a strong audio adversarial attack](https://mlsanity.com/paper/2509.14959/) (2025)
- [BEAT2AASIST model with layer fusion for ESDD 2026 Challenge](https://mlsanity.com/paper/2512.15180/) (2025)
- [How to Label Resynthesized Audio: The Dual Role of Neural Audio Codecs in Audio Deepfake Detection](https://mlsanity.com/paper/2602.16343/) (2026)
- [Eroding Trust in Real Speech: A Large-Scale Study of Human Audio Deepfake Perception](https://mlsanity.com/paper/2605.26136/) (2026)
- [SpAArSIST: Sparsified AASIST for Efficient and Reliable Anti-Spoofing](https://mlsanity.com/paper/2606.11674/) (2026)
- [Dual-Granularity Orthogonal Disentanglement for Generalizable Audio Deepfake Detection](https://mlsanity.com/paper/2606.16532/) (2026)
- [VoxENES 2026: Benchmarking Generalization of Speech Spoofing Detectors Against LLM-Era TTS and Voice Conversion](https://mlsanity.com/paper/2607.11706/) (2026)
- [A survey of AI-generated voices and their detection](https://mlsanity.com/paper/2608.15411/) (2026)
- [From Scores to Evidence: Auditable Decisions Can Improve Speech Deepfake Detection](https://mlsanity.com/paper/2609.08899/) (2026)
- [Not Quite My Tempo: Voice Activity-aware Speech Synthesis for Lip-Synchronous Dubbing](https://mlsanity.com/paper/2609.26486/) (2026)
- [Spooftral: Can Voxtral Audio-Language Model Detect Speech Spoofing?](https://mlsanity.com/paper/2609.28713/) (2026)
- [Learning as Deepfakes Evolve: RF-Prompt for Continual Audio Deepfake Detection](https://mlsanity.com/paper/2609.37586/) (2026)
- [SEAR: Spoofing Evidence-Grounded Audio Reasoning Benchmark for Audio Language Models](https://mlsanity.com/paper/2609.39847/) (2026)
- [On Evaluating Quantum Kernel Robustness for Low-Resource Cross-Corpus Audio Deepfake Detection](https://mlsanity.com/paper/2610.00649/) (2026)
- [QBFF: Deep Quantum Bottleneck for Automatic Speaker Verification Anti-Spoofing](https://mlsanity.com/paper/2610.16390/) (2026)

## Abstract (arXiv)

ASVspoof 5 is the fifth edition in a series of challenges which promote the study of speech spoofing and deepfake attacks as well as the design of detection solutions. We introduce the ASVspoof 5 database which is generated in a crowdsourced fashion from data collected in diverse acoustic conditions (cf. studio-quality data for earlier ASVspoof databases) and from ~2,000 speakers (cf. ~100 earlier). The database contains attacks generated with 32 different algorithms, also crowdsourced, and optimised to varying degrees using new surrogate detection models. Among them are attacks generated with a mix of legacy and contemporary text-to-speech synthesis and voice conversion models, in addition to adversarial attacks which are incorporated for the first time. ASVspoof 5 protocols comprise seven speaker-disjoint partitions. They include two distinct partitions for the training of different sets of attack models, two more for the development and evaluation of surrogate detection models, and then three additional partitions which comprise the ASVspoof 5 training, development and evaluation sets. An auxiliary set of data collected from an additional 30k speakers can also be used to train speaker encoders for the implementation of attack algorithms. Also described herein is an experimental validation of the new ASVspoof 5 database using a set of automatic speaker verification and spoof/deepfake baseline detectors. With the exception of protocols and tools for the generation of spoofed/deepfake speech, the resources described in this paper, already used by participants of the ASVspoof 5 challenge in 2024, are now all freely available to the community.