# ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection

## TL;DR

The ASVspoof 2019 challenge advances spoofed and fake audio detection by including diverse attack scenarios, state-of-the-art generation models, and improved evaluation metrics, fostering progress in protecting speaker verification systems.

## Contribution

This paper introduces the 2019 ASVspoof database, protocols, and challenge results, highlighting significant progress in spoofing countermeasures and fake audio detection techniques.

## Key findings

- Over 60 research teams participated, with many surpassing baseline systems.
- Inclusion of multiple attack types and advanced neural models improved detection.
- Use of t-DCF metric provided a comprehensive assessment of system performance.

## Abstract

ASVspoof, now in its third edition, is a series of community-led challenges which promote the development of countermeasures to protect automatic speaker verification (ASV) from the threat of spoofing. Advances in the 2019 edition include: (i) a consideration of both logical access (LA) and physical access (PA) scenarios and the three major forms of spoofing attack, namely synthetic, converted and replayed speech; (ii) spoofing attacks generated with state-of-the-art neural acoustic and waveform models; (iii) an improved, controlled simulation of replay attacks; (iv) use of the tandem detection cost function (t-DCF) that reflects the impact of both spoofing and countermeasures upon ASV reliability. Even if ASV remains the core focus, in retaining the equal error rate (EER) as a secondary metric, ASYspoof also embraces the growing importance of fake audio detection. ASVspoof 2019 attracted the participation of 63 research teams, with more than half of these reporting systems that improve upon the performance of two baseline spoofing countermeasures. This paper describes the 2019 database, protocols and challenge results. It also outlines major findings which demonstrate the real progress made in protecting against the threat of spoofing and fake audio.