# Introduction to Voice Presentation Attack Detection and Recent Advances

## TL;DR

This paper reviews recent advances in voice presentation attack detection for speaker recognition, highlighting progress, challenges, and the need for generalized solutions based on community benchmarking efforts.

## Contribution

It provides a comprehensive summary of recent developments, challenges, and lessons learned from community-led benchmarking efforts in voice presentation attack detection.

## Key findings

- ASVspoof challenges reveal PAD remains unsolved
- Standard benchmarks enable meaningful comparison of PAD methods
- Generalized PAD solutions are needed for diverse spoofing attacks

## Abstract

Over the past few years significant progress has been made in the field of presentation attack detection (PAD) for automatic speaker recognition (ASV). This includes the development of new speech corpora, standard evaluation protocols and advancements in front-end feature extraction and back-end classifiers. The use of standard databases and evaluation protocols has enabled for the first time the meaningful benchmarking of different PAD solutions. This chapter summarises the progress, with a focus on studies completed in the last three years. The article presents a summary of findings and lessons learned from two ASVspoof challenges, the first community-led benchmarking efforts. These show that ASV PAD remains an unsolved problem and that further attention is required to develop generalised PAD solutions which have potential to detect diverse and previously unseen spoofing attacks.