# Detecting Spoofing Attacks Using VGG and SincNet: BUT-Omilia Submission to ASVspoof 2019 Challenge

## TL;DR

This paper describes a system combining VGG and SincNet architectures to detect spoofing attacks in voice authentication, achieving significant improvements but facing challenges in generalizing to unseen attack types.

## Contribution

It introduces a fusion of VGG and SincNet models for spoofing detection, demonstrating competitive performance and highlighting the need for better generalization to unseen attacks.

## Key findings

- 86% relative improvement over baseline for physical access
- High performance on known spoofing attacks
- Limited generalization to unseen attack types

## Abstract

In this paper, we present the system description of the joint efforts of Brno University of Technology (BUT) and Omilia -- Conversational Intelligence for the ASVSpoof2019 Spoofing and Countermeasures Challenge. The primary submission for Physical access (PA) is a fusion of two VGG networks, trained on single and two-channels features. For Logical access (LA), our primary system is a fusion of VGG and the recently introduced SincNet architecture. The results on PA show that the proposed networks yield very competitive performance in all conditions and achieved 86\:\% relative improvement compared to the official baseline. On the other hand, the results on LA showed that although the proposed architecture and training strategy performs very well on certain spoofing attacks, it fails to generalize to certain attacks that are unseen during training.