Attentive Filtering Networks for Audio Replay Attack Detection

ABSTRACT An attacker may use a variety of techniques to fool an automatic speaker verification system into accepting them as a genuine user. Anti-spoofing methods meanwhile aim to make the system robust against such attacks. The ASVspoof 2017 Challenge focused specifically on replay attacks, with the intention of measuring the limits of replay attack detection as well as developing countermeasures against them. In this work, we propose our replay attacks detection system - Attentive Filtering Network, which is composed of an attention-based filtering mechanism that enhances feature representations in both the frequency and time domains, and a ResNet-based classifier. We show that the network enables us to visualize the automatically acquired feature representations that are helpful for spoofing detection. Index Terms-- ASVspoof, Anti-Spoofing, Spoofing Attack, Replay Attacks, Automatic Speaker Verification 1. INTRODUCTION Automatic speaker verification (ASV) systems have become increasingly widespread in recent years with the advent of voice assistant and smart home devices.