When the Differences in Frequency Domain are Compensated: Understanding and Defeating Modulated Replay Attacks on Automatic Speech Recognition

09/01/2020
by   Shu Wang, et al.
0

Automatic speech recognition (ASR) systems have been widely deployed in modern smart devices to provide convenient and diverse voice-controlled services. Since ASR systems are vulnerable to audio replay attacks that can spoof and mislead ASR systems, a number of defense systems have been proposed to identify replayed audio signals based on the speakers' unique acoustic features in the frequency domain. In this paper, we uncover a new type of replay attack called modulated replay attack, which can bypass the existing frequency domain based defense systems. The basic idea is to compensate for the frequency distortion of a given electronic speaker using an inverse filter that is customized to the speaker's transform characteristics. Our experiments on real smart devices confirm the modulated replay attacks can successfully escape the existing detection mechanisms that rely on identifying suspicious features in the frequency domain. To defeat modulated replay attacks, we design and implement a countermeasure named DualGuard. We discover and formally prove that no matter how the replay audio signals could be modulated, the replay attacks will either leave ringing artifacts in the time domain or cause spectrum distortion in the frequency domain. Therefore, by jointly checking suspicious features in both frequency and time domains, DualGuard can successfully detect various replay attacks including the modulated replay attacks. We implement a prototype of DualGuard on a popular voice interactive platform, ReSpeaker Core v2. The experimental results show DualGuard can achieve 98 detecting modulated replay attacks.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 8

06/02/2020

Detecting Audio Attacks on ASR Systems with Dropout Uncertainty

Various adversarial audio attacks have recently been developed to fool a...
04/13/2019

Towards Vulnerability Analysis of Voice-Driven Interfaces and Countermeasures for Replay

Fake audio detection is expected to become an important research area in...
09/03/2019

Voice Spoofing Detection Corpus for Single and Multi-order Audio Replays

The evolution of modern voice controlled devices (VCDs) in recent years ...
03/18/2020

Detecting Replay Attacks Using Multi-Channel Audio: A Neural Network-Based Method

With the rapidly growing number of security-sensitive systems that use v...
01/31/2019

Discriminate natural versus loudspeaker emitted speech

In this work, we address a novel, but potentially emerging, problem of d...
06/27/2021

Open, Sesame! Introducing Access Control to Voice Services

Personal voice assistants (VAs) are shown to be vulnerable against recor...
04/25/2022

Understanding Audio Features via Trainable Basis Functions

In this paper we explore the possibility of maximizing the information r...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.