  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% accuracy on
detecting modulated replay attacks.
