  Speech is a common and effective way of communication between humans, and
modern consumer devices such as smartphones and home hubs are equipped with
deep learning based accurate automatic speech recognition to enable natural
interaction between humans and machines. Recently, researchers have
demonstrated powerful attacks against machine learning models that can fool
them to produceincorrect results. However, nearly all previous research in
adversarial attacks has focused on image recognition and object detection
models. In this short paper, we present a first of its kind demonstration of
adversarial attacks against speech classification model. Our algorithm performs
targeted attacks with 87% success by adding small background noise without
having to know the underlying model parameter and architecture. Our attack only
changes the least significant bits of a subset of audio clip samples, and the
noise does not change 89% the human listener's perception of the audio clip as
evaluated in our human study.
