  We propose a method to generate audio adversarial examples that can attack a
state-of-the-art speech recognition model in the physical world. Previous work
assumes that generated adversarial examples are directly fed to the recognition
model, and is not able to perform such a physical attack because of
reverberation and noise from playback environments. In contrast, our method
obtains robust adversarial examples by simulating transformations caused by
playback or recording in the physical world and incorporating the
transformations into the generation process. Evaluation and a listening
experiment demonstrated that our adversarial examples are able to attack
without being noticed by humans. This result suggests that audio adversarial
examples generated by the proposed method may become a real threat.
