  Adversarial examples are inputs to machine learning models designed by an
adversary to cause an incorrect output. So far, adversarial examples have been
studied most extensively in the image domain. In this domain, adversarial
examples can be constructed by imperceptibly modifying images to cause
misclassification, and are practical in the physical world. In contrast,
current targeted adversarial examples applied to speech recognition systems
have neither of these properties: humans can easily identify the adversarial
perturbations, and they are not effective when played over-the-air. This paper
makes advances on both of these fronts. First, we develop effectively
imperceptible audio adversarial examples (verified through a human study) by
leveraging the psychoacoustic principle of auditory masking, while retaining
100% targeted success rate on arbitrary full-sentence targets. Next, we make
progress towards physical-world over-the-air audio adversarial examples by
constructing perturbations which remain effective even after applying realistic
simulated environmental distortions.
