  Recent years have seen a surge in the popularity of acoustics-enabled
personal devices powered by machine learning. Yet, machine learning has proven
to be vulnerable to adversarial examples. A large number of modern systems
protect themselves against such attacks by targeting artificiality, i.e., they
deploy mechanisms to detect the lack of human involvement in generating the
adversarial examples. However, these defenses implicitly assume that humans are
incapable of producing meaningful and targeted adversarial examples. In this
paper, we show that this base assumption is wrong. In particular, we
demonstrate that for tasks like speaker identification, a human is capable of
producing analog adversarial examples directly with little cost and
supervision: by simply speaking through a tube, an adversary reliably
impersonates other speakers in eyes of ML models for speaker identification.
Our findings extend to a range of other acoustic-biometric tasks such as
liveness detection, bringing into question their use in security-critical
settings in real life, such as phone banking.

Machine Learning;Cryptography and Security;Sound;Audio and Speech Processing