  Voice authentication has become an integral part in security-critical
operations, such as bank transactions and call center conversations. The
vulnerability of automatic speaker verification systems (ASVs) to spoofing
attacks instigated the development of countermeasures (CMs), whose task is to
tell apart bonafide and spoofed speech. Together, ASVs and CMs form today's
voice authentication platforms, advertised as an impregnable access control
mechanism. We develop the first practical attack on CMs, and show how a
malicious actor may efficiently craft audio samples to bypass voice
authentication in its strictest form. Previous works have primarily focused on
non-proactive attacks or adversarial strategies against ASVs that do not
produce speech in the victim's voice. The repercussions of our attacks are far
more severe, as the samples we generate sound like the victim, eliminating any
chance of plausible deniability. Moreover, the few existing adversarial attacks
against CMs mistakenly optimize spoofed speech in the feature space and do not
take into account the existence of ASVs, resulting in inferior synthetic audio
that fails in realistic settings. We eliminate these obstacles through our key
technical contribution: a novel joint loss function that enables mounting
advanced adversarial attacks against combined ASV/CM deployments directly in
the time domain. Our adversarials achieve concerning black-box success rates
against state-of-the-art authentication platforms (up to 93.57\%). Finally, we
perform the first targeted, over-telephony-network attack on CMs, bypassing
several challenges and enabling various potential threats, given the increased
use of voice biometrics in call centers. Our results call into question the
security of modern voice authentication systems in light of the real threat of
attackers bypassing these measures to gain access to users' most valuable
resources.

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