  In this work, we investigate the concept of biometric backdoors: a template
poisoning attack on biometric systems that allows adversaries to stealthily and
effortlessly impersonate users in the long-term by exploiting the template
update procedure. We show that such attacks can be carried out even by
attackers with physical limitations (no digital access to the sensor) and zero
knowledge of training data (they know neither decision boundaries nor user
template). Based on the adversaries' own templates, they craft several
intermediate samples that incrementally bridge the distance between their own
template and the legitimate user's. As these adversarial samples are added to
the template, the attacker is eventually accepted alongside the legitimate
user. To avoid detection, we design the attack to minimize the number of
rejected samples.
  We design our method to cope with the weak assumptions for the attacker and
we evaluate the effectiveness of this approach on state-of-the-art face
recognition pipelines based on deep neural networks. We find that in scenarios
where the deep network is known, adversaries can successfully carry out the
attack over 70% of cases with less than ten injection attempts. Even in
black-box scenarios, we find that exploiting the transferability of adversarial
samples from surrogate models can lead to successful attacks in around 15% of
cases. Finally, we design a poisoning detection technique that leverages the
consistent directionality of template updates in feature space to discriminate
between legitimate and malicious updates. We evaluate such a countermeasure
with a set of intra-user variability factors which may present the same
directionality characteristics, obtaining equal error rates for the detection
between 7-14% and leading to over 99% of attacks being detected after only two
sample injections.
