Unraveling Adversarial Examples against Speaker Identification --
  Techniques for Attack Detection and Victim Model Classification
Sonal Joshi Thomas Thebaud Jesús Villalba Najim Dehak
Adversarial examples have proven to threaten speaker identification systems,
and several countermeasures against them have been proposed. In this paper, we
propose a method to detect the presence of adversarial examples, i.e., a binary
classifier distinguishing between benign and adversarial examples. We build
upon and extend previous work on attack type classification by exploring new
architectures. Additionally, we introduce a method for identifying the victim
model on which the adversarial attack is carried out. To achieve this, we
generate a new dataset containing multiple attacks performed against various
victim models. We achieve an AUC of 0.982 for attack detection, with no more
than a 0.03 drop in performance for unknown attacks. Our attack classification
accuracy (excluding benign) reaches 86.48% across eight attack types using our
LightResNet34 architecture, while our victim model classification accuracy
reaches 72.28% across four victim models.