  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.
