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.

提出了一种检测对抗性示例的方法，基于新的架构进行攻击类型分类并介绍一种对受害模型进行识别的方法，获得了较高的检测准确率和分类准确率。