Unraveling Adversarial Examples against Speaker Identification -- Techniques for Attack Detection and Victim Model Classification
<|reference_start|>Unraveling Adversarial Examples against Speaker Identification -- Techniques for Attack Detection and Victim Model Classification: 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.<|reference_end|>
@article{joshi2024unraveling,
    title={Unraveling Adversarial Examples against Speaker Identification --
  Techniques for Attack Detection and Victim Model Classification},
    author={Sonal Joshi, Thomas Thebaud, Jes'us Villalba, Najim Dehak},
    journal={arXiv preprint arXiv:2402.19355},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.19355},
    primaryClass={cs.SD cs.CR cs.LG eess.AS}
}