Symmetric Saliency-based Adversarial Attack To Speaker Identification. (arXiv:2210.16777v1 [cs.SD])

Adversarial attack approaches to speaker identification either need high
computational cost or are not very effective, to our knowledge. To address this
issue, in this paper, we propose a novel generation-network-based approach,
called symmetric saliency-based encoder-decoder (SSED), to generate adversarial
voice examples to speaker identification. It contains two novel components.
First, it uses a novel saliency map decoder to learn the importance of speech
samples to the decision of a targeted speaker identification system, so as to
make the attacker …

adversarial attack identification