  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 focus on generating artificial noise to the important
samples. It also proposes an angular loss function to push the speaker
embedding far away from the source speaker. Our experimental results
demonstrate that the proposed SSED yields the state-of-the-art performance,
i.e. over 97% targeted attack success rate and a signal-to-noise level of over
39 dB on both the open-set and close-set speaker identification tasks, with a
low computational cost.
