Symmetric Saliency-Based Adversarial Attack to Speaker Identification

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Symmetric Saliency-Based Adversarial Attack to Speaker Identification

By: 
Jiadi Yao; Xing Chen; Xiao-Lei Zhang; Wei-Qiang Zhang; Kunde Yang

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 letter, 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.

Introduction

Speaker recognition is vulnerable to spoofing attacks [1]. Many spoofing attack techniques to speaker recognition, including replay, voice conversion, impersonation and text-to-speech synthesis, and adversarial attacks [2], have been developed. On the contrary, various detection [3][4][5] and countermeasures [6] against spoofing attacks are in full swing. In this letter, we focus on developing adversarial attacks to speaker identification. An adversarial attack to speaker identification aims to make an identification system wrongly recognize the adversarial voice of a source speaker as a targeted imposter speaker, where the adversarial voice, a.k.a. adversarial example, is produced by adding human-imperceptible noise to the speech of the source speaker. It shows great threat to modern speaker identification systems based on deep learning.

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