论文标题
对扬声器验证的字典攻击
Dictionary Attacks on Speaker Verification
论文作者
论文摘要
在本文中，我们提出了针对说话者验证的字典攻击 - 一种新颖的攻击向量，旨在偶然地与大量的说话者人口相匹配。我们介绍了攻击的通用表述，可以与各种语音表示和威胁模型一起使用。攻击者使用对抗性优化来最大程度地提高种子语音样本和代理人之间的扬声器嵌入的原始相似性。由此产生的大师声音成功地匹配了未知人群中人群中的非平凡的一部分。通过我们的方法获得的对抗波形平均可以匹配69％的女性和38％的男性，其中38％的男性以严格的决策阈值校准，以产生1％的错误警报率。通过将攻击与黑框语音克隆系统一起使用，我们获得了在最具挑战性的条件下有效并在说话者编码之间转移的主声音。我们还表明，与多次尝试相结合，这次攻击更加对这些系统安全性的严重问题开放。
In this paper, we propose dictionary attacks against speaker verification - a novel attack vector that aims to match a large fraction of speaker population by chance. We introduce a generic formulation of the attack that can be used with various speech representations and threat models. The attacker uses adversarial optimization to maximize raw similarity of speaker embeddings between a seed speech sample and a proxy population. The resulting master voice successfully matches a non-trivial fraction of people in an unknown population. Adversarial waveforms obtained with our approach can match on average 69% of females and 38% of males enrolled in the target system at a strict decision threshold calibrated to yield false alarm rate of 1%. By using the attack with a black-box voice cloning system, we obtain master voices that are effective in the most challenging conditions and transferable between speaker encoders. We also show that, combined with multiple attempts, this attack opens even more to serious issues on the security of these systems.
