黑盒条件下使用进化多目标优化的语音识别免调整对抗样本生成
Adjust-free adversarial example generation in speech recognition using evolutionary multi-objective optimization under black-box condition
AI总结：
针对自动语音识别系统，本文提出一种黑盒对抗攻击方法，利用进化多目标优化生成对时间滞后鲁棒、无需调整播放时机的对抗样本。
摘要：
本文提出了一种针对自动语音识别系统的黑盒对抗攻击方法。已有研究尝试攻击用于语音识别的神经网络；然而，这些方法没有考虑所生成对抗样本相对于目标语音在时间滞后方面的鲁棒性。本文提出的方法采用进化多目标优化（Evolutionary Multi-objective Optimization，EMO），使其能够在黑盒场景下生成鲁棒的对抗样本。实验结果表明，该方法成功生成了免调整对抗样本，这些样本对时间滞后具有足够强的鲁棒性，因此攻击者无需考虑相对于目标语音播放它的时机。
英文摘要：
This paper proposes a black-box adversarial attack method to automatic speech recognition systems. Some studies have attempted to attack neural networks for speech recognition; however, these methods did not consider the robustness of generated adversarial examples against timing lag with a target speech. The proposed method in this paper adopts Evolutionary Multi-objective Optimization (EMO)that allows it generating robust adversarial examples under black-box scenario. Experimental results showed that the proposed method successfully generated adjust-free adversarial examples, which are sufficiently robust against timing lag so that an attacker does not need to take the timing of playing it against the target speech.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
