标题： 针对自动语音识别系统的选择性屏蔽对抗攻击 显示英文标题

标题： Selective Masking Adversarial Attack on Automatic Speech Recognition Systems

摘要： 大量研究表明，自动语音识别（ASR）系统容易受到音频对抗攻击的威胁。目前的攻击主要集中在单源场景上，而忽略了双源场景（即两人同时说话的情况）。为了弥补这一差距，我们提出了一种选择性掩蔽对抗攻击方法，即SMA攻击，该方法确保在双源场景中选择一个音频源用于识别，同时静音另一个音频源。为了更好地适应双源场景，我们的SMA攻击通过从静音音频和选定音频构建正常的双源音频。SMA攻击以小高斯噪声初始化对抗扰动，并使用选择性掩蔽优化算法对其进行迭代优化。广泛的实验表明，SMA攻击能够在双源场景中生成有效且难以察觉的音频对抗样本，在Conformer-CTC上的平均攻击成功率为100%，信噪比为37.15 dB，优于基线方法。 显示英文摘要

摘要： Extensive research has shown that Automatic Speech Recognition (ASR) systems are vulnerable to audio adversarial attacks. Current attacks mainly focus on single-source scenarios, ignoring dual-source scenarios where two people are speaking simultaneously. To bridge the gap, we propose a Selective Masking Adversarial attack, namely SMA attack, which ensures that one audio source is selected for recognition while the other audio source is muted in dual-source scenarios. To better adapt to the dual-source scenario, our SMA attack constructs the normal dual-source audio from the muted audio and selected audio. SMA attack initializes the adversarial perturbation with a small Gaussian noise and iteratively optimizes it using a selective masking optimization algorithm. Extensive experiments demonstrate that the SMA attack can generate effective and imperceptible audio adversarial examples in the dual-source scenario, achieving an average success rate of attack of 100% and signal-to-noise ratio of 37.15dB on Conformer-CTC, outperforming the baselines.