保护语音控制设备免受LASER注入攻击
Protecting Voice-Controlled Devices against LASER Injection Attacks
AI总结：
针对MEMS麦克风易受激光注入攻击的问题，本文提出基于离散小波变换与高阶统计特征、利用SVM分类器的检测框架，在190条音频上实现98%至100%的分类准确率。
摘要：
语音控制设备（VCDs）由于MEMS麦克风的小尺寸外形及其与现代小工具的易于集成，其采用率呈现日益增长的趋势。近期研究表明，MEMS麦克风易受音频调制的激光注入攻击。本文旨在开发对策，以检测并防止针对MEMS麦克风的激光注入攻击。采用基于离散小波变换（DWT）的时频分解方法，将麦克风输出音频信号分解为n+1个频率子带，以捕获光声相关伪影。利用高阶统计特征，即子带音频信号的前四阶矩（如方差、偏度和峰度），来区分声学响应与光声响应。使用SVM分类器学习底层模型，以区分MEMS麦克风中声学诱发响应与激光诱发（光声）响应。所提出的框架在包含19位说话人的190条音频数据集上进行了评估。实验结果表明，在随机数据划分设置下，该框架能够正确分类98%的声学诱发和激光诱发音频；在说话人无关和文本无关的数据划分设置下，能够正确分类100%的音频。
英文摘要：
Voice-Controllable Devices (VCDs) have seen an increasing trend towards their adoption due to the small form factor of the MEMS microphones and their easy integration into modern gadgets. Recent studies have revealed that MEMS microphones are vulnerable to audio-modulated laser injection attacks. This paper aims to develop countermeasures to detect and prevent laser injection attacks on MEMS microphones. A time-frequency decomposition based on discrete wavelet transform (DWT) is employed to decompose microphone output audio signal into n + 1 frequency subbands to capture photo-acoustic related artifacts. Higher-order statistical features consisting of the first four moments of subband audio signals, e.g., variance, skew, and kurtosis are used to distinguish between acoustic and photo-acoustic responses. An SVM classifier is used to learn the underlying model that differentiates between an acoustic- and laser-induced (photo-acoustic) response in the MEMS microphone. The proposed framework is evaluated on a data set of 190 audios, consisting of 19 speakers. The experimental results indicate that the proposed framework is able to correctly classify $98\%$ of the acoustic- and laser-induced audio in a random data partition setting and $100\%$ of the audio in speaker-independent and text-independent data partition settings.
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。
