With recent developments in deep learning, the ubiquity of micro-phones and the rise in online services via personal devices, acoustic side channel attacks present a greater threat to keyboards than ever. This paper presents a practical implementation of a state-of-the-art deep learning model in order to classify laptop keystrokes, using a smartphone integrated microphone. When trained on keystrokes recorded by a nearby phone, the classifier achieved an accuracy of 95%, the highest accuracy seen without the use of a language model. When trained on keystrokes recorded using the video-conferencing software Zoom, an accuracy of 93% was achieved, a new best for the medium. Our results prove the practicality of these side channel attacks via off-the-shelf equipment and algorithms. We discuss a series of mitigation methods to protect users against these series of attacks.

翻译：随着深度学习技术的进步、麦克风的普及以及个人设备上在线服务的兴起，声学侧信道攻击对键盘的威胁空前增大。本文提出了一种实用方法，利用智能手机集成麦克风，通过最先进的深度学习模型对笔记本电脑键盘击键进行分类。当使用附近手机录制的击键声进行训练时，分类器达到了95%的准确率，这是在不使用语言模型的情况下取得的最高准确率。当使用视频会议软件Zoom录制的击键声进行训练时，准确率达到93%，创下了该媒介的新纪录。我们的结果证明了利用现成设备和算法实施此类侧信道攻击的实用性。我们还讨论了一系列缓解方法，以保护用户免受此类攻击的侵害。