(sp)iPhone: decoding vibrations from nearby keyboards using mobile phone accelerometers

Philip Marquardt, A. Verma, Henry Carter, Patrick Traynor
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引用次数: 288

Abstract

Mobile phones are increasingly equipped with a range of highly responsive sensors. From cameras and GPS receivers to three-axis accelerometers, applications running on these devices are able to experience rich interactions with their environment. Unfortunately, some applications may be able to use such sensors to monitor their surroundings in unintended ways. In this paper, we demonstrate that an application with access to accelerometer readings on a modern mobile phone can use such information to recover text entered on a nearby keyboard. Note that unlike previous emanation recovery papers, the accelerometers on such devices sample at near the Nyquist rate, making previous techniques unworkable. Our application instead detects and decodes keystrokes by measuring the relative physical position and distance between each vibration. We then match abstracted words against candidate dictionaries and record word recovery rates as high as 80%. In so doing, we demonstrate the potential to recover significant information from the vicinity of a mobile device without gaining access to resources generally considered to be the most likely sources of leakage (e.g., microphone, camera).
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(sp)iPhone:使用手机加速度计解码附近键盘的振动
移动电话越来越多地配备了一系列高响应传感器。从相机和GPS接收器到三轴加速度计,在这些设备上运行的应用程序能够与环境进行丰富的交互。不幸的是,一些应用程序可能会使用这种传感器以意想不到的方式监控周围环境。在本文中,我们证明了一个应用程序可以访问现代移动电话上的加速度计读数,可以使用这些信息来恢复在附近键盘上输入的文本。请注意,与以前的辐射恢复论文不同,这种设备上的加速度计的采样接近奈奎斯特速率,这使得以前的技术不可行。我们的应用程序通过测量每次振动之间的相对物理位置和距离来检测和解码击键。然后,我们将抽象的单词与候选字典进行匹配,并记录单词恢复率高达80%。在这样做的过程中,我们展示了从移动设备附近恢复重要信息的潜力,而无需访问通常被认为是最有可能的泄漏源的资源(例如,麦克风,相机)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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期刊最新文献
WristPrint: Characterizing User Re-identification Risks from Wrist-worn Accelerometry Data. CCS '21: 2021 ACM SIGSAC Conference on Computer and Communications Security, Virtual Event, Republic of Korea, November 15 - 19, 2021 WAHC '21: Proceedings of the 9th on Workshop on Encrypted Computing & Applied Homomorphic Cryptography, Virtual Event, Korea, 15 November 2021 Incremental Learning Algorithm of Data Complexity Based on KNN Classifier How to Accurately and Privately Identify Anomalies.
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