EAGER: Automatic Reconstruction of Typed Input from Compromising Reflections

EAGER：从妥协的反射中自动重建键入的输入

• 批准号: 1148895
• 负责人: Jan-Michael Frahm
• 金额: $ 15.17万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1148895&HistoricalAwards=false
• 关键词: EAGER; Automatic; Reconstruction; Typed; Input

This project explores computer vision techniques aimed at exploiting compromising reflections associated with data input in mobile electronic devices such as smart phones. The ubiquity of these personal communication devices and their growing roles in data manipulation tasks, make unintended visual emanations an exploitable liability to data security. Nevertheless, there is still a gap in understanding of both the limitations of these techniques as well as the availability of effective mitigation mechanisms. It is the goal of this work to contribute to filling this conceptual gap.The study builds upon recent state of the art techniques for automatic reconstruction of typed input from compromising reflections, comprising of robust keystroke event detection and classification mechanisms coupled to natural language processing modules. Such paradigm is both effective and amenable to low cost implementation in commodity devices. Based on these new developments, threat scenarios are no longer restricted to controlled scenarios using specialized equipment, but rather consist of highly flexible and possibly impromptu attacks. The project develops advanced cross-platform data input transcription prototypes used within a threat validation framework. This framework provides a characterization of both threat scenario operational limitations (e.g., imaging resolution, scene illumination, computational requirements) as well as the performance characteristics (e.g., robustness, accuracy) of the different vulnerability exploitation mechanisms. Moreover, the results of the analysis of diverse threat scenarios are being used to identify and develop appropriate mitigation mechanisms when possible.

该项目探索计算机视觉技术，旨在利用与智能手机等移动的电子设备中的数据输入相关的损害反射。 这些个人通信设备的普遍存在及其在数据操作任务中日益增长的作用，使非预期的视觉辐射成为数据安全的可利用责任。然而，在理解这些技术的局限性以及有效缓解机制的可用性方面仍然存在差距。这是这项工作的目标，有助于填补这一概念gap.The研究建立在最近的最先进的技术，自动重建类型的输入从妥协的反射，包括强大的事件检测和分类机制耦合到自然语言处理模块。这样的范例既有效又适合于在商品设备中的低成本实现。基于这些新的发展，威胁情景不再局限于使用专门设备的受控情景，而是包括高度灵活和可能的即兴攻击。该项目开发了在威胁验证框架内使用的高级跨平台数据输入转录原型。该框架提供了威胁场景操作限制（例如，成像分辨率、场景照明、计算要求）以及性能特性（例如，鲁棒性、准确性）。此外，正在利用对各种威胁情况的分析结果，尽可能确定和制定适当的缓解机制。