Advanced Techniques for Preventing Thermal Imaging Attacks
Advanced Techniques for Preventing Thermal Imaging Attacks
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DOI：
10.1145/3490100.3516472
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
N. Alotaibi;M. Islam;Karola Marky;Mohamed Khamis-
中科院分区：
文献类型：
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作者：
N. Alotaibi;M. Islam;Karola Marky;Mohamed Khamis-
热感相机可用于通过记录用户手指在交互之后留下的热迹（例如，键入消息或输入PIN）并使用它们来重构输入。虽然以前的工作通过复杂化输入或扭曲热迹线来减轻热攻击，但我们的研究是第一个提出使用深度学习（DL）技术来防止热攻击的研究，以防止恶意使用热像仪。我们的DL模型检测热成像摄像机馈送中的接口，然后混淆它们上的热痕迹。我们的初步研究结果表明，该框架可以检测接口和消除认证信息从热图像。与此同时，我们的方法仍然可以显示是否与接口进行了交互。因此，我们的方法提高了安全性，而不影响热成像相机的实用性。
Thermal cameras can be used to detect user input on interfaces, such as touchscreens, keyboards, and PIN pads, by recording the heat traces left by the users’ fingers after interaction (e.g., typing a message or entering a PIN) and using them to reconstruct the input. While previous work mitigated the thermal attacks by complicating input or distorting heat traces, our research is the first to propose preventing thermal attack using deep learning (DL) techniques to prevent malicious use of thermal cameras. Our DL models detect interfaces in the thermal camera feed and then obfuscate heat traces on them. Our preliminary findings show that the proposed framework can detect interfaces and eliminate authentication information from thermal images. At the same time, our methods still reveal if an interface has been interacted with. Thus, our approach improves security without impacting the utility of the thermal camera.