Screen Gleaning: A Screen Reading TEMPEST Attack on Mobile Devices Exploiting an Electromagnetic Side Channel
Zhuoran Liu, Niels Samwel, L\'eo Weissbart, Zhengyu Zhao, Dirk Lauret,, Lejla Batina, Martha Larson

TL;DR
This paper demonstrates a novel electromagnetic side-channel attack on mobile device screens using SDRs and deep learning to reconstruct sensitive information without visual access, highlighting security risks and potential defenses.
Contribution
It introduces the screen gleaning TEMPEST attack, combining electromagnetic signal capture with deep learning to read screen content remotely, and provides a testbed for evaluating such attacks.
Findings
Successfully reconstructed security codes from electromagnetic signals
Deep learning classifiers effectively interpret emages
Attack feasibility increases with SDR and deep learning advancements
Abstract
We introduce screen gleaning, a TEMPEST attack in which the screen of a mobile device is read without a visual line of sight, revealing sensitive information displayed on the phone screen. The screen gleaning attack uses an antenna and a software-defined radio (SDR) to pick up the electromagnetic signal that the device sends to the screen to display, e.g., a message with a security code. This special equipment makes it possible to recreate the signal as a gray-scale image, which we refer to as an emage. Here, we show that it can be used to read a security code. The screen gleaning attack is challenging because it is often impossible for a human viewer to interpret the emage directly. We show that this challenge can be addressed with machine learning, specifically, a deep learning classifier. Screen gleaning will become increasingly serious as SDRs and deep learning continue to rapidly…
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Taxonomy
TopicsCryptographic Implementations and Security · Advanced Malware Detection Techniques · User Authentication and Security Systems
