Detecting Cellphone Camera Status at Distance by Exploiting Electromagnetic Emanations

Yilmaz, Baki Berkay; Ugurlu, Elvan Mert; Prvulovic, Milos; Zajić, Alenka

doi:10.1109/milcom47813.2019.9021060

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…Our work draws inspiration from recent works showing that embedded cameras' electromagnetic (EM) emissions allow people to detect the presence of cameras [25], [34], [41]. While these works simply use the existence of EM emissions as an on/off indicator of camera operations, essentially extracting a single bit of information, our work further investigates how much information of camera data is leaked from such EM emissions 1 , and how adversaries can eavesdrop on the camera image streams by reconstructing synthesized images from the EM signals.…”

Section: New Dimension Of the Problem By Asking How May Adversaries E...mentioning

confidence: 99%

“…This hypothesis is motivated by recent research discoveries of the EM characteristics of embedded cameras. Several works have shown that smartphone cameras and hidden spy cameras produce EM emissions when they are turned on, allowing people to detect forbidden malicious operations of these cameras [25], [34], [41]. Essentially, these works only extract a single bit of entropy (on/off) from camera EM emissions.…”

Section: A Prior Workmentioning

confidence: 99%

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EM Eye: Characterizing Electromagnetic Side-channel Eavesdropping on Embedded Cameras

Long,

Jiang,

Yan

et al. 2024

Proceedings 2024 Network and Distributed System Security Symposium

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IoT devices and other embedded systems are increasingly equipped with cameras that can sense critical information in private spaces. The data security of these cameras, however, has hardly been scrutinized from the hardware design perspective. Our paper presents the first attempt to analyze the attack surface of physical-channel eavesdropping on embedded cameras. We characterize EM Eye-a vulnerability in the digital image data transmission interface that allows adversaries to reconstruct high-quality image streams from the cameras' unintentional electromagnetic emissions, even from over 2 meters away in many cases. Our evaluations of 4 popular IoT camera development platforms and 12 commercial off-the-shelf devices with cameras show that EM Eye poses threats to a wide range of devices, from smartphones to dash cams and home security cameras. By exploiting this vulnerability, adversaries may be able to visually spy on private activities in an enclosed room from the other side of a wall. We provide root cause analysis and modeling that enable system defenders to identify and simulate mitigation against this vulnerability, such as improving embedded cameras' data transmission protocols with minimum costs. We further discuss EM Eye's relationship with known computer display eavesdropping attacks to reveal the gaps that need to be addressed to protect the data confidentiality of sensing systems.

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Section: New Dimension Of the Problem By Asking How May Adversaries E...mentioning

confidence: 99%

Section: A Prior Workmentioning

confidence: 99%

EM Eye: Characterizing Electromagnetic Side-channel Eavesdropping on Embedded Cameras

Long,

Jiang,

Yan

et al. 2024

Proceedings 2024 Network and Distributed System Security Symposium

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“…The identification of such devices during the triage examination phase can help law-enforcement to make timely interventions. Yilmaz et al explored the potential of detecting smartphone camera status through their EM radiation patterns [15]. Their work acquires EM radiation coming from the system-on-chip (SoC) clock frequency of each considered smartphone type and later uses two-stage dimensionality reduction approach to select a feature vector.…”

Section: Related Workmentioning

confidence: 99%

“…A smartphone can be producing EM radiation from various internal hardware components in various signal frequencies. As the information related to the ongoing activities of a smartphone is associated with its SoC chip, the system clock frequency of the SoC chip can be considered as the most important EM frequency to observe [15]. In order to locate the most appropriate location to observe EM radiation from the exterior of a smartphone, the H-loop near-field probe can be moved across the surface of the device while plotting the radiation pattern as a spectrogram.…”

Section: Smartphone Electromagnetic Radiationmentioning

confidence: 99%

Forensic Insights From Smartphones Through Electromagnetic Side-Channel Analysis

Sayakkara

Le-Khac

2021

IEEE Access

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The increasing use of smartphones has increased their presence in legal and corporate investigations. Unlike desktop and laptop computers, forensic analysis of smartphones is a challenging task due to their limited interfaces to retrieve information of forensic value. Electromagnetic side-channel analysis (EM-SCA) has been recently proposed as an alternative window to acquire forensic insights from computers, in particularly from Internet of Things devices. Along this line, this work experimentally evaluates the potential of extracting information of forensic value from smartphones through their EM radiation. Initially, a group of smartphones representing a diverse set of system-on-chip (SoC) processors were used to acquire EM radiation traces. Later, deep learning models were trained to detect various internal software behaviours running on the SoCs. The results of this work indicates that a wide variety of insights can be extracted from smartphones through EM side-channel, increasing the potential opportunities for digital forensic investigators.INDEX TERMS Digital forensics, smartphone forensics, electromagnetic side-channel, software behaviour detection, deep learning model.

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“…This component can be either an oscilloscope, signal analyser or a software-define radio (SDR). Depending on the distance to the DUT from the signal capturing equipment, a near-field or a far-field antenna have to be used [23]. The signal acquisition equipment is connected to a host computer that runs the necessary software to read the EM data samples and save them into trace files.…”

Section: B Acquisition Of Electromagnetic Radiationmentioning

confidence: 99%

Electromagnetic Side-Channel Analysis for IoT Forensics: Challenges, Framework, and Datasets

Sayakkara

Le-Khac

2021

IEEE Access

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Electromagnetic (EM) side-channel radiation from Internet of Things (IoT) devices are shown to be effective at acquiring forensic insights during digital investigations. These EM radiation patterns can be analysed with the help of machine learning algorithms to detect internal behaviours of IoT devices, which can be relevant to an investigation. However, the real-world application of EM sidechannel analysis for digital forensic purposes is obstructed by the lack of suitable tools and the technical expertise among law-enforcement communities. Although certain frameworks, such as EMvidence, exist to cater this requirement, the sheer diversity of the IoT ecosystem makes it difficult to support a sufficiently large collection of devices that are commonly encountered in forensic investigations. The work presented in this paper makes multiple contributions towards addressing this problem. Initially, a detailed discussion on the challenges of applying EM side-channel analysis in practical digital forensic purposes is provided, where the practical difficulties are illustrated. Then, it was shown that the existing EM side-channel analysis frameworks, such as EMvidence, can be used to overcome the diversity of IoT devices in forensics by equipping them with extensible plug-ins targeting the internal system-on-chips (SoC) of each device type. These plug-ins are expected to incorporate trained machine learning models, which are capable of recognising patterns of specific IoT device SoCs. However, the development of such plug-ins requires sufficiently diverse EM datasets from IoT devices. Facilitating this requirement, this work presents a comprehensive EM side-channel dataset representing a diverse collection of popular IoT devices and smartphones. The presented dataset is used to demonstrate the potential usage of machine learning models to recognise device behaviour.

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Detecting Cellphone Camera Status at Distance by Exploiting Electromagnetic Emanations

Cited by 7 publications

References 16 publications

EM Eye: Characterizing Electromagnetic Side-channel Eavesdropping on Embedded Cameras

EM Eye: Characterizing Electromagnetic Side-channel Eavesdropping on Embedded Cameras

Forensic Insights From Smartphones Through Electromagnetic Side-Channel Analysis

Electromagnetic Side-Channel Analysis for IoT Forensics: Challenges, Framework, and Datasets

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