Search research articles

Related Concept Videos

Photoreceptors and Visual Pathways

Photoreceptors and Visual Pathways

At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

On-patient medical record and mRNA therapeutics using intradermal microneedles.

Nature materials·2025

Same author

A deep learning method for reflective boundary estimation.

The Journal of the Acoustical Society of America·2024

Same author

An architecture for passive joint localization and structure learning in reverberant environments.

The Journal of the Acoustical Society of America·2023

Same author

A Maximal Correlation Framework for Fair Machine Learning.

Entropy (Basel, Switzerland)·2022

Same author

An Information Theoretic Interpretation to Deep Neural Networks.

Entropy (Basel, Switzerland)·2022

Same author

A Modulo-Based Architecture for Analog-to-Digital Conversion.

IEEE journal of selected topics in signal processing·2021

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jul 6, 2025

Smartphone Fundus Photography

Smartphone Fundus Photography

Published on: July 6, 2017

Imaging privacy threats from an ambient light sensor.

Yang Liu^1,2, Gregory W Wornell^1,2,3, William T Freeman^1,2

¹Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Science Advances

|January 10, 2024

Summary

Smart devices can leak user information using only a light sensor and screen, creating privacy risks. Researchers developed a method to reconstruct touch interactions without a camera, highlighting potential security threats.

More Related Videos

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

Published on: January 16, 2020

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

Published on: October 8, 2011

Related Experiment Videos

Last Updated: Jul 6, 2025

Smartphone Fundus Photography

Smartphone Fundus Photography

Published on: July 6, 2017

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

Published on: January 16, 2020

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

Published on: October 8, 2011

Area of Science:

Computer Science
Cybersecurity
Human-Computer Interaction

Background:

Smart devices contain embedded sensors that can unintentionally leak sensitive user information.
Existing privacy risks often focus on cameras, but other sensors may also pose threats.

Purpose of the Study:

To investigate the potential for capturing touch interactions using only an ambient light sensor and a device display, without a camera.
To develop and evaluate a method for reconstructing images of touch events from ambient light sensor data.

Main Methods:

Utilized a known video sequence displayed on the device screen.
Captured reflected light intensity variations using the ambient light sensor, partially occluded by a hand.
Formulated an inverse problem analogous to single-pixel imaging.
Developed an inversion algorithm incorporating an ℓ₁-norm dequantizer and a deep denoiser to reconstruct images.

Main Results:

Successfully demonstrated the reconstruction of touch interactions and hand gestures on an Android tablet.
The proposed method can capture images from the screen's perspective, revealing user interactions.
Limitations in resolution and speed were observed.

Conclusions:

Combining ambient light sensors and device displays presents a novel, camera-less method for capturing user interactions.
This research highlights potential security and privacy threats from passive and active component interactions in smart devices.
The findings encourage the development of mitigation strategies against such vulnerabilities.