Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Photoreceptors and Visual Pathways01:22

Photoreceptors and Visual Pathways

6.0K
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,...
6.0K

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

Related Experiment Video

Updated: Jul 6, 2025

Smartphone Fundus Photography
05:51

Smartphone Fundus Photography

Published on: July 6, 2017

39.0K

Imaging privacy threats from an ambient light sensor.

Yang Liu1,2, Gregory W Wornell1,2,3, William T Freeman1,2

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

Science Advances
|January 10, 2024
PubMed
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
05:50

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

Published on: January 16, 2020

5.8K
Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation
20:12

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

Published on: October 8, 2011

30.5K

Related Experiment Videos

Last Updated: Jul 6, 2025

Smartphone Fundus Photography
05:51

Smartphone Fundus Photography

Published on: July 6, 2017

39.0K
Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger
05:50

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

Published on: January 16, 2020

5.8K
Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation
20:12

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

Published on: October 8, 2011

30.5K

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.