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

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

612
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
612
Association Areas of the Cortex01:21

Association Areas of the Cortex

5.2K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
5.2K
Perceptual Constancy01:12

Perceptual Constancy

369
Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
369

You might also read

Related Articles

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

Sort by
Same author

Analysis of the Current Status and Associated Factors of Premature Frailty in Adults Aged 18-59 Years Based on the NHANES Database.

Geriatrics & gerontology international·2026
Same author

Exploring barriers to the sustainable implementation of an evidence-based program for preventing deep vein thrombosis in patients with aneurysmal subarachnoid hemorrhage: a qualitative study using the Consolidated Framework for Implementation Research (CFIR).

BMC health services research·2026
Same author

Face Forgery Detection With CLIP-Enhanced Multi-Encoder Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
Same author

FA-Net: A Feature Alignment Network for Video-Based Visible-Infrared Person Re-Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
Same author

VPT-NSP<sup>2</sup>++: Importance-Aware Visual Prompt Tuning in Null Space for Continual Learning.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Missing links in nanomaterials research impacting productivity and perceptions.

Beilstein journal of nanotechnology·2025

Related Experiment Video

Updated: Jun 15, 2025

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.0K

GazeForensics: DeepFake detection via gaze-guided spatial inconsistency learning.

Qinlin He1, Chunlei Peng1, Decheng Liu1

  • 1State Key Laboratory of Integrated Services Networks, School of Cyber Engineering, Xidian University, Xi'an 710071, Shaanxi, PR China.

Neural Networks : the Official Journal of the International Neural Network Society
|August 22, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces GazeForensics, a novel method for DeepFake detection. It integrates gaze authenticity and general features to improve accuracy and interpretability in identifying manipulated media.

Keywords:
Attention mechanismDeepFake detectionGaze estimation

More Related Videos

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.6K
Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
07:09

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

10.6K

Related Experiment Videos

Last Updated: Jun 15, 2025

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.0K
A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.6K
Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
07:09

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

10.6K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biometrics

Background:

  • DeepFake technology is rapidly advancing, creating highly deceptive forged media.
  • Existing DeepFake detection methods often fail to integrate general and biometric features effectively.
  • Gaze authenticity analysis has been underutilized in DeepFake detection research.

Purpose of the Study:

  • To develop an innovative DeepFake detection method named GazeForensics.
  • To enhance DeepFake detection by integrating gaze representation with general features.
  • To improve the performance and interpretability of DeepFake detection models.

Main Methods:

  • Utilized gaze representation from a 3D gaze estimation model.
  • Regularized gaze representation within the DeepFake detection model.
  • Integrated general features to boost overall model performance.

Main Results:

  • The GazeForensics method demonstrated strong performance in DeepFake detection.
  • The approach showed excellent interpretability in identifying manipulated media.
  • The integration of gaze and general features led to enhanced detection capabilities.

Conclusions:

  • GazeForensics offers a promising approach to combatting sophisticated DeepFakes.
  • The method effectively leverages biometric and general features for improved detection.
  • Future research can build upon this integrated feature approach for enhanced media authenticity verification.