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

Prosopagnosia01:24

Prosopagnosia

1.1K
Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
1.1K
Association Areas of the Cortex01:21

Association Areas of the Cortex

10.3K
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,...
10.3K
Understanding Deception01:14

Understanding Deception

220
Deception is a pervasive aspect of human communication. Empirical studies have shown that most individuals engage in some form of deceit on a daily basis, with approximately 20% of social exchanges involving deceptive elements. Lying follows a developmental trajectory, peaking during adolescence and declining with age, possibly due to the maturation of cognitive control and social accountability.Cognitive and Social Factors in Deception DetectionDespite its prevalence, accurately detecting...
220
Muscles for Facial Expressions01:14

Muscles for Facial Expressions

5.5K
The craniofacial muscles are a collection of approximately 20 thin skeletal muscles situated beneath the skin of the face and scalp. These muscles, primarily responsible for the vast array of human facial expressions, originate from the bones or fibrous structures of the skull and extend outwards to connect with the skin. While most skeletal muscles in the body are enveloped in thick fascia, facial muscles generally have a more delicate fascial covering, with the buccinator muscle being a...
5.5K

You might also read

Related Articles

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

Sort by
Same author

Fine-Grained Visual Classification via Adaptive Attention Quantization Transformer.

IEEE transactions on neural networks and learning systems·2025
Same author

Dual-circularly polarized flat-top-beam transmitarray antenna with flexible energy allocations.

Optics express·2025
Same author

Ensembling a Learned Volterra Polynomial with a Neural Network for Joint Nonlinear Distortions and Mismatch Errors Calibration of Time-Interleaved Pipelined ADCs.

Sensors (Basel, Switzerland)·2025
Same author

Quantum recurrent neural networks for sequential learning.

Neural networks : the official journal of the International Neural Network Society·2023
Same author

SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement with Multi-Scale Perception.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2022
Same author

Transformer for Image Harmonization and Beyond.

IEEE transactions on pattern analysis and machine intelligence·2022
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Mar 19, 2026

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
09:49

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Published on: December 24, 2015

14.7K

Revisiting Face Forgery Detection: From Facial Representation to Forgery Detection.

Zonghui Guo, Yingjie Liu, Jie Zhang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 17, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new approach for face forgery detection (FFD) by developing a specialized backbone and competitive fine-tuning. This method enhances generalization and improves the reliability of identifying fake or deepfake images.

    More Related Videos

    Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
    06:53

    Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

    Published on: March 1, 2017

    13.9K
    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
    06:37

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

    Published on: December 15, 2023

    5.6K

    Related Experiment Videos

    Last Updated: Mar 19, 2026

    Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
    09:49

    Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

    Published on: December 24, 2015

    14.7K
    Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
    06:53

    Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

    Published on: March 1, 2017

    13.9K
    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
    06:37

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

    Published on: December 15, 2023

    5.6K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Image Processing

    Background:

    • Face Forgery Detection (FFD) models struggle with generalization due to overfitting on specific forgery patterns from diverse synthesis algorithms.
    • Current methods using general pre-trained backbones lack domain-specific facial knowledge, hindering the identification of subtle forgery cues.

    Purpose of the Study:

    • To enhance the generalization capabilities of Face Forgery Detection (FFD) models.
    • To develop a more effective FFD workflow by integrating domain-specific facial representation and forgery detection.
    • To improve the identification of implicit forgery cues and inference reliability.

    Main Methods:

    • Developed an FFD-specific backbone through self-supervised pre-training on real faces for superior facial representation.
    • Proposed a competitive fine-tuning framework to stimulate the backbone in identifying implicit forgery cues.
    • Devised a threshold optimization mechanism using prediction confidence to enhance inference reliability.

    Main Results:

    • The proposed method achieved excellent performance in Face Forgery Detection (FFD).
    • Demonstrated strong generalization capabilities on unseen forgery patterns.
    • Showcased effectiveness in related face-related tasks, including presentation attack detection.

    Conclusions:

    • The FFD-specific pre-trained backbone and competitive fine-tuning framework significantly improve generalization and performance in deepfake detection.
    • The integration of domain-specific knowledge and advanced training techniques is crucial for robust face forgery detection.
    • The method offers improved reliability and effectiveness for identifying digital face forgeries.