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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.0K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.0K

You might also read

Related Articles

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

Sort by
Same author

Prediction of Bandgap and Key Feature Analysis of Lead-Free Double Perovskite Oxides Based on Deep Learning.

Molecules (Basel, Switzerland)·2026
Same author

VIPS: Learning-View-Invariant Feature for Person Search.

Sensors (Basel, Switzerland)·2025
Same author

Prediction and Screening of Lead-Free Double Perovskite Photovoltaic Materials Based on Machine Learning.

Molecules (Basel, Switzerland)·2025
Same author

Effects of Different Concentrations of AmB on the Unsaturated Phospholipid-Cholesterol Membrane Using the Langmuir Monolayer and Liposome Models.

Molecules (Basel, Switzerland)·2024
Same author

Studying the Thermodynamic Phase Stability of Organic-Inorganic Hybrid Perovskites Using Machine Learning.

Molecules (Basel, Switzerland)·2024
Same author

Effect of Amphotericin B on the Thermodynamic Properties and Surface Morphology of the Pulmonary Surfactant Model Monolayer during Respiration.

Molecules (Basel, Switzerland)·2023

Related Experiment Video

Updated: Jan 7, 2026

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

5.2K

Audio Deepfake Detection via a Fuzzy Dual-Path Time-Frequency Attention Network.

Jinzi Li1, Hexu Wang1,2, Fei Xie3,4

  • 1Xi'an Key Laboratory of Human-Machine Integration and Control Technology for Intelligent Rehabilitation, Xijing University, Xi'an 710123, China.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
Summary

This study introduces a novel Dual-Path Time-Frequency Attention Network (DPTFAN) to combat audio deepfakes. The method enhances detection accuracy and robustness against noise and compression, improving information security.

Keywords:
Pythagorean hesitant fuzzy setsattention mechanismaudio deepfake detectiontime-frequency path

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

990

Related Experiment Videos

Last Updated: Jan 7, 2026

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

5.2K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

990

Area of Science:

  • Artificial Intelligence
  • Information Security
  • Signal Processing

Background:

  • Audio deepfake technology poses significant threats to information security due to advancements in speech synthesis.
  • Existing detection methods struggle with robustness against environmental noise, signal compression, and subtle fake audio features.
  • Highly concealed fake audio remains difficult to identify effectively with current techniques.

Purpose of the Study:

  • To develop a robust and accurate method for detecting audio deepfakes.
  • To address the limitations of existing methods in handling noisy and compressed audio.
  • To improve the identification of highly concealed fake audio through advanced feature characterization and enhancement.

Main Methods:

  • Proposes a Dual-Path Time-Frequency Attention Network (DPTFAN) incorporating Pythagorean Hesitant Fuzzy Sets (PHFS) for uncertainty modeling.
  • Employs a dual-path attention mechanism in time and frequency domains to improve feature representation and discriminative power.
  • Introduces a Lightweight Fuzzy Branch Network (LFBN) for explicit enhancement of ambiguous audio features, balancing performance and efficiency.

Main Results:

  • Achieved 98.94% accuracy on the ASVspoof 2019 LA dataset.
  • Reached 99.40% accuracy on the FoR (Fake or Real) dataset.
  • Demonstrated superior performance and robustness compared to existing mainstream audio deepfake detection methods.

Conclusions:

  • The proposed DPTFAN method offers excellent detection performance and robustness for audio deepfakes.
  • Uncertainty modeling with PHFS and dual-path attention effectively characterizes and enhances fake audio features.
  • The LFBN contributes to improved performance while maintaining computational efficiency in deepfake detection.