Search research articles

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

Related Concept Videos

Masking and Demasking Agents

Masking and Demasking Agents

EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on the metal...

You might also read

Related Articles

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

Sort by

Same author

Structural plasticity for neuromorphic networks with electropolymerized dendritic PEDOT connections.

Nature communications·2023

Same author

Voltage-dependent synaptic plasticity: Unsupervised probabilistic Hebbian plasticity rule based on neurons membrane potential.

Frontiers in neuroscience·2022

Same author

High efficiency photocatalytic degradation of Ambroxol over Mn doped TiO<sub>2</sub>: Experimental designs, identification of transformation products, mineralization and mechanism.

The Science of the total environment·2021

Same author

Multimodal Change Detection in Remote Sensing Images Using an Unsupervised Pixel Pairwise Based Markov Random Field Model.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2019

Same author

Effect of the chain length on the thermal and analytical properties of laterally biforked nematogens.

Journal of chromatography. A·2010

Same journal

Integrated multi-assessment and structural performance index framework for stacking-sequence optimisation of natural fibre reinforced laminates.

Scientific reports·2026

Same journal

SuperiorGAT: graph attention networks for sparse LiDAR point cloud reconstruction in autonomous systems.

Scientific reports·2026

Same journal

The effect of stretching the pectoralis major, sternocleidomastoid, and iliopsoas muscles on 800 m swimming performance in master swimmers.

Scientific reports·2026

Same journal

ISNR-PQC: isometry noise resilience post quantum cryptography primitive.

Scientific reports·2026

Same journal

Identification of high-yielding and stable genotypes of barley in the cold climate of Iran using AMMI and GGE biplot models.

Scientific reports·2026

Same journal

Bayesian negative binomial modelling of spatial and temporal patterns of road traffic deaths in Ghana.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 24, 2026

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Enhancing practicality and efficiency of deepfake detection.

Ismael Balafrej¹, Mohamed Dahmane²

¹Computer Research Institute of Montreal- CRIM, Vision Research Department, Montreal, Qc., Canada.

Scientific Reports

|December 27, 2024

Summary

This summary is machine-generated.

This study introduces efficient deepfake detection methods, making them accessible for everyday users and edge computing. Our techniques significantly reduce computational costs for identifying manipulated videos.

More Related Videos

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

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

Published on: December 15, 2023

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training

Published on: December 6, 2024

Related Experiment Videos

Last Updated: Jun 24, 2026

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

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

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

Published on: December 15, 2023

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training

Published on: December 6, 2024

Area of Science:

Computer Science
Artificial Intelligence
Digital Forensics

Background:

The rapid advancement of deepfake technology poses significant challenges for content authenticity verification.
Existing deepfake detection methods are computationally intensive, limiting their practical application, especially in resource-constrained environments like edge computing.
There is a growing need for efficient and accessible deepfake detection solutions for non-expert users.

Purpose of the Study:

To develop and present a series of techniques for accelerating the inference speed of deepfake detection on video data.
To explore the application of steganalysis principles for identifying deepfakes.
To identify key strategies for reducing the computational footprint of deepfake detection models, specifically convolutional neural networks.

Main Methods:

Proposed novel techniques to optimize deepfake detection inference speed on video.
Adapted steganalysis methods to treat deepfakes as encoded secret payloads within images.
Identified critical factors for significantly downsizing core convolutional neural network architectures.
Evaluated the proposed methods on Celeb-DFv2 and DFDC datasets.

Main Results:

Achieved competitive performance in deepfake detection accuracy on benchmark datasets.
Demonstrated a substantial reduction in computational cost and resource requirements compared to traditional methods.
Validated the effectiveness of the proposed acceleration and model size reduction techniques.

Conclusions:

The developed techniques offer a practical and efficient approach to deepfake detection, suitable for a wider range of applications, including edge computing.
The findings suggest that deepfake detection can be made more accessible and less resource-intensive without compromising accuracy.
This research contributes to the ongoing efforts to combat the spread of misinformation and enhance digital media integrity.