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

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...

Force Classification

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

You might also read

Related Articles

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

Sort by

Same author

Route optimization in urban waste management using locally adjusted discrete cuckoo search: a hybrid metaheuristic approach.

Scientific reports·2026

Same author

Hybrid learning framework for synergistic fusion of SAR and optical UAV data in wildfire surveillance.

Scientific reports·2025

Same author

Cuckoo optimization algorithm via Grey Wolf Optimizer for usage in engineering optimization and optimal power flow with renewable energy sources.

Scientific reports·2025

Same author

Optimized deep learning approach for lung cancer detection using flying fox optimization and bidirectional generative adversarial networks.

PeerJ. Computer science·2025

Same author

EEG-based schizophrenia diagnosis using deep learning with multi-scale and adaptive feature selection.

PeerJ. Computer science·2025

Same author

Label dependency modeling in Multi-Label Naïve Bayes through input space expansion.

PeerJ. Computer science·2025

Same journal

DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions.

PeerJ. Computer science·2026

Same journal

Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.

PeerJ. Computer science·2026

Same journal

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

PeerJ. Computer science·2026

Same journal

An anomaly detection model for multivariate time series with anomaly perception.

PeerJ. Computer science·2026

Same journal

Retraction: A wormhole attack detection method for tactical wireless sensor networks.

PeerJ. Computer science·2026

Same journal

Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators.

PeerJ. Computer science·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 3, 2025

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

Deep fake detection using cascaded deep sparse auto-encoder for effective feature selection.

Saravana Balaji Balasubramanian¹, Jagadeesh Kannan R², Prabu P³

¹Department of Information Technology, Lebanese French University, Erbil, Iraq.

Peerj. Computer Science

|July 25, 2022

Summary

This summary is machine-generated.

This study introduces a novel Deepfake detection method using computer vision and deep learning. The Cascaded Deep Sparse Auto Encoder (CDSAE) model effectively identifies fake images and videos, enhancing digital security.

Keywords:

DNN Deep fake detection Deep learning Deep sparse Auto encoder Face2Face FaceSwap Faceforensics++Temporal Convolutional neural network

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Related Experiment Videos

Last Updated: Sep 3, 2025

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: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Area of Science:

Computer Vision
Artificial Intelligence
Cybersecurity

Background:

The proliferation of artificial intelligence has led to increased security and privacy concerns.
Deepfakes, AI-generated fake media, pose risks for political abuse, misinformation, and illicit content.

Purpose of the Study:

To develop an advanced Deepfake detection method.
To enhance the identification of manipulated digital content.

Main Methods:

Utilized computer vision features, specifically frame changes, for detection.
Developed a Cascaded Deep Sparse Auto Encoder (CDSAE) model trained with temporal CNN.
Employed a Deep Neural Network (DNN) for classifying real versus fake media.

Main Results:

The CDSAE model demonstrated improved detection rates on Face2Face, FaceSwap, and DFDC datasets.
Achieved superior performance compared to traditional Deepfake detection approaches.

Conclusions:

The proposed CDSAE-based method offers a robust solution for Deepfake detection.
This research contributes to mitigating the security and privacy risks associated with AI-generated fake media.