Advances in DeepFake detection algorithms: Exploring fusion techniques in single and multi-modal approach

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 102993 - 102993

Published: Feb. 1, 2025

Language: Английский

Enhancing Fake Image Detection with Ensembled Convolutional Neural Networks

Published: May 13, 2025

Fake image detection has emerged as a vital task for the Generative AI era due to fast evolution in generations of models that have made highly realistic synthetic images possible. In this paper, we formulate an ensemble-based Convolutional Neural Network (CNN) enhance fake accuracy. Our methodology includes training five CNN on separate datasets consisting real and artificially created found different public datasets. The are produced using latest include StyleGAN2, StyleGAN3, Diffusion GAN, Taming Transformer Gansformer. outputs fused stacking ensemble process which several classifiers such Random Forest, Gradient Boosting, AdaBoost, Support Vector Machine, Multi-Layer Perceptron Logistic Regression utilized boost final classification performance. ultimate test unseen data reveals increase performance our approach exhibits high accuracy rate more than 90%. Comparison metrics precision, recall F1-score complete insight about proposed approach. These results indicate use deep learning approaches makes systems strongly robust nature even applicable real-world settings.

Language: Английский

Living in the Age of Deepfakes: A Bibliometric Exploration of Trends, Challenges, and Detection Approaches

Information, Journal Year: 2024, Volume and Issue: 15(9), P. 525 - 525

Published: Aug. 28, 2024

In an era where all information can be reached with one click and by using the internet, risk has increased in a significant manner. Deepfakes are of main threats on affect society influencing altering information, decisions, actions. The rise artificial intelligence (AI) simplified creation deepfakes, allowing even novice users to generate false order create propaganda. One most prevalent methods falsification involves images, as they constitute impactful element which reader engages. second common method pertains videos, viewers often interact with. Two major events led increase number deepfake images namely COVID-19 pandemic Russia–Ukraine conflict. Together ongoing “revolution” AI, expanded at fastest rate, impacting each us. reduce misinformation, must aware phenomenon exposed to. This also means encouraging more thoroughly consider sources from obtain leading culture caution regarding any new receive. purpose analysis is extract relevant articles related domain. Using specific keywords, database was extracted Clarivate Analytics’ Web Science Core Collection. Given annual growth rate 161.38% relatively brief period between 2018 2023, research community demonstrated keen interest issue positioning it forward-looking subjects technology. aims identify key authors, examine collaborative efforts among them, explore primary topics under scrutiny, highlight bigrams, or trigrams utilized. Additionally, this document outlines potential strategies combat proliferation deepfakes preserve trust.

Language: Английский