Fake news image based detection using Convolutional Neural Network / Noor Hazriena Afizah Awang

In today's digital era, the ubiquitous presence of social media has facilitated the rapid dissemination of information, including images. However, this connectivity has given rise to a concerning phenomenon - the widespread distribution of fake news images. These manipulated visuals, easily cre...

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Main Author:	Awang, Noor Hazriena Afizah
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/96388/1/96388.pdf
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id	my-uitm-ir.96388
record_format	uketd_dc
spelling	my-uitm-ir.963882024-06-05T03:58:11Z Fake news image based detection using Convolutional Neural Network / Noor Hazriena Afizah Awang 2024 Awang, Noor Hazriena Afizah Neural networks (Computer science) In today's digital era, the ubiquitous presence of social media has facilitated the rapid dissemination of information, including images. However, this connectivity has given rise to a concerning phenomenon - the widespread distribution of fake news images. These manipulated visuals, easily created using available software, pose a serious threat by spreading misinformation and eroding public trust. This research addresses this issue through proposing a Fake News Image-Based Detection system using Convolutional Neural Network (CNN). The study unfolds in several phases, commencing with a comprehensive background study highlighting the prevalence and impact of fake news images in our interconnected world. The problem statement emphasizes the challenges posed by the sophistication of manipulated images, the potential to influence public perception, and the difficulty in detection. The objectives focus on studying the requirements of CNN for detection, developing a prototype, and evaluating its performance. Methodologically, the research navigates through phases, encompassing literature reviews, video analyses, and data collection from a Image Communication Laboratory dataset focusing on copy-move images. The design and development phase culminate in the creation of a CNN-based prototype for Fake News Image-Based Detection. Evaluation metrics, including accuracy, precision, recall, and F1-score, showcase the model's robust performance, achieving an accuracy of 89.29%. In conclusion, the proposed CNN-based approach stands as a promising solution to the challenges posed by fake news images. The high accuracy attained underscores its efficacy in discerning manipulated content. This research contributes to fostering trust and reliability in the digital landscape, offering a valuable tool to combat the adverse effects of misinformation through image manipulation. As technology continues to evolve, ongoing research in this field remains imperative to stay ahead of increasingly sophisticated image manipulation techniques. 2024 Thesis https://ir.uitm.edu.my/id/eprint/96388/ https://ir.uitm.edu.my/id/eprint/96388/1/96388.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Mathematics Sa’dan, Siti ‘Aisyah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Sa’dan, Siti ‘Aisyah
topic	Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Awang, Noor Hazriena Afizah Fake news image based detection using Convolutional Neural Network / Noor Hazriena Afizah Awang
description	In today's digital era, the ubiquitous presence of social media has facilitated the rapid dissemination of information, including images. However, this connectivity has given rise to a concerning phenomenon - the widespread distribution of fake news images. These manipulated visuals, easily created using available software, pose a serious threat by spreading misinformation and eroding public trust. This research addresses this issue through proposing a Fake News Image-Based Detection system using Convolutional Neural Network (CNN). The study unfolds in several phases, commencing with a comprehensive background study highlighting the prevalence and impact of fake news images in our interconnected world. The problem statement emphasizes the challenges posed by the sophistication of manipulated images, the potential to influence public perception, and the difficulty in detection. The objectives focus on studying the requirements of CNN for detection, developing a prototype, and evaluating its performance. Methodologically, the research navigates through phases, encompassing literature reviews, video analyses, and data collection from a Image Communication Laboratory dataset focusing on copy-move images. The design and development phase culminate in the creation of a CNN-based prototype for Fake News Image-Based Detection. Evaluation metrics, including accuracy, precision, recall, and F1-score, showcase the model's robust performance, achieving an accuracy of 89.29%. In conclusion, the proposed CNN-based approach stands as a promising solution to the challenges posed by fake news images. The high accuracy attained underscores its efficacy in discerning manipulated content. This research contributes to fostering trust and reliability in the digital landscape, offering a valuable tool to combat the adverse effects of misinformation through image manipulation. As technology continues to evolve, ongoing research in this field remains imperative to stay ahead of increasingly sophisticated image manipulation techniques.
format	Thesis
qualification_level	Bachelor degree
author	Awang, Noor Hazriena Afizah
author_facet	Awang, Noor Hazriena Afizah
author_sort	Awang, Noor Hazriena Afizah
title	Fake news image based detection using Convolutional Neural Network / Noor Hazriena Afizah Awang
title_short	Fake news image based detection using Convolutional Neural Network / Noor Hazriena Afizah Awang
title_full	Fake news image based detection using Convolutional Neural Network / Noor Hazriena Afizah Awang
title_fullStr	Fake news image based detection using Convolutional Neural Network / Noor Hazriena Afizah Awang
title_full_unstemmed	Fake news image based detection using Convolutional Neural Network / Noor Hazriena Afizah Awang
title_sort	fake news image based detection using convolutional neural network / noor hazriena afizah awang
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Mathematics
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/96388/1/96388.pdf
_version_	1804889987259301888

Fake news image based detection using Convolutional Neural Network / Noor Hazriena Afizah Awang

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