Application of Multi-Modal Neural Networks in Verifying the Authenticity of News Text and Images

In the digital information era, the authenticity of news media is crucial for shaping public opinion and maintaining social stability. The verification of authenticity, especially in news content rich in both text and images, has become a significant task in safeguarding online information security....

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Published in:TRAITEMENT DU SIGNAL
Main Authors: Li, Feng; Xu, Meiling; Rosli, Marshima Mohd
Format: Article
Language:English
Published: INT INFORMATION & ENGINEERING TECHNOLOGY ASSOC 2023
Subjects:
Online Access:https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001137494800004
author Li
Feng; Xu
Meiling; Rosli
Marshima Mohd
spellingShingle Li
Feng; Xu
Meiling; Rosli
Marshima Mohd
Application of Multi-Modal Neural Networks in Verifying the Authenticity of News Text and Images
Computer Science; Engineering
author_facet Li
Feng; Xu
Meiling; Rosli
Marshima Mohd
author_sort Li
spelling Li, Feng; Xu, Meiling; Rosli, Marshima Mohd
Application of Multi-Modal Neural Networks in Verifying the Authenticity of News Text and Images
TRAITEMENT DU SIGNAL
English
Article
In the digital information era, the authenticity of news media is crucial for shaping public opinion and maintaining social stability. The verification of authenticity, especially in news content rich in both text and images, has become a significant task in safeguarding online information security. Traditional methods, often relying on single-modality analysis, are ineffectual against the complex interplay and manipulation techniques possible between news text and accompanying images. Addressing this, research in multi-modal neural networks has emerged, aiming to enhance verification effectiveness by integrating information from both text and images. However, limitations exist in existing research regarding key issue resolution and modality fusion strategies, including inadequate exploration in searching for anomalously similar image blocks, reducing false alarms, and post-processing of tampered images. This study systematically investigates these challenges in news text and image authenticity verification, proposing a novel multi-modal fusion method. This method encompasses three components: a joint gated co-attention mechanism, a filtering gate mechanism, and a joint cooperative representation. These effectively combine text and image information, enhancing the model's ability to discern complex manipulations. The findings of this study not only advance theoretical research in multi -modal fusion but also provide a powerful tool for news media to verify authenticity, significantly contributing to the fight against fake news and maintaining the integrity of information dissemination.
INT INFORMATION & ENGINEERING TECHNOLOGY ASSOC
0765-0019
1958-5608
2023
40
6
10.18280/ts.400606
Computer Science; Engineering
hybrid
WOS:001137494800004
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001137494800004
title Application of Multi-Modal Neural Networks in Verifying the Authenticity of News Text and Images
title_short Application of Multi-Modal Neural Networks in Verifying the Authenticity of News Text and Images
title_full Application of Multi-Modal Neural Networks in Verifying the Authenticity of News Text and Images
title_fullStr Application of Multi-Modal Neural Networks in Verifying the Authenticity of News Text and Images
title_full_unstemmed Application of Multi-Modal Neural Networks in Verifying the Authenticity of News Text and Images
title_sort Application of Multi-Modal Neural Networks in Verifying the Authenticity of News Text and Images
container_title TRAITEMENT DU SIGNAL
language English
format Article
description In the digital information era, the authenticity of news media is crucial for shaping public opinion and maintaining social stability. The verification of authenticity, especially in news content rich in both text and images, has become a significant task in safeguarding online information security. Traditional methods, often relying on single-modality analysis, are ineffectual against the complex interplay and manipulation techniques possible between news text and accompanying images. Addressing this, research in multi-modal neural networks has emerged, aiming to enhance verification effectiveness by integrating information from both text and images. However, limitations exist in existing research regarding key issue resolution and modality fusion strategies, including inadequate exploration in searching for anomalously similar image blocks, reducing false alarms, and post-processing of tampered images. This study systematically investigates these challenges in news text and image authenticity verification, proposing a novel multi-modal fusion method. This method encompasses three components: a joint gated co-attention mechanism, a filtering gate mechanism, and a joint cooperative representation. These effectively combine text and image information, enhancing the model's ability to discern complex manipulations. The findings of this study not only advance theoretical research in multi -modal fusion but also provide a powerful tool for news media to verify authenticity, significantly contributing to the fight against fake news and maintaining the integrity of information dissemination.
publisher INT INFORMATION & ENGINEERING TECHNOLOGY ASSOC
issn 0765-0019
1958-5608
publishDate 2023
container_volume 40
container_issue 6
doi_str_mv 10.18280/ts.400606
topic Computer Science; Engineering
topic_facet Computer Science; Engineering
accesstype hybrid
id WOS:001137494800004
url https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001137494800004
record_format wos
collection Web of Science (WoS)
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