Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Integrating NLP and Computer Vision for Multimodal Fake News Detection with Explainable AI

Author(s) Ms. Riya Mary Raju, Prof. Dr. Ajay Kumar Singh
Country India
Abstract The proliferation of fake news through online channels is a real menace that has posed considerable problems in determining the authenticity of online information. This is a multimodal misinformation de-tection project that is an integrated project using both text and visual data in order to find out misleading information in a more effective way. The system uses transformer-based models including BERT, RoB-ERTa and XLNet to perform text classification besides CNN and FastText models to receive semantic and contextual features of the news articles. To verify images, convolutional neural networks and mobile net are used in order to identify manipulated or tampered pictures. This compilation of models makes it possible to create a single framework that will increase the accuracy of detection in a variety of data sets. Besides, Explainable AI methods, in particular SHAP, are included to explain decisions of a model and visualize the importance of features to guarantee transparency and trust in predictions. The entire system is deployed as a web application in Flask, whereby it has user registration modules, log in modules, con-tent submission modules, and modules to display the classification results. The findings of the experi-ment prove that the suggested scheme has a better reliability level than single-modality systems. On the whole, the current project allows creating a viable and interpretable misinformation detection system based on natural language processing, computer vision, and explainable AI.
Keywords Misinformation Detection, Fake News, Transformer Models, CNN, Explainable AI, SHAP
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 2, March-April 2026
Published On 2026-03-19
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E-ISSN 3048-7641
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