Authors : Saranya N.; Karmukilan B.; Kishore P.; Mohammed Safik M.; Nithan Anto J.

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/2n3t5363

Scribd : https://tinyurl.com/26hk5sbz

DOI : https://doi.org/10.38124/ijisrt/26mar1568

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Ever since the arrival of the digital world, misleading information through the use of manipulated pictures and poorly constructed text on news websites and social media has become a world wide issue. Detection of such manipulated or false information is required in order to protect the public opinion, state security, as well as trust to digital environments, so in order to detect misinformation using a single input, which is a visual information that also has hidden text (memes, posters, and screenshots), this work suggests a deep learning dual-stream fusion model. The system recognizes the text on the image using optical character recognition (OCR) and derives the semantic meaning with the help of a BERT-BiLSTM model. Highlevel features of the image are simultaneously extracted by feeding it through a ResNet convolutional neural network. After that, the two features (text and visual) are fused on a deep fusion layer and maintain context and multi-modal dependency and generate a more robust and accurate classification. Such an integrated solution provides the final classifier that can distinguish the authenticity of supplied image-text input and prevent the further propagation of the misinformation introduced by multimedia sources and can greatly enhance the correctness of fake news recognition based on either Natural Language Understanding or Visual Semantics.

Keywords : Disinformation Detection, Sustainable Digital Infrastructure, Multimodal Deep Learning, BERT-BiLSTM.

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