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arXiv · 2605.17115

F2IND-IT! -- Multimodal Fuzzy Fake Indian News Detection using Images and Text

Kushal Trivedi·Murtuza Shaikh·Khushi Singh·Jeevaraj S.
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Abstract

Biased manipulation of facts across regional and national media outlets complicates misinformation detection in diverse landscapes like India. This paper introduces a novel multimodal framework combining visual and textual modalities for enhanced fake news detection on Indian media. The architecture utilizes a ResNet-50 Convolutional Neural Network to extract visual features from news images, a DistilBERT encoder to obtain textual semantic embeddings, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) to generate a fuzzy reliability score. A lightweight attention-based fusion module assigns learnable weights to each modality prior to classification. Evaluated on the IFND dataset, the proposed model is validated through an in-depth comparative analysis against previous research. Experimental results demonstrate superior performance across accuracy, precision, recall, and $F_1$-scores, confirming the efficacy of the architecture.

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Kushal Trivedi, Murtuza Shaikh, Khushi Singh, Jeevaraj S.. 2026-05-16. F2IND-IT! -- Multimodal Fuzzy Fake Indian News Detection using Images and Text. https://arxiv.org/abs/2605.17115

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