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 F1-scores, confirming the efficacy of the architecture.
@article{arxiv.2605.17115,
title = {F2IND-IT! -- Multimodal Fuzzy Fake Indian News Detection using Images and Text},
author = {Kushal Trivedi and Murtuza Shaikh and Khushi Singh and Jeevaraj S.},
journal= {arXiv preprint arXiv:2605.17115},
year = {2026}
}