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

arXiv: https://arxiv.org/abs/2605.17115 · HTML: https://mlsanity.com/paper/2605.17115/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 16 May 2026.

## Builds on (most cited first, 3 of 3)

- [Multimodal Fake News Detection via CLIP-Guided Learning](https://mlsanity.com/paper/2205.14304/) (2022)
- [ETMA: Efficient Transformer Based Multilevel Attention framework for Multimodal Fake News Detection](https://mlsanity.com/paper/2206.07331/) (2022)
- [A Multimodal Adaptive Graph-based Intelligent Classification Model for Fake News](https://mlsanity.com/paper/2411.06097/) (2024)

## Abstract (arXiv)

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.