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

TL;DR

This paper presents a multimodal fake news detection framework combining images and text, utilizing deep learning and fuzzy inference to improve accuracy in Indian media contexts.

Contribution

It introduces a novel architecture integrating CNN, BERT, and neuro-fuzzy systems with an attention mechanism for enhanced fake news detection.

Findings

Outperforms previous models on the IFND dataset

Achieves higher accuracy, precision, recall, and F1-score

Validates effectiveness through comprehensive comparative analysis

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.