🤖 AI Summary
This study addresses the challenge of detecting multimodal fake news in India’s diverse media landscape by proposing a novel approach that integrates visual and textual modalities. The method leverages ResNet-50 to extract image features and DistilBERT to obtain semantic embeddings, and uniquely introduces an Adaptive Neuro-Fuzzy Inference System (ANFIS) into multimodal fake news detection to better model ambiguity and uncertainty. A lightweight, learnable-weight attention-based fusion module is further designed to dynamically integrate multimodal representations. Experimental results on the IFND dataset demonstrate that the proposed framework significantly outperforms existing methods across key evaluation metrics, including accuracy, precision, recall, and F1 score.
📝 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.