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

• URL: http://arxiv.org/abs/2605.17115v1
• Date: Sat, 16 May 2026 18:46:51 GMT
• Title: F2IND-IT! -- Multimodal Fuzzy Fake Indian News Detection using Images and Text
• Abstract summary: This paper introduces a novel framework combining visual and textual modalities for enhanced fake news detection on Indian media.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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