F2IND-IT! -- Multimodal Fuzzy Fake Indian News Detection using Images and Text
The paper presents a new framework for detecting fake news in Indian media by integrating visual and textual analysis. It employs advanced technologies such as ResNet-50 for image feature extraction and DistilBERT for text analysis, combined with a fuzzy inference system for reliability scoring. Experimental results indicate that this multimodal approach outperforms previous methods in accuracy and other performance metrics.
- ▪The framework addresses the challenges of misinformation in diverse media landscapes like India.
- ▪It combines visual features from news images and textual semantic embeddings for enhanced detection.
- ▪The model was evaluated on the IFND dataset and showed superior performance in accuracy, precision, recall, and F1-scores.
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Computer Science > Artificial Intelligence arXiv:2605.17115 (cs) [Submitted on 16 May 2026] Title:F2IND-IT! -- Multimodal Fuzzy Fake Indian News Detection using Images and Text Authors:Kushal Trivedi, Murtuza Shaikh, Khushi Singh, Jeevaraj S. View a PDF of the paper titled F2IND-IT! -- Multimodal Fuzzy Fake Indian News Detection using Images and Text, by Kushal Trivedi and 3 other authors View PDF HTML (experimental) 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.
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