What question did this study set out to answer?

This research aims to develop a Transformer-based system for effectively detecting fake news and deepfake content.

June 19, 2026Open Access

Fake News and Deepfake Detection Using Transformer-Based Multi-Modal Networks

Key Points

This research aims to develop a Transformer-based system for effectively detecting fake news and deepfake content.
Utilized advanced Transformer architectures like BERT, RoBERTa, and Vision Transformer for multi-modal analysis of textual and multimedia data.
Employed natural language processing for fake news classification and computer vision techniques for deepfake detection.
Evaluated system performance using datasets of fake news articles and deepfake media samples with metrics like accuracy and F1-score.
Achieved higher accuracy and improved contextual understanding compared to traditional CNN-based detectors.
Demonstrated enhanced robustness in detecting facial manipulations in images and synthetic artifacts in videos.
Resulting system offers significant advancement in combating misinformation and strengthening cybersecurity.

Abstract

Abstract: Fake news and deepfakemultimedia content have emerged as majorthreats to digital media authenticity,cybersecurity, and public trust due to the rapidgrowth of social media and ArtificialIntelligence technologies. Traditional machinelearning and Convolutional Neural Network(CNN)-based approaches often fail to detectsophisticated fake content because of limitedcontextual understanding and weak featureextraction capabilities. This paper proposes aTransformer-based Fake News and DeepfakeDetection System capable of identifyingmanipulated textual and multimedia contentwith high accuracy. The proposed frameworkintegrates advanced Transformer architecturessuch as BERT, RoBERTa, Vision Transformer(ViT), and video-based Transformer models formulti-modal analysis of text, images, andvideos. Natural Language Processing (NLP)techniques are employed for fake newsclassification, while Computer Visiontechniques are utilized for detecting facialmanipulations and synthetic media artifacts indeepfake images and videos. The system istrained and evaluated using benchmark datasetscontaining fake news articles and deepfakemedia samples. Experimental evaluation isperformed using accuracy, precision, recall, andF1-score metrics. The proposed frameworkdemonstrates improved detection performance,better contextual understanding, and enhancedrobustness compared to traditional approaches.The system contributes toward combatingmisinformation, improving mediaauthentication, and strengthening cybersecuritymechanisms in digital environments

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