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Sigma Journal of Engineering and Natural Sciences – Sigma Mühendislik ve Fen Bilimleri Dergisi

Quantum-enhanced multimodal fusion for robust and accurate fake news detection

Thulasi Bikku

Published Jan 1, 2025• 2 citations
Publisher

Abstract

The digital era has witnessed a significant rise in misinformation, underscoring the urgent need for effective tools to distinguish factual news from fabricated content.This study proposes a novel methodology for fake news detection that leverages quantum entanglement to facilitate multimodal fusion of textual, visual, and acoustic data.The quantum entanglement algorithm offers significant advantages in managing high-dimensional data by enabling efficient feature optimization through quantum computing encodings integrated with neural network architectures.The existing quantum circuits for text, visual, and audio would co-occur while witnessing the message from humans to machines with the machines acting as quantum computers integrated with neural networks, specifically designed for fake news detection.The results generated from our method demonstrate significantly improved fake news detection accuracy, and increased accuracy in the noise simulation, and the system is resilient to adversarial methods, all in contrast to typical methods.The proposed Quantum Encoding with Multimodal Fusion (QEMF) framework surpasses existing approaches by offering as it suggests promising future directions to tackle fake news across the web.The system, in a scientific manner, performs a variety of strict pre-processing techniques to all textual data such as tokenization, stemming, and lemmatization, along with sophisticated image pre-processing to all visual data.It uses the latest extracting features, i.e., Glove for text embedding, and conventional convolutional networks, like VGG16, for visual data.The feature representation is significantly enriched through quantum encoding, while the capability of QCNNs to identify the most salient and discriminative features enhances accuracy and ensures robustness against noise and adversarial interference.Furthermore, the system's real-time detection capability and its scalability position it as a powerful tool in combating misinformation within the evolving bio-informational ecosystem.

DOI
10.14744/sigma.2025.00082

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The digital era has witnessed a significant rise in misinformation, underscoring the urgent need for effective tools to distinguish factual news from fabricated content.This study proposes a novel methodology for fake news detection that leverages quantum entanglement to facilitate multimodal fusion of textual, visual, and acoustic data.The quantum entanglement algorithm offers significant advantages in managing high-dimensional data by enabling efficient feature optimization through quantum computing encodings integrated with neural network architectures.The existing quantum circuits for text, visual, and audio would co-occur while witnessing the message from humans to machines with the machines acting as quantum computers integrated with neural networks, specifically designed for fake news detection.The results generated from our method demonstrate significantly improved fake news detection accuracy, and increased accuracy in the noise simulation, and the system is resilient to adversarial methods, all in contrast to typical methods.The proposed Quantum Encoding with Multimodal Fusion (QEMF) framework surpasses existing approaches by offering as it suggests promising future directions to tackle fake news across the web.The system, in a scientific manner, performs a variety of strict pre-processing techniques to all textual data such as tokenization, stemming, and lemmatization, along with sophisticated image pre-processing to all visual data.It uses the latest extracting features, i.e., Glove for text embedding, and conventional convolutional networks, like VGG16, for visual data.The feature representation is significantly enriched through quantum encoding, while the capability of QCNNs to identify the most salient and discriminative features enhances accuracy and ensures robustness against noise and adversarial interference.Furthermore, the system's real-time detection capability and its scalability position it as a powerful tool in combating misinformation within the evolving bio-informational ecosystem.
Fake news on social media platforms poses significant threats to public trust and information integrity.This research explores the application of quantum machine learning (QML) techniques for detecting fake news by leveraging quantum computing's unique capabilities.Our work introduces a hybrid quantum-classical framework that utilizes quantum convolutional neural networks (QCNNs) with angle and amplitude encoding schemes for processing multimodal features from text and images.Experiments conducted on benchmark datasets -Gos-sipCop and Politifact -demonstrate that our quantum-enhanced model achieves superior performance compared to classical approaches, with accuracy rates of 88.52% and 85.58%, and F1 scores of 93.19% and 90.20% respectively.Our findings establish QML as a viable approach for addressing the challenges of fake news detection in the digital era.
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This article proposes a quantum multimodal neural network (QMNN) model that can be implemented on parameterized quantum circuits (PQCs), providing a novel avenue for processing multimodal data and performing advanced multimodal sentiment analysis tasks. The comprehensive QMNN model is structured into four fundamental blocks: multimodal data preprocessing, unimodal feature extraction, multimodal feature fusion, and optimization. Through these blocks, multimodal data are initially preprocessed and encoded into quantum states. Subsequently, visual and textual features are extracted from the quantum states and are then integrated to learn the interactions between different modalities. Finally, the model parameters are fine-tuned to optimize the sentiment analysis performance. Simulation results confirm that QMNN surpasses state-of-the-art baselines, using significantly lower input dimensions and substantially fewer parameters than classical models. Furthermore, the entanglement, integrity, robustness, and scalability of the model are analyzed in depth. Internally, the strong entanglement within the multimodal fusion block enhances interactions between textual and visual features, and the integrity of the model reflects the indispensable contribution of each component to the overall performance. Externally, robustness ensures the model operates stably under noisy conditions and incomplete inputs, and scalability enables it to efficiently adapt to varying architectural depths and widths. The above simulation results and performance analyses showcase the comprehensive strength of our proposed model.
The spread of fake news on the virtual platform is so high that it requires efficient and scalable fake news detection systems. Recent language models based on transformers including DistilBERT have been shown to be very effective at extracting contextual semantic representation of text, but cannot express high-dimensional embedding space due to their dependency on classical classifiers. In the given work, we suggest QUATUM, a quantum-classical architecture with the incorporation of DistilBERT embeddings and variational quantum circuits to detect fake news. The suggested algorithm maps the output of transformers into a quantum Hilbert space to execute enhanced feature representation with superposition and entanglement on parameterized quantum circuits. They compare a traditional MLP-based classifier with the suggested quantum-enhanced model with the same training strategies on the IFND data set. It is shown that the QUATUM model has better performance, with the accuracy of 95.55%, and the equivalent values of the precision (96.45), recall (94.65), and F1-score (95.54). The results of these studies indicate that quantum-enhanced learning is effective to learn the complex decision boundaries and propose the possibility of using hybrid quantum-classical models to provide the next-generation fake news detection systems.
This paper introduces a novel quantum neural network architecture, termed "Multi-Modal Information Fusion Quantum Neural Network" (MMIFQN), designed to enhance the representation learning capabilities of complex data sequences. The core principle of MMIFQN lies in the strategic integration of multiple information sources – including visual, textual, and audio data – through a quantum neural network. This integration leverages quantum state manipulation, specifically superposition and entanglement, to facilitate a deeper and more nuanced understanding of the underlying data patterns. The proposed architecture demonstrates significant improvements in accuracy and robustness compared to traditional neural network models when dealing with intricate datasets. This work presents a paradigm shift in how quantum computing can be applied to data representation and processing, potentially unlocking new capabilities in areas such as pattern recognition and anomaly detection.