Multi-Modal Data Fusion with Deep Neural Networks for Holistic Security Assessments
V. Samuthira Pandi, D Shobana, B. Sarala · 2025
The rapid advancements in deep neural networks (DNNs) have revolutionized multi-modal data fusion, paving the way for transformative applications in holistic security assessments. This book chapter explores the integration of diverse data modalities, such as visual, textual, and behavioral inputs, to enhance security systems' accuracy, robustness, and adaptability. The chapter delves into state-of-the-art DNN architectures, including hybrid models that combine Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers, to effectively process and fuse multi-modal data. Key challenges, such as balancing model complexity with fusion efficiency and addressing issues of scalability and real-time applicability, are critically analyzed. Advanced topics, including attention mechanisms for emphasizing relevant features and innovative fusion strategies, are discussed to provide actionable insights for developing intelligent security systems. Case studies, such as integrated facial and behavior recognition systems, demonstrate the efficacy of these approaches in real-world applications. By addressing the gaps in existing methodologies and proposing novel solutions, this chapter contributes significantly to advancing the field of multi-modal data fusion for security.