Federated micro-expression mining and multi-modal metadata fusion for Deepfake fraud detection in ubiquitous financial video-KYC systems at IoT network

Introduction & Problem Statement- The increasing sophistication of AI-generated deepfakes poses significant challenges for financial video-KYC systems, where identity verification relies on accurate and real-time analysis of user biometrics. Traditional centralized and unimodal detection models struggle to balance accuracy, privacy, and deployment scalability, particularly across heterogeneous IoT edge devices. Need for Research-There is a pressing need for privacy-preserving, scalable, and robust deepfake detection mechanisms capable of identifying subtle manipulations in real-world financial environments. Current solutions often fail under domain-shift conditions, low-resolution inputs, or in scenarios involving complex micro-expression and behavioral cues. Proposed Work & Objective- This research proposes the Federated Micro-Expression Mining and Multi-Modal Metadata Fusion (FED-MEMF) framework, designed to accurately detect deepfake fraud in decentralized video-KYC systems. The objectives are to (i) enhance detection accuracy by leveraging facial micro-expression dynamics, audio signals, and session metadata, and (ii) preserve user privacy through federated learning while ensuring low-latency real-time inference. Novelty- The novelty lies in integrating fine-grained micro-expression analysis with behavioral metadata fusion in a federated learning environment, combined with cross-modal attention mechanisms. This approach enables robust detection across multiple datasets while maintaining privacy and edge-device compatibility. Method- The framework employs modality-specific encoders—μ-Transformer for micro-expressions, CNN for audio, and LSTM for metadata—with features fused via a cross-modal attention engine. Federated Averaging (FedAvg) aggregates local model updates from IoT edge devices without transferring sensitive data. Quantization and hardware optimizations enable real-time performance on low-power devices. Dataset- Experiments utilized FaceForensics++, CAS(ME)^2, and a proprietary KYC-FinVox2024 dataset comprising video, audio, and metadata streams, including micro-expression labels, to evaluate both intra- and cross-dataset performance. Results- FED-MEMF achieved an overall accuracy of 98.7%, F1-score of 0.987, AUC of 0.996, and inference latency of 82 ms, outperforming XceptionNet, EfficientNet-B4, and CNN+LSTM baselines. Multi-modal fusion significantly reduced false positives and false negatives, demonstrating robustness under domain-shift conditions. Conclusion & Future Work- FED-MEMF provides a privacy-conscious, real-time, and scalable solution for deepfake detection in financial video-KYC applications. Future directions include multilingual audio-visual alignment, blockchain-enabled federated auditing, explainable AI integration, and deployment in other regulatory-sensitive sectors such as e-governance, healthcare, and remote education verification.