Real-time cross-cultural lie detection system via multimodal fusion: microexpression enhancement and adversarial defense for forensics

Traditional methods, such as polygraphs, suffer from limitations including single-modality vulnerability, cultural bias, and adversarial attacks. This paper presents a novel Multimodal Physiological-Behavioral Fusion System (MPBFS) that integrates seven modalities: microexpressions, speech, text, eye movements, galvanic skin response (GSR), cultural features, and adversarial defense mechanisms.The system employs a Cultural Dimension-Physiological Signal Cross-Validation Module to dynamically weight modalities, reducing cultural bias.The Cascade Lightweight for Edge Computing achieves 84.6% accuracy (F1=0.87) on cross-cultural datasets with 23 FPS. A Two-Channel Adversarial Defense: Detects DeepFake audio via phase discontinuity analysis and validates microexpressions using Lagrangian biomechanical modeling confirming enhanced cross-cultural robustness. Experiments demonstrate a 31% improvement in Cultural Stability Index and a 42% reduction in adversarial attack success rates, validating the systems robustness. Designed for forensic interrogation and security screening, MPBFS integrates dynamic anonymization and cross-modal validation to ensure ethical AI deployment, addressing key limitations of traditional deception detection methods.