System and Methods for Multi-Modal Data Authentication Using Neuro-Symbolic AI

A method of generating a multimodal forensic report, comprising:

• generating, utilizing one or more processors, a hybrid metric learning and signature-based model by:
• receiving, at the one or more processors, data from one or more sources of sensory data, the sensory data comprising multimodal or single-modality sensory data;
• preprocessing, by the one or more processors, the sensory data comprising applying normalization to the data;
• extracting, using the one or more processors, features utilizing artificial intelligence models, said features comprising spatial, temporal, spatiotemporal, spectral, handcrafted, and biometric;
• applying unimodal or multimodal reasoning on the extracted features;
• detecting anomalies based on an inter-features or intra-features reasoning;
• applying binary or multiclass classification based on an inter-features or intra-features reasoning by:
• integrating spatiotemporal, temporal, and spatial features, multimodal AI representation learning features, and symbolic knowledge derived from landmark features and the inter-features and the intra-features reasoning;
• integrating the detected anomalies and the binary or multiclass classification;
• generating, utilizing the one or more processors, dynamic domain specific knowledge by applying data driven knowledge and ontology knowledge to the hybrid metric learning and signature-based model, by:
• extracting data driven knowledge from the hybrid metric learning and signature-based model by applying Artificial Intelligence models, the data driven knowledge comprising biological cues including emotions and temperature;
• storing human knowledge, in one or more databases, the human knowledge comprising rules, information, ranges, or ontology obtained from human domain experts;
• and
• generating, utilizing the one or more processors, explanations based on the dynamic domain specific knowledge and the authentication data, by:
• applying unimodal and multimodal reasoning on the dynamic domain specific knowledge and the authentication data;
• and
• sorting, prioritizing, and indexing associated features in a structure which included annotated rules, visual data, and statistic data.