A multimodal artificial intelligence framework for digital forensics

Digital forensics plays a key role in investigating different types of crimes. It involves analyzing evidence from electronic devices, social media, and system logs. However, investigators face major challenges due to the large size of digital data. Information from networks, smartphones, and online platforms is often too complex to analyze within a limited time. The large size of digital evidence and the pressure of a strict timeline for case investigation force the investigator to do data reduction and selective analysis. It is also difficult for the investigator to correlate the various digital evidence to create the complete picture of the crime story. To address these challenges, we propose a Multimodal Artificial Intelligence Framework designed to integrate heterogeneous digital evidence analysis capabilities by leveraging artificial intelligence. The proof-of-concept architecture proposed in the paper incorporates explainable AI, pattern recognition, and a confidence-aware inference mechanism for semantic evidence analysis. The experimental analysis of the proposed model shows improved investigative efficiency. The analysis of the research results endorses the effectiveness of AI-assisted workflow in digital forensics investigation.