Work

Digital forensics in law enforcement: A case study of LLM-driven evidence analysis

Year: 2025

Type: article

Abstract: The advent of digital technology and the ubiquity of mobile devices in today's society has led to a significant increase in the importance of mobile forensics in criminal investigations. Responding to the escalating volume and complexity of data due to enhanced smartphone capabilities and pervasive messaging apps, law enforcement agencies face challenges in data analysis. This study explores improving investigative efficiency through LLM-driven analysis of text from mobile messenger communications. We have conducted experiments on anonymized data collected from real crime scenes by employing three state-of-the-art LLM models, namely GPT-4o, Gemini 1.5 and Claude 3.5. The study focuses on optimizing model performance by employing prompt engineering, interpreting expressions embedded with hidden meanings such as slang, and contextually inferring ambiguous word usage. Finally, model performance is quantitatively evaluated using metrics such as precision, recall, F1 score, and hallucination rate.

Source: Forensic Science International Digital Investigation

DOI: 10.1016/j.fsidi.2025.301939

Authors: Kyung-Jong Kim, Chan-Hwi Lee, So-Eun Bae, Ju-Hyun Choi, Wook Kang

Institutions: Korea Advanced Institute of Science and Technology, Korean National Police University

Language: English

Topic: Digital and Cyber Forensics

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Peace, justice, and strong institutions

Open Access status: hybrid

Estimated APC paid: $3,100