COMPUTER VISION METHODS FOR CONDUCTING OSINT INVESTIGATIONS

Abstract

This paper researches the application of computer vision techniques in
concern with OSINT (Open Source Intelligence) investigations. It explores how state-ofthe-
art algorithms and models in computer vision can be used to automate and enhance the
process of gathering, analyzing, and interpreting visual data from open sources. The research
focuses on the critical steps of image scraping, data preprocessing, and embedding generation
using advanced deep learning models such as CLIP. Additionally, the study examines the
challenges of managing large-scale visual data and implementing efficient search mechanisms
through vector databases like Faiss and Weaviate. By applying these technologies, the
paper illustrates how investigators can improve the accuracy and efficiency of image-based
searches, which are later used for uncovering hidden connections and verifying information
in OSINT investigations. The findings contribute to the growing field of computer vision
and intelligence gathering, offering practical recommendations for enhancing investigative
processes through the integration of computer vision methodologies.