COMPUTER VISION METHODS FOR CONDUCTING OSINT INVESTIGATIONS

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 imagebased 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.