METHOD OF SEMANTIC SEGMENTATION OF VIDEO IMAGES USING U-NET NEURAL NETWORK - Kostromytskyi, Andrii; Bezruk, Valery; Malinin Іgor; Panchuk, Artem; Breslavets, Bohdan (Science-based technologies, 2026)
In today’s information environment, video information has become one of the most massive and valuable types of data. Surveillance cameras, mobile devices, drones and online platforms generate huge amounts of visual data every day, a significant part of which contains confidential or personal information. Traditional approaches to protection, such as full encryption of the video stream, provide a high level of security, but have significant limitations: they require significant computing resources, increase transmission delays and do not allow for effective real-time operation. The article considers the scientific and applied problem of selective protection of video information in conditions of limited computing resources, which is relevant for video surveillance systems, medical records and other areas where it is necessary to ensure the confidentiality of only critically important areas of the image. The purpose of the research is to create a software tool for dynamic identification and encryption of significant fragments of a video image using semantic segmentation methods and machine learning technologies. The proposed system is based on the architecture of the U-Net neural network, which allows to accurately identify areas requiring cryptographic protection and apply selective encryption to them. The Stanford Background dataset was used as an experimental basis, the test results confirm the effectiveness of the chosen approach. The presented methods can be integrated into practical systems with limited resources and increased requirements for performance and security.