Персона: Иваненко, Виталий Григорьевич
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Research on the Vulnerabilities of Urban Video Surveillance Systems
2020, Alfara, A. U. A., Ivanov, N. S., Ivanenko, V. G., Иваненко, Виталий Григорьевич
© 2020 IEEE.This article discusses the problem of detecting dark spots in the urban video surveillance system. To identify shortcomings in the work of urban surveillance cameras, machine learning was used, which allowed to solve the problem of visualizing areas that are not in the camera's field of vision. Taking into account the fact that a wide range of video surveillance devices participated in the study: yard, porch video surveillance cameras and surveillance cameras in crowded places, a system was developed consisting of a database and an algorithm that allows you to request information only about the nearest video surveillance devices. This approach to the implementation of the main goal of the study contributed to the creation of an intuitive, user-friendly program that will help identify and eliminate dark spots in the urban video surveillance system.
Detection of Fake and Provokative Comments in Social Network Using Machine Learning
2020, Gamova, A. A., Horoshiy, A. A., Ivanenko, V. G., Иваненко, Виталий Григорьевич
© 2020 IEEE.Nowadays internet-trolls have big impact on other users, it interferes with comfortable use. Objective of the project is creating model for identifying provocative and fake comments. Science articles about detection of trolls by hand were searched for making the criteria of relevant comments selection. To achieve the goal there were created two artificial neural networks: definition of sarcasm and definition of sentiment analysis. The program result is datasets of troll comments and fake comments, statistic and diagrams of definition.
Combining Deep Learning and Super-Resolution Algorithms for Deep Fake Detection
2020, Ivanov, N. S., Arzhskov, A. V., Ivanenko, V. G., Иваненко, Виталий Григорьевич
© 2020 IEEE.Deep Fake is a technique for human image synthesis based on artificial intelligence. In this article is explored the problem of Deep Fake Video content and its detection. Has been gathered information about previous attempts, analyzed methods used by different researches and considered their actuality right now. Basing on results of the discovery was designed strategy to expose Deep Fake videos that combines previous detection methods with super-resolution algorithms. Results of the research were compared with expected, so recommendations and possible way of continuing developments were given.