Персона: Максутов, Артем Артурович
Загружается...
Email Address
Birth Date
Научные группы
Организационные подразделения
Организационная единица
Институт интеллектуальных кибернетических систем
Цель ИИКС и стратегия развития - это подготовка кадров, способных противостоять современным угрозам и вызовам, обладающих знаниями и компетенциями в области кибернетики, информационной и финансовой безопасности для решения задач разработки базового программного обеспечения, повышения защищенности критически важных информационных систем и противодействия отмыванию денег, полученных преступным путем, и финансированию терроризма.
Статус
Фамилия
Максутов
Имя
Артем Артурович
Имя
2 results
Результаты поиска
Теперь показываю 1 - 2 из 2
- ПубликацияТолько метаданныеThe Transformer Neural Network Architecture for Part-of-Speech Tagging(2021) Maksutov, A. A.; Zamyatovskiy, V. I.; Morozov, V. O.; Dmitriev, S. O.; Максутов, Артем Артурович; Дмитриев, Святослав Олегович© 2021 IEEE.Part-of-speech tagging (POS tagging) is one of the most important tasks in natural language processing. This process implies determining part of speech and assigning an appropriate tag for each word in given sentence. The resulting tag sequence can be used as is and as a part of more complicated tasks, such as dependency and constituency parsing. This task belongs to sequence-to-sequence tasks and multilayer bidirectional LSTM networks are commonly used for POS tagging. Such networks are rather slow in terms of training and processing large amounts of information due to sequential computation of each timestamp from the input sequence. This paper is focused on developing an accurate model for POS tagging that uses the original Transformer neural network architecture.
- ПубликацияТолько метаданныеMethods of Deepfake Detection Based on Machine Learning(2020) Maksutov, A. A.; Morozov, V. O.; Lavrenov, A. A.; Smirnov, A. S.; Максутов, Артем Артурович© 2020 IEEE.Nowadays, people faced an emerging problem of AI-synthesized face swapping videos, widely known as the DeepFakes. This kind of videos can be created to cause threats to privacy, fraudulence and so on. Sometimes good quality DeepFake videos recognition could be hard to distinguish with people eyes. That's why researchers need to develop algorithms to detect them. In this work, we present overview of indicators that can tell us about the fact that face swapping algorithms were used on photos. Main purpose of this paper is to find algorithm or technology that can decide whether photo was changed with DeepFake technology or not with good accuracy.