Automatyczne rozpoznawanie treści
nielegalnych filmów typu CSAM
za pomocą klasyfikatora
częściowo splatającego kolejne klatki
materiału wideo
Więcej
Ukryj
1
Zespół Złożonych Systemów, Instytut Automatyki i Informatyki
Stosowanej, Wydział Elektroniki i Technik Informacyjnych, Politechnika Warszawska
Data publikacji: 31-10-2023
Cybersecurity and Law 2023;10(2):195-201
SŁOWA KLUCZOWE
STRESZCZENIE
The paper describes one of the methods of automatic recognition of CSAM materials,
which was tested during the research under the APAKT project. The proposed solution
is based on Temporal Shift Module (TSM), a model of a deep neural network created for
efficient human activities rocognition in video. We applied transfer learning method for
training the model with a relatively small number of training data to succesfully rocognize
films with pornografic and illegal content. We conducted some tests of classification
of films from three categories: neutral films, legal pornography and illegal pornografic
videos (CSAM). In this paper we present problems that are connected with this research
topic that come from the characteristic of the data. We also show that further works are
needed to keep children safe in cyberspace.
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Badźmirowska-Masłowska K., Child protection in cyberspace, „Cybersecurity and Law” 2019, nr 1.
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Koonce B., ResNet 50 [w:]: Convolutional Neural Networks with Swift for Tensorflow, Berkeley, CA 2021.