Quarterly journal published in SPbPU
and edited by prof. Dmitry Zegzhda
Peter the Great St. Petersburg Polytechnic University
Institute of computer sciences and technologies
information security of computer systems
Information Security Problems. Computer Systems
Published since 1999.
ISSN 2071-8217
V. D. Danilov, N. A. Gribkov, D. V. Ivanov Peter the Great St. Petersburg Polytechnic University
Annotation: The paper presents an analysis of existing methods for detecting artificially synthesized content and proposes a proprietary architecture for DeepFake's hybrid detection system based on searching original content. The study tests and compares the effectiveness of detection methods in two different cases. In the first case, records for training and testing samples are used from the same dataset; in the second case, testing is performed using a black-box method using records from different datasets. As a result, it is concluded that there are shortcomings in the existing methods and a hybrid DeepFake detection system architecture is proposed.
Keywords: DeepFake detection, generative adversarial networks, artificially synthesized content, deep learning.
Pages 34-44