| Title | Methods of Deepfake Detection Based on Machine Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Maksutov, A. A., Morozov, V. O., Lavrenov, A. A., Smirnov, A. S. |
| Conference Name | 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) |
| Keywords | Conferences, data privacy, Deep Learning, DeepFake, deepfake detection, DeepFake video recognition, face recognition, face swapping algorithms, face swapping indicators, faces, feature extraction, fraudulence, Gallium nitride, Human Behavior, human factors, Information integrity, learning (artificial intelligence), machine learning, Metrics, Neural networks, privacy threats, pubcrawl, resilience, Resiliency, Scalability, video signal processing, Videos |
| Abstract | 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. |
| DOI | 10.1109/EIConRus49466.2020.9039057 |
| Citation Key | maksutov_methods_2020 |
Methods of Deepfake Detection Based on Machine Learning