학술논문

Methods of Deepfake Detection Based on Machine Learning
Document Type
Conference
Source
2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), 2020 IEEE Conference of. :408-411 Jan, 2020
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Videos
Face recognition
Information integrity
Faces
Conferences
Gallium nitride
Neural networks
deep learning
DeepFake detection
neural networks
face swapping indicators
Language
ISSN
2376-6565
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.