Detecting Images That Have a Destructive Impact on Users on the Internet

DOI

10.2991/aisr.k.201029.018How to use a DOI?

Keywords

neural network, aggressive content, convolutional neural network, residual neural network, object, destructive impact, image processing, control and security for critical infrastructure systems

Abstract

This article deals with the current problem of automated detection of facts of destructive influence of images on the Internet on the person. It should be noted that detecting aggressive content in images is more difficult than detecting an object and defining its category. The reason for this is that aggressive content has no specific color or object parameters, there are no common features for classification. In this case the use of convolutional neural networks with pre-learning is a successful solution. Due to increase in aggressive content on the Internet, the problem of identifying such objects in order to minimize its impact on users is acute. The paper presents the developed architecture of the neural network for solving the problem of image with aggressive content recognition. Experiments during 180 epochs showed that at the number of epochs ~ 95-100 during training and ~ during testing it is possible to achieve the classification accuracy 91.8% taking into account the top-5 results.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Anastasia Iskhakova
AU  - Roman Meshcherykov
PY  - 2020
DA  - 2020/11/10
TI  - Detecting Images That Have a Destructive Impact on Users on the Internet
BT  - Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020)
PB  - Atlantis Press
SP  - 89
EP  - 93
SN  - 1951-6851
UR  - https://doi.org/10.2991/aisr.k.201029.018
DO  - 10.2991/aisr.k.201029.018
ID  - Iskhakova2020
ER  -