Method for Constructing Neural Network Means for Recognizing Scenes of Political Extremism in Graphic Materials of Online Social Networks

Q1 Mathematics
I. Tereikovskyi, Rabah AlShboul, Shynar Mussiraliyeva, L. Tereikovska, Kalamkas Bagitova, O. Tereikovskyi, Zhengbing Hu
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引用次数: 0

Abstract

Countering the spread of calls for political extremism through graphic content on online social networks is becoming an increasingly pressing problem that requires the development of new technological solutions, since traditional approaches to countering are based on the results of recognizing destructive content only in text messages. Since in modern conditions neural network tools for analyzing graphic information are considered the most effective, it is assumed that it is advisable to use such tools for analyzing images and video materials in online social networks, taking into account the need to adapt them to the expected conditions of use, which are determined by the wide variability in the size of graphic content, the presence of typical interference, limited computing resources of recognition tools. Using this thesis, a method has been proposed that makes it possible to implement the construction of neural network recognition tools adapted to the specified conditions. For recognition, the author's neural network model was used, which, due to the reasonable determination of the architectural parameters of the low-resource convolutional neural network of the MobileNetV2 type and the recurrent neural network of the LSTM type, which makes up its structure, ensures high accuracy of recognition of scenes of political extremism both in static images and in video materials under limited computing conditions resources. A mechanism was used to adapt the input field of the neural network model to the variability of the size of graphic resources, which provides for scaling within acceptable limits of the input graphic resource and, if necessary, filling the input field with zeros. Levelling out typical noise is ensured by using advanced solutions in the method for correcting brightness, contrast and eliminating blur of local areas in images of online social networks. Neural network tools developed on the basis of the proposed method for recognizing scenes of political extremism in graphic materials of online social networks demonstrate recognition accuracy at the level of the most well-known neural network models, while ensuring a reduction in resource intensity by more than 10 times. This allows the use of less powerful equipment, increases the speed of content analysis, and also opens up prospects for the development of easily scalable recognition tools, which ultimately ensures an increase in security and a reduction in the spread of extremist content on online social networks. It is advisable to correlate the paths for further research with the introduction of the Attention mechanism into the neural network model used in the method, which will make it possible to increase the efficiency of neural network analysis of video materials.
构建识别在线社交网络图文资料中政治极端主义场景的神经网络手段的方法
打击通过在线社交网络上的图片内容散布政治极端主义呼声正成为一个日益紧迫的问题,需要开发新的技术解决方案,因为传统的打击方法仅基于识别文本信息中破坏性内容的结果。在现代条件下,用于分析图形信息的神经网络工具被认为是最有效的,因此我们认为最好使用这种工具来分析在线社交网络中的图像和视频资料,同时考虑到需要使它们适应预期的使用条件,这是由图形内容的大小变化很大、存在典型干扰、识别工具的计算资源有限等因素决定的。通过这篇论文,我们提出了一种方法,可以根据特定条件构建神经网络识别工具。由于合理确定了 MobileNetV2 型低资源卷积神经网络和构成其结构的 LSTM 型递归神经网络的结构参数,该模型确保了在有限计算资源条件下对静态图像和视频材料中的政治极端主义场景进行高精度识别。使用了一种机制来调整神经网络模型的输入域,使其适应图形资源大小的变化,从而在可接受的范围内缩放输入的图形资源,并在必要时用零填充输入域。通过使用先进的解决方案来校正在线社交网络图像的亮度、对比度和消除局部区域的模糊,确保消除典型的噪音。根据所提出的识别在线社交网络图片资料中政治极端主义场景的方法开发的神经网络工具,其识别准确率达到了最著名的神经网络模型的水平,同时确保将资源强度降低 10 倍以上。这样就可以使用功能较弱的设备,提高内容分析速度,还为开发易于扩展的识别工具开辟了前景,最终确保提高安全性,减少极端主义内容在在线社交网络上的传播。建议将进一步研究的路径与将注意机制引入该方法所使用的神经网络模型相关联,这将有可能提高对视频资料进行神经网络分析的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.10
自引率
0.00%
发文量
33
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