Identifying illicit graphic in the online community using the neural network framework
In this paper two convolutional neural networks are estimated to classify whether an image contains a swastika or not. The images are gathered from the gaming platform Steam and by scraping a web search engine. The architecture of the networks is kept moderate and the difference between the models i...
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| Format: | Others |
| Language: | English |
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Uppsala universitet, Statistiska institutionen
2017
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-325810 |
| Summary: | In this paper two convolutional neural networks are estimated to classify whether an image contains a swastika or not. The images are gathered from the gaming platform Steam and by scraping a web search engine. The architecture of the networks is kept moderate and the difference between the models is the final layer. The first model uses an average type operation while the second uses the conventional fully-connected layer at the end. The results show that the performance of the two models is similar and the test error is in the 6-9 % range. |
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