Abstract
Spammers often embed text into images in order to avoid filtering by text-based spam filters, which result in a large number of advertisement spam images. Garbage image recognition has become one of the hotspots in the field of Internet spam filtering research. Its goal is to solve the problem that traditional spam information filtering methods encounter a sharp performance decline or even failure when filtering spam image information. In this article, the adaptive multipoint moment estimation (ADAMM) gradient optimization algorithm is first proposed and then applied to the convolution neural network to form a real-time garbage image recognition model weighted spatial pyramid networks (WSP-nets). Finally, MNIST, ImageNet and ImageSpam are used as the training and test data sets. Compared with other garbage image recognition models, our real-time image spam filtering model reaches a new state of the art.
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Acknowledgements
The authors would like to thank the reviewers for their helpful advices. The National Science and Technology Major Project (Grant No. 2017YFB0803001) and the National Natural Science Foundation of China (Grant No. 61502048) are gratefully acknowledged.
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Meng, Q., Zhu, X., Gu, L. (2019). Image Spam Filtering Using Weighted Spatial Pyramid Networks. In: Patnaik, S., Jain, V. (eds) Recent Developments in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 752. Springer, Singapore. https://doi.org/10.1007/978-981-10-8944-2_6
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DOI: https://doi.org/10.1007/978-981-10-8944-2_6
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