Image Spam Filtering Using Weighted Spatial Pyramid Networks

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.

[1]  E. Rosenblueth.  Point estimates for probability moments. , 1975, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Ning Qian,et al.  On the momentum term in gradient descent learning algorithms , 1999, Neural Networks.

[4]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[5]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[6]  Mark Dredze,et al.  Learning Fast Classifiers for Image Spam , 2007, CEAS.

[7]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[8]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[9]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[10]  Matthew D. Zeiler.  ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[11]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[12]  Rob Fergus,et al.  Stochastic Pooling for Regularization of Deep Convolutional Neural Networks , 2013, ICLR.

[13]  John Salvatier,et al.  Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.

[14]  Jian Sun,et al.  Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Lawrence D. Jackel,et al.  Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.

[16]  Yasuo Kuniyoshi,et al.  Discriminative spatial pyramid , 2011, CVPR 2011.

[17]  Fred Moses,et al.  Reliability of Structural Systems , 1974 .