Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Detecting image spam using visual features and near duplicate detection
Proceedings of the 17th international conference on World Wide Web
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A survey and experimental evaluation of image spam filtering techniques
Pattern Recognition Letters
A survey of emerging approaches to spam filtering
ACM Computing Surveys (CSUR)
On online high-dimensional spherical data clustering and feature selection
Engineering Applications of Artificial Intelligence
Hi-index | 0.00 |
A new challenge in the spam email detection is the emergence of image spam, which consists in embedding the advertising messages into attached images to defeat the conventional text-based anti-spam technologies. New techniques are needed to filter these spam messages. In this paper, we proposed a prototype system to automatically classify an image directly as being spam or ham. The proposed method extracts latent topics in image to train a binary classifier for detecting spam images, and achieves more promising detection accuracy than conventional anti-spam approaches. In addition, a detection cascade is proposed to further reduce the computation overhead of the spam filter. Our algorithm is experimentally evaluated under a public spam image dataset, and shown to significantly improve both the detection accuracy and execution efficiency over the baseline approach.