An Efficient Method for Filtering Image-Based Spam

Spam e-mail with advertisement text embedded in images presents a great challenge to anti-spam filters. In this paper, we describe a fast method to detect image-based spam e-mail. Using simple edge-based features, the method computes a vector of similarity scores between an image and a set of templates. This similarity vector is then used with support vector machines to separate spam images from other common categories of images. Our method does not require computationally expensive OCR or even text extraction from images. Empirical results show that the method is fast and has good classification accuracy.